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Pascual TNB, Paez D, Iagaru A, Gnanasegaran G, Lee ST, Sathekge M, Buatti JM, Giammarile F, Al-Ibraheem A, Pardo MA, Baum RP, De Bari B, Ben-Haim S, Blay JY, Brink A, Estrada-Lobato E, Fanti S, Golubic AT, Hatazawa J, Israel O, Kiess A, Knoll P, Louw L, Mariani G, Mirzaei S, Orellana P, Prior JO, Urbain JL, Vichare S, Vinjamuri S, Virgolini I, Scott AM. Guiding principles on the education and practice of theranostics. Eur J Nucl Med Mol Imaging 2024:10.1007/s00259-024-06657-2. [PMID: 38453729 DOI: 10.1007/s00259-024-06657-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 02/13/2024] [Indexed: 03/09/2024]
Abstract
PURPOSE The recent development and approval of new diagnostic imaging and therapy approaches in the field of theranostics have revolutionised nuclear medicine practice. To ensure the provision of these new imaging and therapy approaches in a safe and high-quality manner, training of nuclear medicine physicians and qualified specialists is paramount. This is required for trainees who are learning theranostics practice, and for ensuring minimum standards for knowledge and competency in existing practising specialists. METHODS To address the need for a training curriculum in theranostics that would be utilised at a global level, a Consultancy Meeting was held at the IAEA in May 2023, with participation by experts in radiopharmaceutical therapy and theranostics including representatives of major international organisations relevant to theranostics practice. RESULTS Through extensive discussions and review of existing curriculum and guidelines, a harmonised training program for theranostics was developed, which aims to ensure safe and high quality theranostics practice in all countries. CONCLUSION The guiding principles for theranostics training outlined in this paper have immediate relevance for the safe and effective practice of theranostics.
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Affiliation(s)
| | - Diana Paez
- Division of Human Health, Department of Nuclear Science and Applications, International Atomic Energy Agency, Vienna, Austria
| | - Andrei Iagaru
- Division of Nuclear Medicine and Molecular Imaging, Stanford University Medical Center, Stanford, CA, USA
| | - Gopi Gnanasegaran
- Department of Nuclear Medicine, Royal Free London NHS Foundation Trust, London, UK
| | - Sze Ting Lee
- Department of Molecular Imaging and Therapy, Austin Health, Melbourne, Australia
- Olivia Newton-John Cancer Research Institute, Melbourne, Australia
- School of Cancer Medicine, La Trobe University, Melbourne, Australia
- School of Health and Biomedicine, Royal Melbourne Institute of Technology (RMIT) University, Melbourne, Australia
- Department of Surgery, University of Melbourne, Melbourne, Australia
| | - Mike Sathekge
- Steve Biko Academic Hospital, Pretoria, South Africa
- University of Pretoria, Pretoria, South Africa
| | - John M Buatti
- Department of Radiation Oncology, Holden Comprehensive Cancer Center, Carver College of Medicine, University of Iowa, Iowa City, IA, USA
| | - Francesco Giammarile
- Division of Human Health, Department of Nuclear Science and Applications, International Atomic Energy Agency, Vienna, Austria
| | - Akram Al-Ibraheem
- Department of Nuclear Medicine, King Hussein Cancer Center (KHCC), Amman, Jordan
- School of Medicine, University of Jordan, Amman, Jordan
| | - Manuela Arevalo Pardo
- Division of Human Health, Department of Nuclear Science and Applications, International Atomic Energy Agency, Vienna, Austria
| | - Richard P Baum
- Center for Advanced Radiomolecular Precision Oncology, Curanosticum Wiesbaden, FrankfurtWiesbaden, Germany
| | - Berardino De Bari
- Radiation Oncology Department, Réseau Hospitalier Neuchâtelois, La Chaux-de-Fonds, Switzerland
| | - Simona Ben-Haim
- Department of Biophysics and Nuclear Medicine, Hadassah University Hospital, Jerusalem, Israel
- Faculty of Medicine, Hebrew University, Jerusalem, Israel
- University College London, London, UK
| | - Jean-Yves Blay
- Department of Medicine, Centre Leon Berard, Lyon, France
- University Claude Bernard Lyon, Lyon, France
| | - Anita Brink
- Division of Human Health, Department of Nuclear Science and Applications, International Atomic Energy Agency, Vienna, Austria
| | - Enrique Estrada-Lobato
- Division of Human Health, Department of Nuclear Science and Applications, International Atomic Energy Agency, Vienna, Austria
| | - Stefano Fanti
- Nuclear Medicine Division, IRCCS Azienda Ospedaliero-Universitaria Di Bologna, Policlinico S.Orsola, Bologna, Italy
| | - Anja Tea Golubic
- Department of Nuclear Medicine and Radiation Protection, University Hospital Centre Zagreb, Kispaticeva 12, 10000, Zagreb, Croatia
| | - Jun Hatazawa
- Department of Nuclear Medicine and Tracer Kinetics, Osaka University Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Ora Israel
- B. Rappaport School of Medicine, Israel Institute of Technology-Technion, Haifa, Israel
| | - Ana Kiess
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Peter Knoll
- Division of Human Health, Department of Nuclear Science and Applications, International Atomic Energy Agency, Vienna, Austria
| | - Lizette Louw
- Center of Molecular Imaging and Theranostics, Johannesburg, South Africa
- University of the Witwatersrand, Johannesburg, South Africa
| | - Giuliano Mariani
- Regional Center of Nuclear Medicine, Department of Translational Research and Advanced Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Siroos Mirzaei
- Department of Nuclear Medicine With PET-Centre, Clinic Ottakring, Vienna, Austria
| | | | - John O Prior
- Department of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital, Lausanne, Switzerland
- Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | | | - Shrikant Vichare
- Division of Human Health, Department of Nuclear Science and Applications, International Atomic Energy Agency, Vienna, Austria
| | - Sobhan Vinjamuri
- Nuclear Medicine Department, Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK
| | - Irene Virgolini
- Department of Nuclear Medicine, Medical University of Innsbruck, Innsbruck, Austria
| | - Andrew M Scott
- Department of Molecular Imaging and Therapy, Austin Health, Melbourne, Australia.
- Olivia Newton-John Cancer Research Institute, Melbourne, Australia.
- School of Cancer Medicine, La Trobe University, Melbourne, Australia.
- Faculty of Medicine, University of Melbourne, Melbourne, Australia.
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Tamplin MR, Wang JK, Binkley EM, Garvin MK, Hyer DE, Buatti JM, Boldt HC, Grumbach IM, Kardon RH. Radiation effects on retinal layers revealed by OCT, OCT-A, and perimetry as a function of dose and time from treatment. Sci Rep 2024; 14:3380. [PMID: 38336828 PMCID: PMC10858219 DOI: 10.1038/s41598-024-53830-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 02/05/2024] [Indexed: 02/12/2024] Open
Abstract
Optical coherence tomography (OCT) has become a key method for diagnosing and staging radiation retinopathy, based mainly on the presence of fluid in the central macula. A robust retinal layer segmentation method is required for identification of the specific layers involved in radiation-induced pathology in individual eyes over time, in order to determine damage driven by radiation injury to the microvessels and to the inner retinal neurons. Here, we utilized OCT, OCT-angiography, visual field testing, and patient-specific dosimetry models to analyze abnormal retinal layer thickening and thinning relative to microvessel density, visual function, radiation dose, and time from radiotherapy in a cross-sectional cohort of uveal melanoma patients treated with 125I-plaque brachytherapy. Within the first 24 months of radiotherapy, we show differential thickening and thinning of the two inner retinal layers, suggestive of microvessel leakage and neurodegeneration, mostly favoring thickening. Four out of 13 eyes showed decreased inner retinal capillary density associated with a corresponding normal inner retinal thickness, indicating early microvascular pathology. Two eyes showed the opposite: significant inner retinal layer thinning and normal capillary density, indicating early neuronal damage preceding a decrease in capillary density. At later time points, inner retinal thinning becomes the dominant pathology and correlates significantly with decreased vascularity, vision loss, and dose to the optic nerve. Stable multiple retinal layer segmentation provided by 3D graph-based methods aids in assessing the microvascular and neuronal response to radiation, information needed to target therapeutics for radiation retinopathy and vision loss.
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Affiliation(s)
- Michelle R Tamplin
- Division of Cardiovascular Medicine, Department of Internal Medicine, University of Iowa, Iowa City, IA, USA
- Iowa City VA Center for the Prevention and Treatment of Visual Loss, Iowa City, IA, USA
| | - Jui-Kai Wang
- Iowa City VA Center for the Prevention and Treatment of Visual Loss, Iowa City, IA, USA
- Division of Neuro-Ophthalmology, Department of Ophthalmology and Visual Sciences, University of Iowa, Iowa City, IA, 52242, USA
- Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA, USA
| | - Elaine M Binkley
- Division of Neuro-Ophthalmology, Department of Ophthalmology and Visual Sciences, University of Iowa, Iowa City, IA, 52242, USA
| | - Mona K Garvin
- Iowa City VA Center for the Prevention and Treatment of Visual Loss, Iowa City, IA, USA
- Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA, USA
| | - Daniel E Hyer
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - John M Buatti
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - H Culver Boldt
- Division of Neuro-Ophthalmology, Department of Ophthalmology and Visual Sciences, University of Iowa, Iowa City, IA, 52242, USA
| | - Isabella M Grumbach
- Division of Cardiovascular Medicine, Department of Internal Medicine, University of Iowa, Iowa City, IA, USA
- Iowa City VA Center for the Prevention and Treatment of Visual Loss, Iowa City, IA, USA
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - Randy H Kardon
- Iowa City VA Center for the Prevention and Treatment of Visual Loss, Iowa City, IA, USA.
- Division of Neuro-Ophthalmology, Department of Ophthalmology and Visual Sciences, University of Iowa, Iowa City, IA, 52242, USA.
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Kritikos M, Vivanco-Suarez J, Teferi N, Lee S, Kato K, Eschbacher KL, Bathla G, Buatti JM, Hitchon PW. Survival and neurological outcomes following management of intramedullary spinal metastasis patients: a case series with comprehensive review of the literature. Neurosurg Rev 2024; 47:75. [PMID: 38319484 DOI: 10.1007/s10143-024-02308-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Revised: 01/12/2024] [Accepted: 01/20/2024] [Indexed: 02/07/2024]
Abstract
Intramedullary spinal cord metastasis (ISCM), though rare, represents a potentially debilitating manifestation of systemic cancer. With emerging advances in cancer care, ISCMs are increasingly being encountered in clinical practice. Herein, we describe one of the larger retrospective single institutional case series on ISCMs, analyze survival and treatment outcomes, and review the literature. All surgically evaluated ISCMs at our institution between 2005 and 2023 were retrospectively reviewed. Demographics, tumor features, treatment, and clinical outcome characteristics were collected. Neurological function was quantified via the Frankel grade and the McCormick score (MCS). The pre- and post-operative Karnofsky performance scores (KPS) were used to assess functional status. Descriptive statistics, univariate analysis, log-rank test, and the Kaplan-Meier survival analysis were performed. A total of 9 patients were included (median age 67 years (range, 26-71); 6 were male). Thoracic and cervical spinal segments were most affected (4 patients each). Six patients (75%) underwent surgical management (1 biopsy and 5 resections), and 3 cases underwent chemoradiation only. Post-operatively, 2 patients had an improvement in their neurological exam with one patient becoming ambulatory after surgery; three patients maintained their neurological exam, and 1 had a decline. There was no statistically significant difference in the pre- and post-operative MCS and median KPS scores in surgically treated patients. Median OS after ISCM diagnosis was 7 months. Absence of brain metastasis, tumor histology (renal and melanoma), cervical/thoracic location, and post-op KPS ≥ 70 showed a trend toward improved overall survival. The incidence of ISCM is increasing, and earlier diagnosis and treatment are considered key for the preservation of neurological function. When patient characteristics are favorable, surgical resection of ISCM can be considered in patients with rapidly progressive neurological deficits. Surgical treatment was not associated with an improvement in overall survival in patients with ISCMs.
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Affiliation(s)
- Michael Kritikos
- Department of Neurosurgery, University of Iowa Hospitals and Clinics, Iowa City, IA, USA
| | - Juan Vivanco-Suarez
- Department of Neurosurgery, University of Iowa Hospitals and Clinics, Iowa City, IA, USA
| | - Nahom Teferi
- Department of Neurosurgery, University of Iowa Hospitals and Clinics, Iowa City, IA, USA
| | - Sarah Lee
- Department of Neurosurgery, University of Iowa Hospitals and Clinics, Iowa City, IA, USA
| | - Kyle Kato
- College of Medicine, University of Iowa, Iowa City, IA, USA
| | - Kathryn L Eschbacher
- Department of Pathology, University of Iowa Hospitals and Clinics, Iowa City, IA, USA
| | - Girish Bathla
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - John M Buatti
- Department of Radiation Oncology, College of Medicine, University of Iowa Hospitals and Clinics, Iowa City, IA, USA
| | - Patrick W Hitchon
- Department of Neurosurgery, University of Iowa Hospitals and Clinics, Iowa City, IA, USA.
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Petronek MS, Bodeker KL, Lee CY, Teferi N, Eschbacher KL, Jones KA, Loeffler BT, Smith BJ, Buatti JM, Magnotta VA, Allen BG. Iron-based biomarkers for personalizing pharmacological ascorbate therapy in glioblastoma: insights from a phase 2 clinical trial. J Neurooncol 2024; 166:493-501. [PMID: 38285244 DOI: 10.1007/s11060-024-04571-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 01/11/2024] [Indexed: 01/30/2024]
Abstract
BACKGROUND Pharmacological ascorbate (intravenous delivery reaching plasma concentrations ≈ 20 mM; P-AscH-) has emerged as a promising therapeutic strategy for glioblastoma. Recently, a single-arm phase 2 clinical trial demonstrated a significant increase in overall survival when P-AscH- was combined with temozolomide and radiotherapy. As P-AscH- relies on iron-dependent mechanisms, this study aimed to assess the predictive potential of both molecular and imaging-based iron-related markers to enhance the personalization of P-AscH- therapy in glioblastoma participants. METHODS Participants (n = 55) with newly diagnosed glioblastoma were enrolled in a phase 2 clinical trial conducted at the University of Iowa (NCT02344355). Tumor samples obtained during surgical resection were processed and stained for transferrin receptor and ferritin heavy chain expression. A blinded pathologist performed pathological assessment. Quantitative susceptibility mapping (QSM) measures were obtained from pre-radiotherapy MRI scans following maximal safe surgical resection. Circulating blood iron panels were evaluated prior to therapy through the University of Iowa Diagnostic Laboratory. RESULTS Through univariate analysis, a significant inverse association was observed between tumor transferrin receptor expression and overall and progression-free survival. QSM measures exhibited a significant, positive association with progression-free survival. Subjects were actively followed until disease progression and then were followed through chart review or clinical visits for overall survival. CONCLUSIONS This study analyzes iron-related biomarkers in the context of P-AscH- therapy for glioblastoma. Integrating molecular, systemic, and imaging-based markers offers a multifaceted approach to tailoring treatment strategies, thereby contributing to improved patient outcomes and advancing the field of glioblastoma therapy.
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Affiliation(s)
- M S Petronek
- Department of Radiation Oncology, Division of Free Radical and Radiation Biology, University of Iowa, Iowa City, IA, USA.
| | - K L Bodeker
- Department of Radiation Oncology, Division of Free Radical and Radiation Biology, University of Iowa, Iowa City, IA, USA
| | - C Y Lee
- Department of Radiology, University of Iowa, Iowa City, IA, USA
| | - N Teferi
- Department of Radiation Oncology, Division of Free Radical and Radiation Biology, University of Iowa, Iowa City, IA, USA
| | - K L Eschbacher
- Department of Pathology, University of Iowa, Iowa City, IA, USA
| | - K A Jones
- Department of Pathology, Division of Neuropathology, Duke University, Durham, NC, USA
| | - B T Loeffler
- Holden Comprehensive Cancer Center, University of Iowa, Iowa City, IA, USA
| | - B J Smith
- Department of Biostatistics, University of Iowa, Iowa City, IA, USA
| | - J M Buatti
- Department of Radiation Oncology, Division of Free Radical and Radiation Biology, University of Iowa, Iowa City, IA, USA
| | - V A Magnotta
- Department of Radiology, University of Iowa, Iowa City, IA, USA
| | - B G Allen
- Department of Radiation Oncology, Division of Free Radical and Radiation Biology, University of Iowa, Iowa City, IA, USA
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5
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Piscopo AJ, Chowdhury AJ, Teferi N, Lee S, Challa M, Petronek M, Eschbacher K, Bathla G, Buatti JM, Hitchon P. Surgical Management of Craniospinal Axis Solitary Fibrous Tumors: A Single-Institution Case Series and Comprehensive Review of the Literature. Neurosurgery 2024; 94:358-368. [PMID: 37747216 DOI: 10.1227/neu.0000000000002692] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Accepted: 08/16/2023] [Indexed: 09/26/2023] Open
Abstract
BACKGROUND AND OBJECTIVES Meningeal solitary fibrous tumors (SFTs) comprise 0.4% of primary central nervous system neoplasms and carry metastatic potential. Disease course and optimal management are largely unknown, and there is currently no literature rigorously describing neurological outcomes in surgically managed SFTs. We present one of the largest craniospinal SFT series, analyze patient outcomes, and extensively review the associated literature. METHODS All surgically managed SFTs at our institution between January 2005 and March 2023 were retrospectively reviewed. Patient demographics, tumor and radiographic features, treatment, and clinical outcomes were collected. Neurological function was quantified using Frankel grade and Neurologic Assessment in Neuro-Oncology scores. Descriptive statistics, multivariate analysis, log-rank test, and Kaplan-Meier survival analysis were performed. RESULTS Twenty-one patients satisfied inclusion criteria. Tumor locations included 15 supratentorial, three infratentorial, and three spinal. All patients underwent surgical resection, and 16 (76.2%) underwent radiation. Six (28.6%) patients had tumor recurrence, and three (14.3%) developed metastasis. Younger age and higher postoperative Frankel grade were significantly associated with increased overall survival (OS) ( P = .011, P = .002, respectively). All patients symptomatically improved or stabilized after surgery, and Neurologic Assessment in Neuro-Oncology score ( P = .001) and functional status significantly improved postoperatively (Karnofsky Performance Status: 65.2 ± 25.2 vs 91.4 ± 13.5, P = .001). Sex, adjuvant radiation, and extent of resection were not significantly associated with OS. CONCLUSION SFT of the central nervous system is a rare entity with a variable clinical course. Surgical resection was associated with improved postoperative functional and neurological status. Higher postoperative neurological function was significantly associated with OS. Further studies are warranted to validate a standardized treatment algorithm and investigate the efficacy of adjuvant radiation in SFT.
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Affiliation(s)
- Anthony J Piscopo
- Department of Neurosurgery, University of Iowa Hospital and Clinics, Iowa City , Iowa , USA
| | - A J Chowdhury
- Department of Neurosurgery, University of Iowa Hospital and Clinics, Iowa City , Iowa , USA
| | - Nahom Teferi
- Department of Neurosurgery, University of Iowa Hospital and Clinics, Iowa City , Iowa , USA
| | - Sarah Lee
- Department of Neurosurgery, University of Iowa Hospital and Clinics, Iowa City , Iowa , USA
| | - Meron Challa
- University of Iowa, Carver College of Medicine, Iowa City , Iowa , USA
| | - Michael Petronek
- Department of Radiation Oncology, University of Iowa Hospital and Clinics, Iowa City , Iowa , USA
| | - Kathryn Eschbacher
- Department of Pathology, University of Iowa Hospital and Clinics, Iowa City , Iowa , USA
| | - Girish Bathla
- Department of Radiology, Mayo Clinic, Rochester , Minnesota , USA
| | - John M Buatti
- Department of Radiation Oncology, University of Iowa Hospital and Clinics, Iowa City , Iowa , USA
| | - Patrick Hitchon
- Department of Neurosurgery, University of Iowa Hospital and Clinics, Iowa City , Iowa , USA
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Petronek MS, Monga V, Bodeker KL, Kwofie M, Lee CY, Mapuskar KA, Stolwijk JM, Zaher A, Wagner BA, Smith MC, Vollstedt S, Brown H, Chandler ML, Lorack AC, Wulfekuhle JS, Sarkaria JN, Flynn RT, Greenlee JD, Howard MA, Smith BJ, Jones KA, Buettner GR, Cullen JJ, St-Aubin J, Buatti JM, Magnotta VA, Spitz DR, Allen BG. Magnetic Resonance Imaging of Iron Metabolism with T2* Mapping Predicts an Enhanced Clinical Response to Pharmacologic Ascorbate in Patients with GBM. Clin Cancer Res 2024; 30:283-293. [PMID: 37773633 PMCID: PMC10841843 DOI: 10.1158/1078-0432.ccr-22-3952] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Revised: 05/22/2023] [Accepted: 09/27/2023] [Indexed: 10/01/2023]
Abstract
PURPOSE Pharmacologic ascorbate (P-AscH-) is hypothesized to be an iron (Fe)-dependent tumor-specific adjuvant to chemoradiation in treating glioblastoma (GBM). This study determined the efficacy of combining P-AscH- with radiation and temozolomide in a phase II clinical trial while simultaneously investigating a mechanism-based, noninvasive biomarker in T2* mapping to predict GBM response to P-AscH- in humans. PATIENTS AND METHODS The single-arm phase II clinical trial (NCT02344355) enrolled 55 subjects, with analysis performed 12 months following the completion of treatment. Overall survival (OS) and progression-free survival (PFS) were estimated with the Kaplan-Meier method and compared across patient subgroups with log-rank tests. Forty-nine of 55 subjects were evaluated using T2*-based MRI to assess its utility as an Fe-dependent biomarker. RESULTS Median OS was estimated to be 19.6 months [90% confidence interval (CI), 15.7-26.5 months], a statistically significant increase compared with historic control patients (14.6 months). Subjects with initial T2* relaxation < 50 ms were associated with a significant increase in PFS compared with T2*-high subjects (11.2 months vs. 5.7 months, P < 0.05) and a trend toward increased OS (26.5 months vs. 17.5 months). These results were validated in preclinical in vitro and in vivo model systems. CONCLUSIONS P-AscH- combined with temozolomide and radiotherapy has the potential to significantly enhance GBM survival. T2*-based MRI assessment of tumor iron content is a prognostic biomarker for GBM clinical outcomes. See related commentary by Nabavizadeh and Bagley, p. 255.
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Affiliation(s)
| | - Varun Monga
- Department of Internal Medicine, Division of Hematology and Oncology, University of Iowa; Iowa City, IA, USA
| | - Kellie L. Bodeker
- Department of Radiation Oncology, University of Iowa; Iowa City, IA, USA
| | - Michael Kwofie
- Department of Radiology, University of Iowa; Iowa City, IA, USA
| | - Chu-Yu Lee
- Department of Radiology, University of Iowa; Iowa City, IA, USA
| | - Kranti A. Mapuskar
- Department of Radiation Oncology, University of Iowa; Iowa City, IA, USA
| | | | - Amira Zaher
- Department of Radiation Oncology, University of Iowa; Iowa City, IA, USA
| | - Brett A. Wagner
- Department of Radiation Oncology, University of Iowa; Iowa City, IA, USA
| | - Mark C. Smith
- Department of Radiation Oncology, University of Iowa; Iowa City, IA, USA
| | - Sandy Vollstedt
- Department of Radiation Oncology, University of Iowa; Iowa City, IA, USA
| | - Heather Brown
- Department of Radiation Oncology, University of Iowa; Iowa City, IA, USA
| | - Meghan L. Chandler
- Department of Radiation Oncology, University of Iowa; Iowa City, IA, USA
| | - Amanda C. Lorack
- Department of Radiation Oncology, University of Iowa; Iowa City, IA, USA
| | | | - Jann N. Sarkaria
- Department of Radiation Oncology, Mayo Clinic; Rochester, MN, USA
| | - Ryan T. Flynn
- Department of Radiation Oncology, University of Iowa; Iowa City, IA, USA
| | | | | | - Brian J. Smith
- Department of Biostatistics, University of Iowa; Iowa City, IA, USA
| | - Karra A. Jones
- Department of Pathology, Division of Neuropathology, Duke University; Durham, NC, USA
| | - Garry R. Buettner
- Department of Radiation Oncology, University of Iowa; Iowa City, IA, USA
| | | | - Joel St-Aubin
- Department of Radiation Oncology, University of Iowa; Iowa City, IA, USA
| | - John M. Buatti
- Department of Radiation Oncology, University of Iowa; Iowa City, IA, USA
| | | | - Douglas R. Spitz
- Department of Radiation Oncology, University of Iowa; Iowa City, IA, USA
| | - Bryan G. Allen
- Department of Radiation Oncology, University of Iowa; Iowa City, IA, USA
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7
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Hyer DE, Caster J, Smith B, St-Aubin J, Snyder J, Shepard A, Zhang H, Mullan S, Geoghegan T, George B, Byrne J, Smith M, Buatti JM, Sonka M. A Technique to Enable Efficient Adaptive Radiation Therapy: Automated Contouring of Prostate and Adjacent Organs. Adv Radiat Oncol 2024; 9:101336. [PMID: 38260219 PMCID: PMC10801646 DOI: 10.1016/j.adro.2023.101336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 07/31/2023] [Indexed: 01/24/2024] Open
Abstract
Purpose The purpose of this work was to investigate the use of a segmentation approach that could potentially improve the speed and reproducibility of contouring during magnetic resonance-guided adaptive radiation therapy. Methods and Materials The segmentation algorithm was based on a hybrid deep neural network and graph optimization approach that also allows rapid user intervention (Deep layered optimal graph image segmentation of multiple objects and surfaces [LOGISMOS] + just enough interaction [JEI]). A total of 115 magnetic resonance-data sets were used for training and quantitative assessment. Expert segmentations were used as the independent standard for the prostate, seminal vesicles, bladder, rectum, and femoral heads for all 115 data sets. In addition, 3 independent radiation oncologists contoured the prostate, seminal vesicles, and rectum for a subset of patients such that the interobserver variability could be quantified. Consensus contours were then generated from these independent contours using a simultaneous truth and performance level estimation approach, and the deviation of Deep LOGISMOS + JEI contours to the consensus contours was evaluated and compared with the interobserver variability. Results The absolute accuracy of Deep LOGISMOS + JEI generated contours was evaluated using median absolute surface-to-surface distance which ranged from a minimum of 0.20 mm for the bladder to a maximum of 0.93 mm for the prostate compared with the independent standard across all data sets. The median relative surface-to-surface distance was less than 0.17 mm for all organs, indicating that the Deep LOGISMOS + JEI algorithm did not exhibit a systematic under- or oversegmentation. Interobserver variability testing yielded a mean absolute surface-to-surface distance of 0.93, 1.04, and 0.81 mm for the prostate, seminal vesicles, and rectum, respectively. In comparison, the deviation of Deep LOGISMOS + JEI from consensus simultaneous truth and performance level estimation contours was 0.57, 0.64, and 0.55 mm for the same organs. On average, the Deep LOGISMOS algorithm took less than 26 seconds for contour segmentation. Conclusions Deep LOGISMOS + JEI segmentation efficiently generated clinically acceptable prostate and normal tissue contours, potentially limiting the need for time intensive manual contouring with each fraction.
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Affiliation(s)
- Daniel E. Hyer
- Department of Radiation Oncology, University of Iowa Hospitals and Clinics, Iowa City, Iowa
| | - Joseph Caster
- Department of Radiation Oncology, University of Iowa Hospitals and Clinics, Iowa City, Iowa
| | - Blake Smith
- Department of Radiation Oncology, University of Iowa Hospitals and Clinics, Iowa City, Iowa
| | - Joel St-Aubin
- Department of Radiation Oncology, University of Iowa Hospitals and Clinics, Iowa City, Iowa
| | - Jeffrey Snyder
- Department of Radiation Oncology, University of Iowa Hospitals and Clinics, Iowa City, Iowa
| | - Andrew Shepard
- Department of Radiation Oncology, University of Iowa Hospitals and Clinics, Iowa City, Iowa
| | - Honghai Zhang
- Iowa Institute for Biomedical Imaging, University of Iowa, Iowa City, Iowa
| | - Sean Mullan
- Iowa Institute for Biomedical Imaging, University of Iowa, Iowa City, Iowa
| | - Theodore Geoghegan
- Department of Radiation Oncology, University of Iowa Hospitals and Clinics, Iowa City, Iowa
| | - Benjamin George
- Department of Radiation Oncology, University of Iowa Hospitals and Clinics, Iowa City, Iowa
| | - James Byrne
- Department of Radiation Oncology, University of Iowa Hospitals and Clinics, Iowa City, Iowa
| | - Mark Smith
- Department of Radiation Oncology, University of Iowa Hospitals and Clinics, Iowa City, Iowa
| | - John M. Buatti
- Department of Radiation Oncology, University of Iowa Hospitals and Clinics, Iowa City, Iowa
| | - Milan Sonka
- Iowa Institute for Biomedical Imaging, University of Iowa, Iowa City, Iowa
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8
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Chowdhury A, Vivanco-Suarez J, Teferi N, Belzer A, Al-Kaylani H, Challa M, Lee S, Buatti JM, Hitchon P. Surgical management of craniospinal axis malignant peripheral nerve sheath tumors: a single-institution experience and literature review. World J Surg Oncol 2023; 21:338. [PMID: 37880773 PMCID: PMC10601280 DOI: 10.1186/s12957-023-03227-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 10/14/2023] [Indexed: 10/27/2023] Open
Abstract
BACKGROUND Malignant peripheral nerve sheath tumor (MPNST) is an exceedingly rare and aggressive tumor, with limited literature on its management. Herein, we present our series of surgically managed craniospinal MPNSTs, analyze their outcomes, and review the literature. METHODS We retrospectively reviewed surgically managed primary craniospinal MPNSTs treated at our institution between January 2005 and May 2023. Patient demographics, tumor features, and treatment outcomes were assessed. Neurological function was quantified using the Frankel grade and Karnofsky performance scores. Descriptive statistics, rank-sum tests, and Kaplan-Meier survival analyses were performed. RESULTS Eight patients satisfied the inclusion criteria (4 male, 4 female). The median age at presentation was 38 years (range 15-67). Most tumors were localized to the spine (75%), and 3 patients had neurofibromatosis type 1. The most common presenting symptoms were paresthesia (50%) and visual changes (13%). The median tumor size was 3 cm, and most tumors were oval-shaped (50%) with well-defined borders (75%). Six tumors were high grade (75%), and gross total resection was achieved in 5 patients, with subtotal resection in the remaining 3 patients. Postoperative radiotherapy and chemotherapy were performed in 6 (75%) and 4 (50%) cases, respectively. Local recurrence occurred in 5 (63%) cases, and distant metastases occurred in 2 (25%). The median overall survival was 26.7 months. Five (63%) patients died due to recurrence. CONCLUSIONS Primary craniospinal MPNSTs are rare and have an aggressive clinical course. Early diagnosis and treatment are essential for managing these tumors. In this single-center study with a small cohort, maximal resection, low-grade pathology, young age (< 30), and adjuvant radiotherapy were associated with improved survival.
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Affiliation(s)
- Ajmain Chowdhury
- Carver College of Medicine, University of Iowa, Iowa City, IA, USA
| | | | - Nahom Teferi
- Neurosurgery and Biomedical Engineering, Department of Neurosurgery, University of Iowa Hospitals and Clinics, 200 Hawkins Dr., Iowa City, IA, 52242, USA
| | - Alex Belzer
- Carver College of Medicine, University of Iowa, Iowa City, IA, USA
| | - Hend Al-Kaylani
- Carver College of Medicine, University of Iowa, Iowa City, IA, USA
| | - Meron Challa
- Carver College of Medicine, University of Iowa, Iowa City, IA, USA
| | - Sarah Lee
- Neurosurgery and Biomedical Engineering, Department of Neurosurgery, University of Iowa Hospitals and Clinics, 200 Hawkins Dr., Iowa City, IA, 52242, USA
| | - John M Buatti
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - Patrick Hitchon
- Neurosurgery and Biomedical Engineering, Department of Neurosurgery, University of Iowa Hospitals and Clinics, 200 Hawkins Dr., Iowa City, IA, 52242, USA.
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9
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Xiong X, Smith BJ, Graves SA, Graham MM, Buatti JM, Beichel RR. Head and Neck Cancer Segmentation in FDG PET Images: Performance Comparison of Convolutional Neural Networks and Vision Transformers. Tomography 2023; 9:1933-1948. [PMID: 37888743 PMCID: PMC10611182 DOI: 10.3390/tomography9050151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 10/11/2023] [Accepted: 10/13/2023] [Indexed: 10/28/2023] Open
Abstract
Convolutional neural networks (CNNs) have a proven track record in medical image segmentation. Recently, Vision Transformers were introduced and are gaining popularity for many computer vision applications, including object detection, classification, and segmentation. Machine learning algorithms such as CNNs or Transformers are subject to an inductive bias, which can have a significant impact on the performance of machine learning models. This is especially relevant for medical image segmentation applications where limited training data are available, and a model's inductive bias should help it to generalize well. In this work, we quantitatively assess the performance of two CNN-based networks (U-Net and U-Net-CBAM) and three popular Transformer-based segmentation network architectures (UNETR, TransBTS, and VT-UNet) in the context of HNC lesion segmentation in volumetric [F-18] fluorodeoxyglucose (FDG) PET scans. For performance assessment, 272 FDG PET-CT scans of a clinical trial (ACRIN 6685) were utilized, which includes a total of 650 lesions (primary: 272 and secondary: 378). The image data used are highly diverse and representative for clinical use. For performance analysis, several error metrics were utilized. The achieved Dice coefficient ranged from 0.833 to 0.809 with the best performance being achieved by CNN-based approaches. U-Net-CBAM, which utilizes spatial and channel attention, showed several advantages for smaller lesions compared to the standard U-Net. Furthermore, our results provide some insight regarding the image features relevant for this specific segmentation application. In addition, results highlight the need to utilize primary as well as secondary lesions to derive clinically relevant segmentation performance estimates avoiding biases.
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Affiliation(s)
- Xiaofan Xiong
- Department of Biomedical Engineering, The University of Iowa, Iowa City, IA 52242, USA
| | - Brian J. Smith
- Department of Biostatistics, The University of Iowa, Iowa City, IA 52242, USA
| | - Stephen A. Graves
- Department of Radiology, The University of Iowa, Iowa City, IA 52242, USA; (S.A.G.)
| | - Michael M. Graham
- Department of Radiology, The University of Iowa, Iowa City, IA 52242, USA; (S.A.G.)
| | - John M. Buatti
- Department of Radiation Oncology, University of Iowa Hospitals and Clinics, Iowa City, IA 52242, USA
| | - Reinhard R. Beichel
- Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA 52242, USA
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10
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Bitterman DS, Gensheimer MF, Jaffray D, Pryma DA, Jiang SB, Morin O, Ginart JB, Upadhaya T, Vallis KA, Buatti JM, Deasy J, Hsiao HT, Chung C, Fuller CD, Greenspan E, Cloyd-Warwick K, Courdy S, Mao A, Barnholtz-Sloan J, Topaloglu U, Hands I, Maurer I, Terry M, Curran WJ, Le QT, Nadaf S, Kibbe W. Cancer Informatics for Cancer Centers: Sharing Ideas on How to Build an Artificial Intelligence-Ready Informatics Ecosystem for Radiation Oncology. JCO Clin Cancer Inform 2023; 7:e2300136. [PMID: 38055914 PMCID: PMC10703125 DOI: 10.1200/cci.23.00136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 08/15/2023] [Accepted: 10/16/2023] [Indexed: 12/08/2023] Open
Abstract
In August 2022, the Cancer Informatics for Cancer Centers brought together cancer informatics leaders for its biannual symposium, Precision Medicine Applications in Radiation Oncology, co-chaired by Quynh-Thu Le, MD (Stanford University), and Walter J. Curran, MD (GenesisCare). Over the course of 3 days, presenters discussed a range of topics relevant to radiation oncology and the cancer informatics community more broadly, including biomarker development, decision support algorithms, novel imaging tools, theranostics, and artificial intelligence (AI) for the radiotherapy workflow. Since the symposium, there has been an impressive shift in the promise and potential for integration of AI in clinical care, accelerated in large part by major advances in generative AI. AI is now poised more than ever to revolutionize cancer care. Radiation oncology is a field that uses and generates a large amount of digital data and is therefore likely to be one of the first fields to be transformed by AI. As experts in the collection, management, and analysis of these data, the informatics community will take a leading role in ensuring that radiation oncology is prepared to take full advantage of these technological advances. In this report, we provide highlights from the symposium, which took place in Santa Barbara, California, from August 29 to 31, 2022. We discuss lessons learned from the symposium for data acquisition, management, representation, and sharing, and put these themes into context to prepare radiation oncology for the successful and safe integration of AI and informatics technologies.
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Affiliation(s)
- Danielle S. Bitterman
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA
- Department of Radiation Oncology, Brigham and Women's Hospital/Dana-Farber Cancer Institute, Boston, MA
| | - Michael F. Gensheimer
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA
| | - David Jaffray
- Department of Radiation Physics, M.D. Anderson Cancer Center, Houston, TX
| | - Daniel A. Pryma
- Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Steve B. Jiang
- Medical Artificial Intelligence and Automation Laboratory and Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX
| | - Olivier Morin
- Department of Radiation Oncology, MEDomics Laboratory, University of California San Francisco, San Francisco, CA
| | - Jorge Barrios Ginart
- Department of Radiation Oncology, MEDomics Laboratory, University of California San Francisco, San Francisco, CA
| | - Taman Upadhaya
- Department of Radiation Oncology, MEDomics Laboratory, University of California San Francisco, San Francisco, CA
| | - Katherine A. Vallis
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA
| | - John M. Buatti
- Department of Oncology, University of Oxford, Oxford, United Kingdom
| | - Joseph Deasy
- Department of Radiation Oncology, University of Iowa Carver College of Medicine, Iowa City, IA
| | - H. Timothy Hsiao
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Caroline Chung
- Department of Scientific Affairs, American Society for Radiation Oncology, Arlington, VA
| | - Clifton D. Fuller
- Department of Scientific Affairs, American Society for Radiation Oncology, Arlington, VA
| | - Emily Greenspan
- Department of Radiation Oncology, M.D. Anderson Cancer Center, Houston, TX
| | - Kristy Cloyd-Warwick
- Center for Biomedical Informatics and Information Technology, National Cancer Institute, Rockville, MD
| | | | | | - Jill Barnholtz-Sloan
- Department of Radiation Oncology, M.D. Anderson Cancer Center, Houston, TX
- Center for Informatics, Digital Vertical, City of Hope National Comprehensive Cancer Center, Los Angeles, CA
| | - Umit Topaloglu
- Department of Radiation Oncology, M.D. Anderson Cancer Center, Houston, TX
| | - Isaac Hands
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD
- Cancer Research Informatics Shared Resource Facility, University of Kentucky Markey Cancer Center, Lexington, NY
| | | | | | | | - Quynh-Thu Le
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA
| | - Sorena Nadaf
- Department of Radiation Oncology, Emory University, Atlanta, GA
| | - Warren Kibbe
- Cancer Center Informatics Society, Los Angeles, CA
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11
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Petronek MS, Teferi N, Caster JM, Stolwijk JM, Zaher A, Buatti JM, Hasan D, Wafa EI, Salem AK, Gillan EG, St-Aubin JJ, Buettner GR, Spitz DR, Magnotta VA, Allen BG. Magnetite nanoparticles as a kinetically favorable source of iron to enhance GBM response to chemoradiosensitization with pharmacological ascorbate. Redox Biol 2023; 62:102651. [PMID: 36924683 PMCID: PMC10025281 DOI: 10.1016/j.redox.2023.102651] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 02/27/2023] [Accepted: 02/27/2023] [Indexed: 03/09/2023] Open
Abstract
Ferumoxytol (FMX) is an FDA-approved magnetite (Fe3O4) nanoparticle used to treat iron deficiency anemia that can also be used as an MR imaging agent in patients that can't receive gadolinium. Pharmacological ascorbate (P-AscH-; IV delivery; plasma levels ≈ 20 mM) has shown promise as an adjuvant to standard of care chemo-radiotherapy in glioblastoma (GBM). Since ascorbate toxicity mediated by H2O2 is enhanced by Fe redox cycling, the current study determined if ascorbate catalyzed the release of ferrous iron (Fe2+) from FMX for enhancing GBM responses to chemo-radiotherapy. Ascorbate interacted with Fe3O4 in FMX to produce redox-active Fe2+ while simultaneously generating increased H2O2 fluxes, that selectively enhanced GBM cell killing (relative to normal human astrocytes) as opposed to a more catalytically active Fe complex (EDTA-Fe3+) in an H2O2 - dependent manner. In vivo, FMX was able to improve GBM xenograft tumor control when combined with pharmacological ascorbate and chemoradiation in U251 tumors that were unresponsive to pharmacological ascorbate therapy. These data support the hypothesis that FMX combined with P-AscH- represents a novel combined modality therapeutic approach to enhance cancer cell selective chemoradiosentization in the management of glioblastoma.
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Affiliation(s)
- M S Petronek
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA.
| | - N Teferi
- Department of Neurosurgery, University of Iowa, Iowa City, IA, USA
| | - J M Caster
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - J M Stolwijk
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - A Zaher
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - J M Buatti
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - D Hasan
- Department of Neurosurgery, Duke University, Durham, NC, USA
| | - E I Wafa
- Department of Pharmaceutical Sciences, University of Iowa, Iowa City, IA, USA
| | - A K Salem
- Department of Pharmaceutical Sciences, University of Iowa, Iowa City, IA, USA
| | - E G Gillan
- Department of Chemistry, University of Iowa, Iowa City, IA, USA
| | - J J St-Aubin
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - G R Buettner
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - D R Spitz
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - V A Magnotta
- Department of Radiology, University of Iowa, Iowa City, IA, USA
| | - B G Allen
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA.
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12
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Gainey JC, He Y, Zhu R, Baek SS, Wu X, Buatti JM, Allen BG, Smith BJ, Kim Y. Predictive power of deep-learning segmentation based prognostication model in non-small cell lung cancer. Front Oncol 2023; 13:868471. [PMID: 37081986 PMCID: PMC10110903 DOI: 10.3389/fonc.2023.868471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 03/20/2023] [Indexed: 04/07/2023] Open
Abstract
PurposeThe study aims to create a model to predict survival outcomes for non-small cell lung cancer (NSCLC) after treatment with stereotactic body radiotherapy (SBRT) using deep-learning segmentation based prognostication (DESEP).MethodsThe DESEP model was trained using imaging from 108 patients with NSCLC with various clinical stages and treatment histories. The model generated predictions based on unsupervised features learned by a deep-segmentation network from computed tomography imaging to categorize patients into high and low risk groups for overall survival (DESEP-predicted-OS), disease specific survival (DESEP-predicted-DSS), and local progression free survival (DESEP-predicted-LPFS). Serial assessments were also performed using auto-segmentation based volumetric RECISTv1.1 and computer-based unidimensional RECISTv1.1 patients was performed.ResultsThere was a concordance between the DESEP-predicted-LPFS risk category and manually calculated RECISTv1.1 (φ=0.544, p=0.001). Neither the auto-segmentation based volumetric RECISTv1.1 nor the computer-based unidimensional RECISTv1.1 correlated with manual RECISTv1.1 (p=0.081 and p=0.144, respectively). While manual RECISTv1.1 correlated with LPFS (HR=6.97,3.51-13.85, c=0.70, p<0.001), it could not provide insight regarding DSS (p=0.942) or OS (p=0.662). In contrast, the DESEP-predicted methods were predictive of LPFS (HR=3.58, 1.66-7.18, c=0.60, p<0.001), OS (HR=6.31, 3.65-10.93, c=0.71, p<0.001) and DSS (HR=9.25, 4.50-19.02, c=0.69, p<0.001). The promising results of the DESEP model were reproduced for the independent, external datasets of Stanford University, classifying survival and ‘dead’ group in their Kaplan-Meyer curves (p = 0.019).ConclusionDeep-learning segmentation based prognostication can predict LPFS as well as OS, and DSS after SBRT for NSCLC. It can be used in conjunction with current standard of care, manual RECISTv1.1 to provide additional insights regarding DSS and OS in NSCLC patients receiving SBRT.SummaryWhile current standard of care, manual RECISTv1.1 correlated with local progression free survival (LPFS) (HR=6.97,3.51-13.85, c=0.70, p<0.001), it could not provide insight regarding disease specific survival (DSS) (p=0.942) or overall survival (OS) (p=0.662). In contrast, the deep-learning segmentation based prognostication (DESEP)-predicted methods were predictive of LPFS (HR=3.58, 1.66-7.18, c=0.60, p<0.001), OS (HR=6.31, 3.65-10.93, c=0.71, p<0.001) and DSS (HR=9.25, 4.50-19.02, c=0.69, p<0.001). DESEP can be used in conjunction with current standard of care, manual RECISTv1.1 to provide additional insights regarding DSS and OS in NSCLC patients.
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Affiliation(s)
- Jordan C. Gainey
- Department of Radiation Oncology, The University of Iowa, Iowa City, IA, United States
| | - Yusen He
- Department of Data Science, Grinnell College, Grinnell, IA, United States
| | - Robert Zhu
- Department of Radiation Oncology, The University of Iowa, Iowa City, IA, United States
| | - Stephen S. Baek
- Department of Data Science, University of Virginia, Charlottesville, VA, United States
| | - Xiaodong Wu
- Department of Radiation Oncology, The University of Iowa, Iowa City, IA, United States
| | - John M. Buatti
- Department of Radiation Oncology, The University of Iowa, Iowa City, IA, United States
| | - Bryan G. Allen
- Department of Radiation Oncology, The University of Iowa, Iowa City, IA, United States
| | - Brian J. Smith
- Department of Radiation Oncology, The University of Iowa, Iowa City, IA, United States
| | - Yusung Kim
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, United States
- *Correspondence: Yusung Kim,
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13
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Buatti JM, Ennis RD, Kiess AP, Michalski JM. Radiation Oncology and Radiopharmaceuticals: Making Our Own History While Learning From the Past. Int J Radiat Oncol Biol Phys 2023; 115:1044-1046. [PMID: 36922080 DOI: 10.1016/j.ijrobp.2022.12.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 12/12/2022] [Indexed: 03/14/2023]
Affiliation(s)
- John M Buatti
- Carver College of Medicine, University of Iowa, Iowa City, Iowa.
| | - Ronald D Ennis
- Rutgers Cancer Institute of New Jersey, New Brunswick, New Jersey
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14
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Nör F, Castro JP, Wongpattaraworakul W, Buatti JM, Gordon D, Powers JG, Terry W, Hellstein J, Tanas M, Stone M. Cutaneous Metastasis of Alveolar Rhabdomyosarcoma in a Child. Am J Dermatopathol 2023; 45:e17-e21. [PMID: 36728280 DOI: 10.1097/dad.0000000000002382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 12/04/2022] [Indexed: 02/03/2023]
Abstract
ABSTRACT Rhabdomyosarcoma (RMS) is one of the most common soft tissue sarcomas in children. This lesion is classically included in the generic group of "small round blue cell tumors" along with other entities that share similar microscopic features. Although the head and neck region is a frequent site for primary tumors, cutaneous metastases of RMS involving this anatomical location are rare in the pediatric population. We report a case of a 12-year old girl previously diagnosed with a primary alveolar RMS involving the left maxillary sinus, presenting with a metastatic lesion on the skin of the left temple area. Along with a brief review of the previous case reports on the topic, we highlight the initial immunohistochemistry panel useful for diagnosing this tumor.
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Affiliation(s)
- Felipe Nör
- Department of Periodontics and Oral Medicine, University of Michigan School of Dentistry, Ann Arbor, MI
- Department of Oral Pathology, Radiology and Medicine, University of Iowa College of Dentistry, Iowa City, IA
| | - Juan Pablo Castro
- Department of Oral Pathology, Radiology and Medicine, University of Iowa College of Dentistry, Iowa City, IA
| | - Wattawan Wongpattaraworakul
- Department of Oral Pathology, Radiology and Medicine, University of Iowa College of Dentistry, Iowa City, IA
| | - John M Buatti
- Department of Radiation Oncology, University of Iowa Hospitals and Clinics, Iowa City, IA
| | - David Gordon
- Department of Pediatrics, Division of Pediatric Hematology-Oncology, University of Iowa Hospitals and Clinics, Iowa City, IA
| | - Jennifer G Powers
- Department of Dermatology, University of Iowa Hospitals and Clinics, Iowa City, IA
| | - William Terry
- Department of Pediatrics, Division of Pediatric Hematology-Oncology, University of Iowa Hospitals and Clinics, Iowa City, IA
| | - John Hellstein
- Department of Oral Pathology, Radiology and Medicine, University of Iowa College of Dentistry, Iowa City, IA
| | - Munir Tanas
- Department of Pathology, University of Iowa Hospitals and Clinics, Iowa City, IA; and
| | - Mary Stone
- Department of Dermatopathology, University of Iowa Hospitals and Clinics, Iowa City, IA
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15
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Pati S, Baid U, Edwards B, Sheller M, Wang SH, Reina GA, Foley P, Gruzdev A, Karkada D, Davatzikos C, Sako C, Ghodasara S, Bilello M, Mohan S, Vollmuth P, Brugnara G, Preetha CJ, Sahm F, Maier-Hein K, Zenk M, Bendszus M, Wick W, Calabrese E, Rudie J, Villanueva-Meyer J, Cha S, Ingalhalikar M, Jadhav M, Pandey U, Saini J, Garrett J, Larson M, Jeraj R, Currie S, Frood R, Fatania K, Huang RY, Chang K, Balaña C, Capellades J, Puig J, Trenkler J, Pichler J, Necker G, Haunschmidt A, Meckel S, Shukla G, Liem S, Alexander GS, Lombardo J, Palmer JD, Flanders AE, Dicker AP, Sair HI, Jones CK, Venkataraman A, Jiang M, So TY, Chen C, Heng PA, Dou Q, Kozubek M, Lux F, Michálek J, Matula P, Keřkovský M, Kopřivová T, Dostál M, Vybíhal V, Vogelbaum MA, Mitchell JR, Farinhas J, Maldjian JA, Yogananda CGB, Pinho MC, Reddy D, Holcomb J, Wagner BC, Ellingson BM, Cloughesy TF, Raymond C, Oughourlian T, Hagiwara A, Wang C, To MS, Bhardwaj S, Chong C, Agzarian M, Falcão AX, Martins SB, Teixeira BCA, Sprenger F, Menotti D, Lucio DR, LaMontagne P, Marcus D, Wiestler B, Kofler F, Ezhov I, Metz M, Jain R, Lee M, Lui YW, McKinley R, Slotboom J, Radojewski P, Meier R, Wiest R, Murcia D, Fu E, Haas R, Thompson J, Ormond DR, Badve C, Sloan AE, Vadmal V, Waite K, Colen RR, Pei L, Ak M, Srinivasan A, Bapuraj JR, Rao A, Wang N, Yoshiaki O, Moritani T, Turk S, Lee J, Prabhudesai S, Morón F, Mandel J, Kamnitsas K, Glocker B, Dixon LVM, Williams M, Zampakis P, Panagiotopoulos V, Tsiganos P, Alexiou S, Haliassos I, Zacharaki EI, Moustakas K, Kalogeropoulou C, Kardamakis DM, Choi YS, Lee SK, Chang JH, Ahn SS, Luo B, Poisson L, Wen N, Tiwari P, Verma R, Bareja R, Yadav I, Chen J, Kumar N, Smits M, van der Voort SR, Alafandi A, Incekara F, Wijnenga MMJ, Kapsas G, Gahrmann R, Schouten JW, Dubbink HJ, Vincent AJPE, van den Bent MJ, French PJ, Klein S, Yuan Y, Sharma S, Tseng TC, Adabi S, Niclou SP, Keunen O, Hau AC, Vallières M, Fortin D, Lepage M, Landman B, Ramadass K, Xu K, Chotai S, Chambless LB, Mistry A, Thompson RC, Gusev Y, Bhuvaneshwar K, Sayah A, Bencheqroun C, Belouali A, Madhavan S, Booth TC, Chelliah A, Modat M, Shuaib H, Dragos C, Abayazeed A, Kolodziej K, Hill M, Abbassy A, Gamal S, Mekhaimar M, Qayati M, Reyes M, Park JE, Yun J, Kim HS, Mahajan A, Muzi M, Benson S, Beets-Tan RGH, Teuwen J, Herrera-Trujillo A, Trujillo M, Escobar W, Abello A, Bernal J, Gómez J, Choi J, Baek S, Kim Y, Ismael H, Allen B, Buatti JM, Kotrotsou A, Li H, Weiss T, Weller M, Bink A, Pouymayou B, Shaykh HF, Saltz J, Prasanna P, Shrestha S, Mani KM, Payne D, Kurc T, Pelaez E, Franco-Maldonado H, Loayza F, Quevedo S, Guevara P, Torche E, Mendoza C, Vera F, Ríos E, López E, Velastin SA, Ogbole G, Soneye M, Oyekunle D, Odafe-Oyibotha O, Osobu B, Shu'aibu M, Dorcas A, Dako F, Simpson AL, Hamghalam M, Peoples JJ, Hu R, Tran A, Cutler D, Moraes FY, Boss MA, Gimpel J, Veettil DK, Schmidt K, Bialecki B, Marella S, Price C, Cimino L, Apgar C, Shah P, Menze B, Barnholtz-Sloan JS, Martin J, Bakas S. Author Correction: Federated learning enables big data for rare cancer boundary detection. Nat Commun 2023; 14:436. [PMID: 36702828 PMCID: PMC9879935 DOI: 10.1038/s41467-023-36188-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
Affiliation(s)
- Sarthak Pati
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Informatics, Technical University of Munich, Munich, Bavaria, Germany
| | - Ujjwal Baid
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | | | | | | | | | | | | | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Chiharu Sako
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Satyam Ghodasara
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michel Bilello
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Suyash Mohan
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Philipp Vollmuth
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Gianluca Brugnara
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | | | - Felix Sahm
- Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Neuropathology, Heidelberg University Hospital, Heidelberg, Germany
| | - Klaus Maier-Hein
- Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
- Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Maximilian Zenk
- Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Wolfgang Wick
- Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ), Heidelberg, Germany
- Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany
| | - Evan Calabrese
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Jeffrey Rudie
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Javier Villanueva-Meyer
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Soonmee Cha
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Madhura Ingalhalikar
- Symbiosis Center for Medical Image Analysis, Symbiosis International University, Pune, Maharashtra, India
| | - Manali Jadhav
- Symbiosis Center for Medical Image Analysis, Symbiosis International University, Pune, Maharashtra, India
| | - Umang Pandey
- Symbiosis Center for Medical Image Analysis, Symbiosis International University, Pune, Maharashtra, India
| | - Jitender Saini
- Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Bangalore, Karnataka, India
| | - John Garrett
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Department of Medical Physics, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Matthew Larson
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Robert Jeraj
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Department of Medical Physics, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Stuart Currie
- Leeds Teaching Hospitals Trust, Department of Radiology, Leeds, UK
| | - Russell Frood
- Leeds Teaching Hospitals Trust, Department of Radiology, Leeds, UK
| | - Kavi Fatania
- Leeds Teaching Hospitals Trust, Department of Radiology, Leeds, UK
| | - Raymond Y Huang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Ken Chang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | | | | | - Josep Puig
- Department of Radiology (IDI), Girona Biomedical Research Institute (IdIBGi), Josep Trueta University Hospital, Girona, Spain
| | - Johannes Trenkler
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Josef Pichler
- Department of Neurooncology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Georg Necker
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Andreas Haunschmidt
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Stephan Meckel
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
- Institute of Diagnostic and Interventional Neuroradiology, RKH Klinikum Ludwigsburg, Ludwigsburg, Germany
| | - Gaurav Shukla
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiation Oncology, Christiana Care Health System, Philadelphia, PA, USA
| | - Spencer Liem
- Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA
| | - Gregory S Alexander
- Department of Radiation Oncology, University of Maryland, Baltimore, MD, USA
| | - Joseph Lombardo
- Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA
- Department of Radiation Oncology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Joshua D Palmer
- Department of Radiation Oncology, The James Cancer Hospital and Solove Research Institute, The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA
| | - Adam E Flanders
- Department of Radiology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Adam P Dicker
- Department of Radiation Oncology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Haris I Sair
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- The Malone Center for Engineering in Healthcare, The Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Craig K Jones
- The Malone Center for Engineering in Healthcare, The Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Archana Venkataraman
- Department of Electrical and Computer Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Meirui Jiang
- The Chinese University of Hong Kong, Hong Kong, China
| | - Tiffany Y So
- The Chinese University of Hong Kong, Hong Kong, China
| | - Cheng Chen
- The Chinese University of Hong Kong, Hong Kong, China
| | | | - Qi Dou
- The Chinese University of Hong Kong, Hong Kong, China
| | - Michal Kozubek
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Filip Lux
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Jan Michálek
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Petr Matula
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Miloš Keřkovský
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Masaryk University, Brno and University Hospital Brno, Brno, Czech Republic
| | - Tereza Kopřivová
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Masaryk University, Brno and University Hospital Brno, Brno, Czech Republic
| | - Marek Dostál
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Masaryk University, Brno and University Hospital Brno, Brno, Czech Republic
- Department of Biophysics, Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Václav Vybíhal
- Department of Neurosurgery, Faculty of Medicine, Masaryk University, Brno, and University Hospital and Czech Republic, Brno, Czech Republic
| | - Michael A Vogelbaum
- Department of Neuro Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - J Ross Mitchell
- University of Alberta, Edmonton, AB, Canada
- Alberta Machine Intelligence Institute, Edmonton, AB, Canada
| | - Joaquim Farinhas
- Department of Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | | | | | - Marco C Pinho
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Divya Reddy
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - James Holcomb
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | | | - Benjamin M Ellingson
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- UCLA Neuro-Oncology Program, Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CaA, USA
| | - Timothy F Cloughesy
- UCLA Neuro-Oncology Program, Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CaA, USA
| | - Catalina Raymond
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Talia Oughourlian
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Akifumi Hagiwara
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Chencai Wang
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Minh-Son To
- College of Medicine and Public Health, Flinders University, Bedford Park, SA, Australia
- Division of Surgery and Perioperative Medicine, Flinders Medical Centre, Bedford Park, SA, Australia
| | - Sargam Bhardwaj
- College of Medicine and Public Health, Flinders University, Bedford Park, SA, Australia
| | - Chee Chong
- South Australia Medical Imaging, Flinders Medical Centre, Bedford Park, SA, Australia
| | - Marc Agzarian
- South Australia Medical Imaging, Flinders Medical Centre, Bedford Park, SA, Australia
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA
| | | | | | - Bernardo C A Teixeira
- Instituto de Neurologia de Curitiba, Curitiba, Paraná, Brazil
- Department of Radiology, Hospital de Clínicas da Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - Flávia Sprenger
- Department of Radiology, Hospital de Clínicas da Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - David Menotti
- Department of Informatics, Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - Diego R Lucio
- Department of Informatics, Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - Pamela LaMontagne
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Daniel Marcus
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Benedikt Wiestler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TranslaTUM (Zentralinstitut für translationale Krebsforschung der Technischen Universität München), Klinikum rechts der Isar, Munich, Germany
| | - Florian Kofler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TranslaTUM (Zentralinstitut für translationale Krebsforschung der Technischen Universität München), Klinikum rechts der Isar, Munich, Germany
- Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, Munich, Germany
| | - Ivan Ezhov
- Department of Informatics, Technical University of Munich, Munich, Bavaria, Germany
- TranslaTUM (Zentralinstitut für translationale Krebsforschung der Technischen Universität München), Klinikum rechts der Isar, Munich, Germany
- Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, Munich, Germany
| | - Marie Metz
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Rajan Jain
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
- Department of Neurosurgery, NYU Grossman School of Medicine, New York, NY, USA
| | - Matthew Lee
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
| | - Yvonne W Lui
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
| | - Richard McKinley
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Johannes Slotboom
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Piotr Radojewski
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Raphael Meier
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Roland Wiest
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Derrick Murcia
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - Eric Fu
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - Rourke Haas
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - John Thompson
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - David Ryan Ormond
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - Chaitra Badve
- Department of Radiology, University Hospitals Cleveland, Cleveland, OH, USA
| | - Andrew E Sloan
- Department of Neurological Surgery, University Hospitals-Seidman Cancer Center, Cleveland, OH, USA
- Case Comprehensive Cancer Center, Cleveland, OH, USA
- Department of Neurosurgery, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Vachan Vadmal
- Department of Neurosurgery, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Kristin Waite
- National Cancer Institute, National Institute of Health, Division of Cancer Epidemiology and Genetics, Bethesda, MD, USA
| | - Rivka R Colen
- Department of Radiology, Neuroradiology Division, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Diagnostic Radiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Linmin Pei
- University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Murat Ak
- Department of Radiology, Neuroradiology Division, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ashok Srinivasan
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - J Rajiv Bapuraj
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Arvind Rao
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Nicholas Wang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Ota Yoshiaki
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Toshio Moritani
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Sevcan Turk
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Joonsang Lee
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Snehal Prabhudesai
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Fanny Morón
- Department of Radiology, Baylor College of Medicine, Houston, TX, USA
| | - Jacob Mandel
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA
| | - Konstantinos Kamnitsas
- Department of Computing, Imperial College London, London, UK
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Ben Glocker
- Department of Computing, Imperial College London, London, UK
| | - Luke V M Dixon
- Department of Radiology, Imperial College NHS Healthcare Trust, London, UK
| | - Matthew Williams
- Computational Oncology Group, Institute for Global Health Innovation, Imperial College London, London, UK
| | - Peter Zampakis
- Department of NeuroRadiology, University of Patras, Patras, Greece
| | | | - Panagiotis Tsiganos
- Clinical Radiology Laboratory, Department of Medicine, University of Patras, Patras, Greece
| | - Sotiris Alexiou
- Department of Electrical and Computer Engineering, University of Patras, Patras, Greece
| | - Ilias Haliassos
- Department of Neuro-Oncology, University of Patras, Patras, Greece
| | - Evangelia I Zacharaki
- Department of Electrical and Computer Engineering, University of Patras, Patras, Greece
| | | | | | | | | | | | | | - Sung Soo Ahn
- Yonsei University College of Medicine, Seoul, Korea
| | - Bing Luo
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, USA
| | - Laila Poisson
- Public Health Sciences, Henry Ford Health System, Detroit, MI, USA
| | - Ning Wen
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, USA
- SJTU-Ruijin-UIH Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 200025, Shanghai, China
| | | | - Ruchika Verma
- Alberta Machine Intelligence Institute, Edmonton, AB, Canada
- Case Western Reserve University, Cleveland, OH, USA
| | - Rohan Bareja
- Case Western Reserve University, Cleveland, OH, USA
| | - Ipsa Yadav
- Case Western Reserve University, Cleveland, OH, USA
| | | | - Neeraj Kumar
- University of Alberta, Edmonton, AB, Canada
- Alberta Machine Intelligence Institute, Edmonton, AB, Canada
| | - Marion Smits
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Sebastian R van der Voort
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Ahmed Alafandi
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Fatih Incekara
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
- Department of Neurosurgery, Brain Tumor Center, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Maarten M J Wijnenga
- Department of Neurology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Georgios Kapsas
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Renske Gahrmann
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Joost W Schouten
- Department of Neurosurgery, Brain Tumor Center, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Hendrikus J Dubbink
- Department of Pathology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Arnaud J P E Vincent
- Department of Neurosurgery, Brain Tumor Center, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Martin J van den Bent
- Department of Neurology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Pim J French
- Department of Neurology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Stefan Klein
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Yading Yuan
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sonam Sharma
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Tzu-Chi Tseng
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Saba Adabi
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Simone P Niclou
- NORLUX Neuro-Oncology Laboratory, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Olivier Keunen
- Translation Radiomics, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Ann-Christin Hau
- NORLUX Neuro-Oncology Laboratory, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, Luxembourg
- Luxembourg Center of Neuropathology, Laboratoire National De Santé, Luxembourg, Luxembourg
| | - Martin Vallières
- Department of Computer Science, Université de Sherbrooke, Sherbrooke, QC, Canada
- Centre de Recherche du Centre Hospitalière Universitaire de Sherbrooke, Sherbrooke, QC, Canada
| | - David Fortin
- Centre de Recherche du Centre Hospitalière Universitaire de Sherbrooke, Sherbrooke, QC, Canada
- Division of Neurosurgery and Neuro-Oncology, Faculty of Medicine and Health Science, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Martin Lepage
- Centre de Recherche du Centre Hospitalière Universitaire de Sherbrooke, Sherbrooke, QC, Canada
- Department of Nuclear Medicine and Radiobiology, Sherbrooke Molecular Imaging Centre, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Bennett Landman
- Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Karthik Ramadass
- Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Kaiwen Xu
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Silky Chotai
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lola B Chambless
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Akshitkumar Mistry
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Reid C Thompson
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yuriy Gusev
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Krithika Bhuvaneshwar
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Anousheh Sayah
- Division of Neuroradiology & Neurointerventional Radiology, Department of Radiology, MedStar Georgetown University Hospital, Washington, DC, USA
| | - Camelia Bencheqroun
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Anas Belouali
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Subha Madhavan
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Thomas C Booth
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
- Department of Neuroradiology, Ruskin Wing, King's College Hospital NHS Foundation Trust, London, UK
| | - Alysha Chelliah
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Marc Modat
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Haris Shuaib
- Stoke Mandeville Hospital, Mandeville Road, Aylesbury, UK
- Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada
| | - Carmen Dragos
- Stoke Mandeville Hospital, Mandeville Road, Aylesbury, UK
| | | | | | | | | | - Shady Gamal
- University of Cairo School of Medicine, Giza, Egypt
| | | | | | | | - Ji Eun Park
- Department of Radiology, Asan Medical Center, Seoul, South Korea
| | - Jihye Yun
- Department of Radiology, Asan Medical Center, Seoul, South Korea
| | - Ho Sung Kim
- Department of Radiology, Asan Medical Center, Seoul, South Korea
| | - Abhishek Mahajan
- The Clatterbridge Cancer Centre NHS Foundation Trust Pembroke Place, Liverpool, UK
| | - Mark Muzi
- Department of Radiology, University of Washington, Seattle, WA, USA
| | - Sean Benson
- Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Regina G H Beets-Tan
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, Netherlands
- GROW School of Oncology and Developmental Biology, Maastricht, Netherlands
| | - Jonas Teuwen
- Netherlands Cancer Institute, Amsterdam, Netherlands
| | | | | | - William Escobar
- Clínica Imbanaco Grupo Quirón Salud, Cali, Colombia
- Universidad del Valle, Cali, Colombia
| | | | - Jose Bernal
- Universidad del Valle, Cali, Colombia
- The University of Edinburgh, Edinburgh, UK
| | | | - Joseph Choi
- Department of Industrial and Systems Engineering, University of Iowa, Iowa, USA
| | - Stephen Baek
- Department of Industrial and Systems Engineering, Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - Yusung Kim
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - Heba Ismael
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - Bryan Allen
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - John M Buatti
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | | | - Hongwei Li
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Tobias Weiss
- Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Michael Weller
- Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Andrea Bink
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Bertrand Pouymayou
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | | | - Joel Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Prateek Prasanna
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Sampurna Shrestha
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Kartik M Mani
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
- Department of Radiation Oncology, Stony Brook University, Stony Brook, NY, USA
| | - David Payne
- Department of Radiology, Stony Brook University, Stony Brook, NY, USA
| | - Tahsin Kurc
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
- Scientific Data Group, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Enrique Pelaez
- Escuela Superior Politecnica del Litoral, Guayaquil, Guayas, Ecuador
| | | | - Francis Loayza
- Escuela Superior Politecnica del Litoral, Guayaquil, Guayas, Ecuador
| | | | | | | | | | - Franco Vera
- Universidad de Concepción, Concepción, Biobío, Chile
| | - Elvis Ríos
- Universidad de Concepción, Concepción, Biobío, Chile
| | - Eduardo López
- Universidad de Concepción, Concepción, Biobío, Chile
| | - Sergio A Velastin
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK
| | - Godwin Ogbole
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | - Mayowa Soneye
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | - Dotun Oyekunle
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | | | - Babatunde Osobu
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | - Mustapha Shu'aibu
- Department of Radiology, Muhammad Abdullahi Wase Teaching Hospital, Kano, Nigeria
| | - Adeleye Dorcas
- Department of Radiology, Obafemi Awolowo University Ile-Ife, Ile-Ife, Osun, Nigeria
| | - Farouk Dako
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Global Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Amber L Simpson
- Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Mohammad Hamghalam
- School of Computing, Queen's University, Kingston, ON, Canada
- Department of Electrical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
| | - Jacob J Peoples
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Ricky Hu
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Anh Tran
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Danielle Cutler
- The Faculty of Arts & Sciences, Queen's University, Kingston, ON, Canada
| | - Fabio Y Moraes
- Department of Oncology, Queen's University, Kingston, ON, Canada
| | - Michael A Boss
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - James Gimpel
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Deepak Kattil Veettil
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Kendall Schmidt
- Data Science Institute, American College of Radiology, Reston, VA, USA
| | - Brian Bialecki
- Data Science Institute, American College of Radiology, Reston, VA, USA
| | - Sailaja Marella
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Cynthia Price
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Lisa Cimino
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Charles Apgar
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | | | - Bjoern Menze
- Department of Informatics, Technical University of Munich, Munich, Bavaria, Germany
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Jill S Barnholtz-Sloan
- National Cancer Institute, National Institute of Health, Division of Cancer Epidemiology and Genetics, Bethesda, MD, USA
- Center for Biomedical Informatics and Information Technology, National Cancer Institute (NCI), National Institute of Health, Bethesda, MD, USA
| | | | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA.
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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16
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Pati S, Baid U, Edwards B, Sheller M, Wang SH, Reina GA, Foley P, Gruzdev A, Karkada D, Davatzikos C, Sako C, Ghodasara S, Bilello M, Mohan S, Vollmuth P, Brugnara G, Preetha CJ, Sahm F, Maier-Hein K, Zenk M, Bendszus M, Wick W, Calabrese E, Rudie J, Villanueva-Meyer J, Cha S, Ingalhalikar M, Jadhav M, Pandey U, Saini J, Garrett J, Larson M, Jeraj R, Currie S, Frood R, Fatania K, Huang RY, Chang K, Balaña C, Capellades J, Puig J, Trenkler J, Pichler J, Necker G, Haunschmidt A, Meckel S, Shukla G, Liem S, Alexander GS, Lombardo J, Palmer JD, Flanders AE, Dicker AP, Sair HI, Jones CK, Venkataraman A, Jiang M, So TY, Chen C, Heng PA, Dou Q, Kozubek M, Lux F, Michálek J, Matula P, Keřkovský M, Kopřivová T, Dostál M, Vybíhal V, Vogelbaum MA, Mitchell JR, Farinhas J, Maldjian JA, Yogananda CGB, Pinho MC, Reddy D, Holcomb J, Wagner BC, Ellingson BM, Cloughesy TF, Raymond C, Oughourlian T, Hagiwara A, Wang C, To MS, Bhardwaj S, Chong C, Agzarian M, Falcão AX, Martins SB, Teixeira BCA, Sprenger F, Menotti D, Lucio DR, LaMontagne P, Marcus D, Wiestler B, Kofler F, Ezhov I, Metz M, Jain R, Lee M, Lui YW, McKinley R, Slotboom J, Radojewski P, Meier R, Wiest R, Murcia D, Fu E, Haas R, Thompson J, Ormond DR, Badve C, Sloan AE, Vadmal V, Waite K, Colen RR, Pei L, Ak M, Srinivasan A, Bapuraj JR, Rao A, Wang N, Yoshiaki O, Moritani T, Turk S, Lee J, Prabhudesai S, Morón F, Mandel J, Kamnitsas K, Glocker B, Dixon LVM, Williams M, Zampakis P, Panagiotopoulos V, Tsiganos P, Alexiou S, Haliassos I, Zacharaki EI, Moustakas K, Kalogeropoulou C, Kardamakis DM, Choi YS, Lee SK, Chang JH, Ahn SS, Luo B, Poisson L, Wen N, Tiwari P, Verma R, Bareja R, Yadav I, Chen J, Kumar N, Smits M, van der Voort SR, Alafandi A, Incekara F, Wijnenga MMJ, Kapsas G, Gahrmann R, Schouten JW, Dubbink HJ, Vincent AJPE, van den Bent MJ, French PJ, Klein S, Yuan Y, Sharma S, Tseng TC, Adabi S, Niclou SP, Keunen O, Hau AC, Vallières M, Fortin D, Lepage M, Landman B, Ramadass K, Xu K, Chotai S, Chambless LB, Mistry A, Thompson RC, Gusev Y, Bhuvaneshwar K, Sayah A, Bencheqroun C, Belouali A, Madhavan S, Booth TC, Chelliah A, Modat M, Shuaib H, Dragos C, Abayazeed A, Kolodziej K, Hill M, Abbassy A, Gamal S, Mekhaimar M, Qayati M, Reyes M, Park JE, Yun J, Kim HS, Mahajan A, Muzi M, Benson S, Beets-Tan RGH, Teuwen J, Herrera-Trujillo A, Trujillo M, Escobar W, Abello A, Bernal J, Gómez J, Choi J, Baek S, Kim Y, Ismael H, Allen B, Buatti JM, Kotrotsou A, Li H, Weiss T, Weller M, Bink A, Pouymayou B, Shaykh HF, Saltz J, Prasanna P, Shrestha S, Mani KM, Payne D, Kurc T, Pelaez E, Franco-Maldonado H, Loayza F, Quevedo S, Guevara P, Torche E, Mendoza C, Vera F, Ríos E, López E, Velastin SA, Ogbole G, Soneye M, Oyekunle D, Odafe-Oyibotha O, Osobu B, Shu'aibu M, Dorcas A, Dako F, Simpson AL, Hamghalam M, Peoples JJ, Hu R, Tran A, Cutler D, Moraes FY, Boss MA, Gimpel J, Veettil DK, Schmidt K, Bialecki B, Marella S, Price C, Cimino L, Apgar C, Shah P, Menze B, Barnholtz-Sloan JS, Martin J, Bakas S. Federated learning enables big data for rare cancer boundary detection. Nat Commun 2022; 13:7346. [PMID: 36470898 PMCID: PMC9722782 DOI: 10.1038/s41467-022-33407-5] [Citation(s) in RCA: 50] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 09/16/2022] [Indexed: 12/12/2022] Open
Abstract
Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing.
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Affiliation(s)
- Sarthak Pati
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Informatics, Technical University of Munich, Munich, Bavaria, Germany
| | - Ujjwal Baid
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | | | | | | | | | | | | | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Chiharu Sako
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Satyam Ghodasara
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michel Bilello
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Suyash Mohan
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Philipp Vollmuth
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Gianluca Brugnara
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | | | - Felix Sahm
- Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Neuropathology, Heidelberg University Hospital, Heidelberg, Germany
| | - Klaus Maier-Hein
- Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
- Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Maximilian Zenk
- Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Wolfgang Wick
- Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ), Heidelberg, Germany
- Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany
| | - Evan Calabrese
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Jeffrey Rudie
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Javier Villanueva-Meyer
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Soonmee Cha
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Madhura Ingalhalikar
- Symbiosis Center for Medical Image Analysis, Symbiosis International University, Pune, Maharashtra, India
| | - Manali Jadhav
- Symbiosis Center for Medical Image Analysis, Symbiosis International University, Pune, Maharashtra, India
| | - Umang Pandey
- Symbiosis Center for Medical Image Analysis, Symbiosis International University, Pune, Maharashtra, India
| | - Jitender Saini
- Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Bangalore, Karnataka, India
| | - John Garrett
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Department of Medical Physics, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Matthew Larson
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Robert Jeraj
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Department of Medical Physics, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Stuart Currie
- Leeds Teaching Hospitals Trust, Department of Radiology, Leeds, UK
| | - Russell Frood
- Leeds Teaching Hospitals Trust, Department of Radiology, Leeds, UK
| | - Kavi Fatania
- Leeds Teaching Hospitals Trust, Department of Radiology, Leeds, UK
| | - Raymond Y Huang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Ken Chang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | | | | | - Josep Puig
- Department of Radiology (IDI), Girona Biomedical Research Institute (IdIBGi), Josep Trueta University Hospital, Girona, Spain
| | - Johannes Trenkler
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Josef Pichler
- Department of Neurooncology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Georg Necker
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Andreas Haunschmidt
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Stephan Meckel
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
- Institute of Diagnostic and Interventional Neuroradiology, RKH Klinikum Ludwigsburg, Ludwigsburg, Germany
| | - Gaurav Shukla
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiation Oncology, Christiana Care Health System, Philadelphia, PA, USA
| | - Spencer Liem
- Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA
| | - Gregory S Alexander
- Department of Radiation Oncology, University of Maryland, Baltimore, MD, USA
| | - Joseph Lombardo
- Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA
- Department of Radiation Oncology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Joshua D Palmer
- Department of Radiation Oncology, The James Cancer Hospital and Solove Research Institute, The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA
| | - Adam E Flanders
- Department of Radiology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Adam P Dicker
- Department of Radiation Oncology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Haris I Sair
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- The Malone Center for Engineering in Healthcare, The Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Craig K Jones
- The Malone Center for Engineering in Healthcare, The Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Archana Venkataraman
- Department of Electrical and Computer Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Meirui Jiang
- The Chinese University of Hong Kong, Hong Kong, China
| | - Tiffany Y So
- The Chinese University of Hong Kong, Hong Kong, China
| | - Cheng Chen
- The Chinese University of Hong Kong, Hong Kong, China
| | | | - Qi Dou
- The Chinese University of Hong Kong, Hong Kong, China
| | - Michal Kozubek
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Filip Lux
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Jan Michálek
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Petr Matula
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Miloš Keřkovský
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Masaryk University, Brno and University Hospital Brno, Brno, Czech Republic
| | - Tereza Kopřivová
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Masaryk University, Brno and University Hospital Brno, Brno, Czech Republic
| | - Marek Dostál
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Masaryk University, Brno and University Hospital Brno, Brno, Czech Republic
- Department of Biophysics, Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Václav Vybíhal
- Department of Neurosurgery, Faculty of Medicine, Masaryk University, Brno, and University Hospital and Czech Republic, Brno, Czech Republic
| | - Michael A Vogelbaum
- Department of Neuro Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - J Ross Mitchell
- University of Alberta, Edmonton, AB, Canada
- Alberta Machine Intelligence Institute, Edmonton, AB, Canada
| | - Joaquim Farinhas
- Department of Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | | | | | - Marco C Pinho
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Divya Reddy
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - James Holcomb
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | | | - Benjamin M Ellingson
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- UCLA Neuro-Oncology Program, Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CaA, USA
| | - Timothy F Cloughesy
- UCLA Neuro-Oncology Program, Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CaA, USA
| | - Catalina Raymond
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Talia Oughourlian
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Akifumi Hagiwara
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Chencai Wang
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Minh-Son To
- College of Medicine and Public Health, Flinders University, Bedford Park, SA, Australia
- Division of Surgery and Perioperative Medicine, Flinders Medical Centre, Bedford Park, SA, Australia
| | - Sargam Bhardwaj
- College of Medicine and Public Health, Flinders University, Bedford Park, SA, Australia
| | - Chee Chong
- South Australia Medical Imaging, Flinders Medical Centre, Bedford Park, SA, Australia
| | - Marc Agzarian
- South Australia Medical Imaging, Flinders Medical Centre, Bedford Park, SA, Australia
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA
| | | | | | - Bernardo C A Teixeira
- Instituto de Neurologia de Curitiba, Curitiba, Paraná, Brazil
- Department of Radiology, Hospital de Clínicas da Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - Flávia Sprenger
- Department of Radiology, Hospital de Clínicas da Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - David Menotti
- Department of Informatics, Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - Diego R Lucio
- Department of Informatics, Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - Pamela LaMontagne
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Daniel Marcus
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Benedikt Wiestler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TranslaTUM (Zentralinstitut für translationale Krebsforschung der Technischen Universität München), Klinikum rechts der Isar, Munich, Germany
| | - Florian Kofler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TranslaTUM (Zentralinstitut für translationale Krebsforschung der Technischen Universität München), Klinikum rechts der Isar, Munich, Germany
- Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, Munich, Germany
| | - Ivan Ezhov
- Department of Informatics, Technical University of Munich, Munich, Bavaria, Germany
- TranslaTUM (Zentralinstitut für translationale Krebsforschung der Technischen Universität München), Klinikum rechts der Isar, Munich, Germany
- Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, Munich, Germany
| | - Marie Metz
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Rajan Jain
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
- Department of Neurosurgery, NYU Grossman School of Medicine, New York, NY, USA
| | - Matthew Lee
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
| | - Yvonne W Lui
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
| | - Richard McKinley
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Johannes Slotboom
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Piotr Radojewski
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Raphael Meier
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Roland Wiest
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Derrick Murcia
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - Eric Fu
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - Rourke Haas
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - John Thompson
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - David Ryan Ormond
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - Chaitra Badve
- Department of Radiology, University Hospitals Cleveland, Cleveland, OH, USA
| | - Andrew E Sloan
- Department of Neurological Surgery, University Hospitals-Seidman Cancer Center, Cleveland, OH, USA
- Case Comprehensive Cancer Center, Cleveland, OH, USA
- Department of Neurosurgery, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Vachan Vadmal
- Department of Neurosurgery, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Kristin Waite
- National Cancer Institute, National Institute of Health, Division of Cancer Epidemiology and Genetics, Bethesda, MD, USA
| | - Rivka R Colen
- Department of Radiology, Neuroradiology Division, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Diagnostic Radiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Linmin Pei
- University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Murat Ak
- Department of Radiology, Neuroradiology Division, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ashok Srinivasan
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - J Rajiv Bapuraj
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Arvind Rao
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Nicholas Wang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Ota Yoshiaki
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Toshio Moritani
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Sevcan Turk
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Joonsang Lee
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Snehal Prabhudesai
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Fanny Morón
- Department of Radiology, Baylor College of Medicine, Houston, TX, USA
| | - Jacob Mandel
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA
| | - Konstantinos Kamnitsas
- Department of Computing, Imperial College London, London, UK
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Ben Glocker
- Department of Computing, Imperial College London, London, UK
| | - Luke V M Dixon
- Department of Radiology, Imperial College NHS Healthcare Trust, London, UK
| | - Matthew Williams
- Computational Oncology Group, Institute for Global Health Innovation, Imperial College London, London, UK
| | - Peter Zampakis
- Department of NeuroRadiology, University of Patras, Patras, Greece
| | | | - Panagiotis Tsiganos
- Clinical Radiology Laboratory, Department of Medicine, University of Patras, Patras, Greece
| | - Sotiris Alexiou
- Department of Electrical and Computer Engineering, University of Patras, Patras, Greece
| | - Ilias Haliassos
- Department of Neuro-Oncology, University of Patras, Patras, Greece
| | - Evangelia I Zacharaki
- Department of Electrical and Computer Engineering, University of Patras, Patras, Greece
| | | | | | | | | | | | | | - Sung Soo Ahn
- Yonsei University College of Medicine, Seoul, Korea
| | - Bing Luo
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, USA
| | - Laila Poisson
- Public Health Sciences, Henry Ford Health System, Detroit, MI, USA
| | - Ning Wen
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, USA
- SJTU-Ruijin-UIH Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 200025, Shanghai, China
| | | | - Ruchika Verma
- Alberta Machine Intelligence Institute, Edmonton, AB, Canada
- Case Western Reserve University, Cleveland, OH, USA
| | - Rohan Bareja
- Case Western Reserve University, Cleveland, OH, USA
| | - Ipsa Yadav
- Case Western Reserve University, Cleveland, OH, USA
| | | | - Neeraj Kumar
- University of Alberta, Edmonton, AB, Canada
- Alberta Machine Intelligence Institute, Edmonton, AB, Canada
| | - Marion Smits
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Sebastian R van der Voort
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Ahmed Alafandi
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Fatih Incekara
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
- Department of Neurosurgery, Brain Tumor Center, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Maarten M J Wijnenga
- Department of Neurology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Georgios Kapsas
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Renske Gahrmann
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Joost W Schouten
- Department of Neurosurgery, Brain Tumor Center, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Hendrikus J Dubbink
- Department of Pathology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Arnaud J P E Vincent
- Department of Neurosurgery, Brain Tumor Center, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Martin J van den Bent
- Department of Neurology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Pim J French
- Department of Neurology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Stefan Klein
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Yading Yuan
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sonam Sharma
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Tzu-Chi Tseng
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Saba Adabi
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Simone P Niclou
- NORLUX Neuro-Oncology Laboratory, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Olivier Keunen
- Translation Radiomics, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Ann-Christin Hau
- NORLUX Neuro-Oncology Laboratory, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, Luxembourg
- Luxembourg Center of Neuropathology, Laboratoire National De Santé, Luxembourg, Luxembourg
| | - Martin Vallières
- Department of Computer Science, Université de Sherbrooke, Sherbrooke, QC, Canada
- Centre de Recherche du Centre Hospitalière Universitaire de Sherbrooke, Sherbrooke, QC, Canada
| | - David Fortin
- Centre de Recherche du Centre Hospitalière Universitaire de Sherbrooke, Sherbrooke, QC, Canada
- Division of Neurosurgery and Neuro-Oncology, Faculty of Medicine and Health Science, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Martin Lepage
- Centre de Recherche du Centre Hospitalière Universitaire de Sherbrooke, Sherbrooke, QC, Canada
- Department of Nuclear Medicine and Radiobiology, Sherbrooke Molecular Imaging Centre, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Bennett Landman
- Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Karthik Ramadass
- Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Kaiwen Xu
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Silky Chotai
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lola B Chambless
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Akshitkumar Mistry
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Reid C Thompson
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yuriy Gusev
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Krithika Bhuvaneshwar
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Anousheh Sayah
- Division of Neuroradiology & Neurointerventional Radiology, Department of Radiology, MedStar Georgetown University Hospital, Washington, DC, USA
| | - Camelia Bencheqroun
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Anas Belouali
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Subha Madhavan
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Thomas C Booth
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
- Department of Neuroradiology, Ruskin Wing, King's College Hospital NHS Foundation Trust, London, UK
| | - Alysha Chelliah
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Marc Modat
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Haris Shuaib
- Stoke Mandeville Hospital, Mandeville Road, Aylesbury, UK
- Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada
| | - Carmen Dragos
- Stoke Mandeville Hospital, Mandeville Road, Aylesbury, UK
| | | | | | | | | | - Shady Gamal
- University of Cairo School of Medicine, Giza, Egypt
| | | | | | | | - Ji Eun Park
- Department of Radiology, Asan Medical Center, Seoul, South Korea
| | - Jihye Yun
- Department of Radiology, Asan Medical Center, Seoul, South Korea
| | - Ho Sung Kim
- Department of Radiology, Asan Medical Center, Seoul, South Korea
| | - Abhishek Mahajan
- The Clatterbridge Cancer Centre NHS Foundation Trust Pembroke Place, Liverpool, UK
| | - Mark Muzi
- Department of Radiology, University of Washington, Seattle, WA, USA
| | - Sean Benson
- Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Regina G H Beets-Tan
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, Netherlands
- GROW School of Oncology and Developmental Biology, Maastricht, Netherlands
| | - Jonas Teuwen
- Netherlands Cancer Institute, Amsterdam, Netherlands
| | | | | | - William Escobar
- Clínica Imbanaco Grupo Quirón Salud, Cali, Colombia
- Universidad del Valle, Cali, Colombia
| | | | - Jose Bernal
- Universidad del Valle, Cali, Colombia
- The University of Edinburgh, Edinburgh, UK
| | | | - Joseph Choi
- Department of Industrial and Systems Engineering, University of Iowa, Iowa, USA
| | - Stephen Baek
- Department of Industrial and Systems Engineering, Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - Yusung Kim
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - Heba Ismael
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - Bryan Allen
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - John M Buatti
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | | | - Hongwei Li
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Tobias Weiss
- Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Michael Weller
- Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Andrea Bink
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Bertrand Pouymayou
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | | | - Joel Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Prateek Prasanna
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Sampurna Shrestha
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Kartik M Mani
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
- Department of Radiation Oncology, Stony Brook University, Stony Brook, NY, USA
| | - David Payne
- Department of Radiology, Stony Brook University, Stony Brook, NY, USA
| | - Tahsin Kurc
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
- Scientific Data Group, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Enrique Pelaez
- Escuela Superior Politecnica del Litoral, Guayaquil, Guayas, Ecuador
| | | | - Francis Loayza
- Escuela Superior Politecnica del Litoral, Guayaquil, Guayas, Ecuador
| | | | | | | | | | - Franco Vera
- Universidad de Concepción, Concepción, Biobío, Chile
| | - Elvis Ríos
- Universidad de Concepción, Concepción, Biobío, Chile
| | - Eduardo López
- Universidad de Concepción, Concepción, Biobío, Chile
| | - Sergio A Velastin
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK
| | - Godwin Ogbole
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | - Mayowa Soneye
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | - Dotun Oyekunle
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | | | - Babatunde Osobu
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | - Mustapha Shu'aibu
- Department of Radiology, Muhammad Abdullahi Wase Teaching Hospital, Kano, Nigeria
| | - Adeleye Dorcas
- Department of Radiology, Obafemi Awolowo University Ile-Ife, Ile-Ife, Osun, Nigeria
| | - Farouk Dako
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Global Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Amber L Simpson
- Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Mohammad Hamghalam
- School of Computing, Queen's University, Kingston, ON, Canada
- Department of Electrical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
| | - Jacob J Peoples
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Ricky Hu
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Anh Tran
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Danielle Cutler
- The Faculty of Arts & Sciences, Queen's University, Kingston, ON, Canada
| | - Fabio Y Moraes
- Department of Oncology, Queen's University, Kingston, ON, Canada
| | - Michael A Boss
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - James Gimpel
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Deepak Kattil Veettil
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Kendall Schmidt
- Data Science Institute, American College of Radiology, Reston, VA, USA
| | - Brian Bialecki
- Data Science Institute, American College of Radiology, Reston, VA, USA
| | - Sailaja Marella
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Cynthia Price
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Lisa Cimino
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Charles Apgar
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | | | - Bjoern Menze
- Department of Informatics, Technical University of Munich, Munich, Bavaria, Germany
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Jill S Barnholtz-Sloan
- National Cancer Institute, National Institute of Health, Division of Cancer Epidemiology and Genetics, Bethesda, MD, USA
- Center for Biomedical Informatics and Information Technology, National Cancer Institute (NCI), National Institute of Health, Bethesda, MD, USA
| | | | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA.
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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17
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Sperduto PW, De B, Li J, Carpenter D, Kirkpatrick J, Milligan M, Shih HA, Kutuk T, Kotecha R, Higaki H, Otsuka M, Aoyama H, Bourgoin M, Roberge D, Dajani S, Sachdev S, Gainey J, Buatti JM, Breen W, Brown PD, Ni L, Braunstein S, Gallitto M, Wang TJC, Shanley R, Lou E, Shiao J, Gaspar LE, Tanabe S, Nakano T, An Y, Chiang V, Zeng L, Soliman H, Elhalawani H, Cagney D, Thomas E, Boggs DH, Ahluwalia MS, Mehta MP. Graded Prognostic Assessment (GPA) for Patients With Lung Cancer and Brain Metastases: Initial Report of the Small Cell Lung Cancer GPA and Update of the Non-Small Cell Lung Cancer GPA Including the Effect of Programmed Death Ligand 1 and Other Prognostic Factors. Int J Radiat Oncol Biol Phys 2022; 114:60-74. [PMID: 35331827 PMCID: PMC9378572 DOI: 10.1016/j.ijrobp.2022.03.020] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 03/02/2022] [Accepted: 03/14/2022] [Indexed: 11/23/2022]
Abstract
PURPOSE Patients with lung cancer and brain metastases represent a markedly heterogeneous population. Accurate prognosis is essential to optimally individualize care. In prior publications, we described the graded prognostic assessment (GPA), but a GPA for patients with small cell lung cancer (SCLC) has never been reported, and in non-small cell lung cancer (NSCLC), the effect of programmed death ligand 1 (PD-L1) was unknown. The 3-fold purpose of this work is to provide the initial report of an SCLC GPA, to evaluate the effect of PD-L1 on survival in patients with NSCLC, and to update the Lung GPA accordingly. METHODS AND MATERIALS A multivariable analysis of prognostic factors and treatments associated with survival was performed on 4183 patients with lung cancer (3002 adenocarcinoma, 611 nonadenocarcinoma, 570 SCLC) with newly diagnosed brain metastases between January 1, 2015, and December 31, 2020, using a multi-institutional retrospective database. Significant variables were used to update the Lung GPA. RESULTS Overall median survival for lung adenocarcinoma, SCLC, and nonadenocarcinoma was 17, 10, and 8 months, respectively, but varied widely by GPA from 2 to 52 months. In SCLC, the significant prognostic factors were age, performance status, extracranial metastases, and number of brain metastases. In NSCLC, the distribution of molecular markers among patients with lung adenocarcinoma and known primary tumor molecular status revealed alterations/expression in PD-L1 50% to 100%, PD-L1 1% to 49%, epidermal growth factor receptor, and anaplastic lymphoma kinase in 32%, 31%, 30%, and 7%, respectively. Median survival of patients with lung adenocarcinoma and brain metastases with 0, 1% to 49%, and ≥50% PD-L1 expression was 17, 19, and 24 months, respectively (P < .01), confirming PD-L1 is a prognostic factor. Previously identified prognostic factors for NSCLC (epidermal growth factor receptor and anaplastic lymphoma kinase status, performance status, age, number of brain metastases, and extracranial metastases) were reaffirmed. These factors were incorporated into the updated Lung GPA with robust separation between subgroups for all histologies. CONCLUSIONS Survival for patients with lung cancer and brain metastases has improved but varies widely. The initial report of a GPA for SCLC is presented. For patients with NSCLC-adenocarcinoma and brain metastases, PD-L1 is a newly identified significant prognostic factor, and the previously identified factors were reaffirmed. The updated indices establish unique criteria for SCLC, NSCLC-nonadenocarcinoma, and NSCLC-adenocarcinoma (incorporating PD-L1). The updated Lung GPA, available for free at brainmetgpa.com, provides an accurate tool to estimate survival, individualize treatment, and stratify clinical trials.
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Affiliation(s)
| | - Brian De
- MD Anderson Cancer Center, Houston, Texas
| | - Jing Li
- MD Anderson Cancer Center, Houston, Texas
| | | | | | | | - Helen A Shih
- Massachusetts General Hospital, Boston, Massachusetts
| | - Tugce Kutuk
- Miami Cancer Institute, Baptist Health South Florida, Miami, Florida
| | - Rupesh Kotecha
- Miami Cancer Institute, Baptist Health South Florida, Miami, Florida
| | | | | | - Hidefumi Aoyama
- Hokkaido Cancer Center, Hokkaido, Japan; Hokkaido University, Sapporo, Japan
| | - Malie Bourgoin
- Centre Hospitalier de l' Université de Montreal, Montreal, Quebec, Canada
| | - David Roberge
- Centre Hospitalier de l' Université de Montreal, Montreal, Quebec, Canada
| | | | | | | | | | | | | | - Lisa Ni
- University of California, San Francisco, California
| | | | | | | | | | - Emil Lou
- University of Minnesota, Minneapolis, Minnesota
| | - Jay Shiao
- University of Colorado Denver, Denver, Colorado
| | - Laurie E Gaspar
- University of Colorado Denver, Denver, Colorado; Banner MD Anderson Cancer Center, Loveland, Colorado
| | | | | | - Yi An
- Yale University, New Haven, Connecticut
| | | | - Liang Zeng
- Sunnybrook Odette Cancer Centre, University of Toronto, Toronto, Ontario, Canada
| | - Hany Soliman
- Sunnybrook Odette Cancer Centre, University of Toronto, Toronto, Ontario, Canada
| | | | | | - Evan Thomas
- University of Alabama at Birmingham, Birmingham, Alabama
| | | | | | - Minesh P Mehta
- Miami Cancer Institute, Baptist Health South Florida, Miami, Florida
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18
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Jacobson GM, Bajaj GK, Buatti JM, Dawson L, Deville C, Eichler TJ, Erickson B, Ford E, Gibbs IC, Mantz C, Marples B, Michalski JM, Sandler H, Smith B, Vapiwala N, Yashar C. ASTRO Supports Access to Evidence-Based Cancer Care for All Patients, Regardless of Pregnancy Status, and Protection for Physicians Recommending and Providing Evidence-Based Care. Int J Radiat Oncol Biol Phys 2022; 114:390-392. [PMID: 35963472 DOI: 10.1016/j.ijrobp.2022.07.1844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Accepted: 07/31/2022] [Indexed: 10/31/2022]
Affiliation(s)
| | - Gopal K Bajaj
- Department of Radiation Oncology, Inova Schar Cancer Institute, Fairfax, Virginia
| | - John M Buatti
- Carver College of Medicine, University of Iowa, Iowa City, Iowa
| | - Laura Dawson
- Department of Radiation Oncology, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | | | | | | | - Eric Ford
- Department of Radiation Oncology, University of Washington, Seattle, Washington
| | | | | | - Brian Marples
- Department of Radiation Oncology, University of Rochester, Rochester, New York
| | - Jeff M Michalski
- Siteman Cancer Center, Washington University, St. Louis, Missouri
| | | | - Benjamin Smith
- University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Neha Vapiwala
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Catheryn Yashar
- Moores UCSD Cancer Center, University of California, San Diego, La Jolla, California
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19
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Tamplin MR, Wang JK, Vitale AH, Hashimoto R, Garvin MK, Binkley EM, Hyer DE, Buatti JM, Boldt HC, Kardon RH, Grumbach IM. Reduced blood flow by laser speckle flowgraphy after 125I-plaque brachytherapy for uveal melanoma. BMC Ophthalmol 2022; 22:285. [PMID: 35765019 PMCID: PMC9238054 DOI: 10.1186/s12886-022-02505-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 06/22/2022] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND To determine whether reductions in retinal and choroidal blood flow measured by laser speckle flowgraphy are detected after 125I-plaque brachytherapy for uveal melanoma. METHODS In a cross-sectional study, retinal and choroidal blood flow were measured using laser speckle flowgraphy in 25 patients after treatment with 125I-plaque brachytherapy for uveal melanoma. Flow was analyzed in the peripapillary region by mean blur rate as well as in the entire image area with a novel superpixel-based method. Relationships between measures were determined by Spearman correlation. RESULTS Significant decreases in laser speckle blood flow were observed in both the retinal and choroidal vascular beds of irradiated, but not fellow, eyes. Overall, 24 of 25 patients had decreased blood flow compared to their fellow eye, including 5 of the 6 patients imaged within the first 6 months following brachytherapy. A significant negative correlation between blood flow and time from therapy was present. CONCLUSIONS Decreases in retinal and choroidal blood flow by laser speckle flowgraphy were detected within the first 6 months following brachytherapy. Reduced retinal and choroidal blood flow may be an early indicator of microangiographic response to radiation therapy.
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Affiliation(s)
- Michelle R Tamplin
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - Jui-Kai Wang
- Iowa City VA Center for the Prevention and Treatment of Visual Loss, Iowa City, IA, USA
- Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA, USA
- Department of Ophthalmology and Visual Sciences, Division of Neuro-Ophthalmology, University of Iowa, Iowa City, IA, 52242, USA
| | - Anthony H Vitale
- Department of Internal Medicine, Division of Cardiovascular Medicine, Abboud Cardiovascular Research Center, Carver College of Medicine, University of Iowa, Iowa City, IA, 52242, USA
| | - Ryuya Hashimoto
- Department of Ophthalmology and Visual Sciences, Division of Neuro-Ophthalmology, University of Iowa, Iowa City, IA, 52242, USA
| | - Mona K Garvin
- Iowa City VA Center for the Prevention and Treatment of Visual Loss, Iowa City, IA, USA
- Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA, USA
| | - Elaine M Binkley
- Department of Ophthalmology and Visual Sciences, Division of Neuro-Ophthalmology, University of Iowa, Iowa City, IA, 52242, USA
| | - Daniel E Hyer
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - John M Buatti
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - H Culver Boldt
- Department of Ophthalmology and Visual Sciences, Division of Neuro-Ophthalmology, University of Iowa, Iowa City, IA, 52242, USA
| | - Randy H Kardon
- Iowa City VA Center for the Prevention and Treatment of Visual Loss, Iowa City, IA, USA.
- Department of Ophthalmology and Visual Sciences, Division of Neuro-Ophthalmology, University of Iowa, Iowa City, IA, 52242, USA.
| | - Isabella M Grumbach
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA.
- Iowa City VA Center for the Prevention and Treatment of Visual Loss, Iowa City, IA, USA.
- Department of Internal Medicine, Division of Cardiovascular Medicine, Abboud Cardiovascular Research Center, Carver College of Medicine, University of Iowa, Iowa City, IA, 52242, USA.
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20
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Callaghan CM, Abukhiran IM, Van Rheeden RV, Kalen AL, Rodman SN, Petronek MS, Mapuskar KA, Mott SL, Coleman MC, Goswami PC, Buatti JM, Allen BG, Spitz DR, Caster JM. Abstract 811: Pharmacologic ascorbate opens a therapeutic window for ATM inhibition and radiotherapy in colorectal cancer. Cancer Res 2022. [DOI: 10.1158/1538-7445.am2022-811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Purpose/Objective(s): The ATM protein is key to DNA double strand break (DSB) repair and ATM inhibitors are potent radiosensitizers. Clinical translation of these agents in combination with radiotherapy (RT) has been limited due to concerns for increased normal tissue toxicity. Pharmacologic Ascorbate (P-AscH-) radiosensitizes cancer cells via generation of a H2O2 flux while acting as a radioprotector in normal tissue via free radicle scavenging. We hypothesized that P-AscH- could open a therapeutic window for the combination of RT and ATM inhibition in colorectal cancer (CRC).
Materials/Methods: Multiple human and murine CRC cell lines were used in vitro. Clonogenic survival was assessed after combinations of RT +/- P-AscH and DNA Repair Inhibitors (DRIs). Catalase expression was induced using HCT116 cells expressing a doxycycline-inducible catalase transgene. DSBs were quantified using neutral comet assays. Cell cycle distribution were assessed using flow cytometry. ATM localization/activation were assessed using IF. Normal tissue toxicity in vivo was assessed using IHC following whole-abdominal RT. Survival and tumor growth delay was assessed following 5Gyx3 +/- drug treatment to unilateral flank tumors in syngeneic/xenograft models.
Results: DRIs were potent radiosensitizers in most CRC cell lines and the addition of P-AscH- further reduced clonogenic survival. In contrast, P-AscH- did not radiosensitize HUVEC or FHs-74int normal cell lines. P-AscH- significantly increased the number of DSBs in tumors after RT in vitro. P-AscH- simultaneously decreased nuclear localization and activation of pATM after RT and perturbed G2+M phase progression. In vivo, the addition of P-AscH- to RT + KU60019 significantly increased survival delayed tumor growth in syngeneic/xenograft models while ameliorating increased normal bowel toxicity as measured by jejunal crypt density, acute weight loss, rectal injury, and markers of oxidative stress following whole-abdominal RT. The effects of P-AscH were reversed by inducing the overexpression of catalase.
Conclusion: P-AscH- improves both aspects of the therapeutic window of RT+ATM inhibition in CRC by simultaneously enhancing tumor efficacy while decreasing RT-mediated bowel toxicity. The effects of P-AscH- on clonogenic survival, initial and persistent DSBs, G2+M phase perturbations, ATM activation/localization, and in vivo survival and tumor growth delay were dependent on H202 flux. Normal tissue protection appears to be related to decreased oxidative stress.
Citation Format: Cameron M. Callaghan, Ibrahim M. Abukhiran, Richard V. Van Rheeden, Amanda L. Kalen, Samuel N. Rodman, Michael S. Petronek, Kranti A. Mapuskar, Sarah L. Mott, Mitchell C. Coleman, Prabhat C. Goswami, John M. Buatti, Bryan G. Allen, Douglas R. Spitz, Joseph M. Caster. Pharmacologic ascorbate opens a therapeutic window for ATM inhibition and radiotherapy in colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 811.
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Affiliation(s)
| | | | | | | | | | | | | | - Sarah L. Mott
- 1University of Iowa Hospitals and Clinics, Iowa City, IA
| | | | | | - John M. Buatti
- 1University of Iowa Hospitals and Clinics, Iowa City, IA
| | - Bryan G. Allen
- 1University of Iowa Hospitals and Clinics, Iowa City, IA
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21
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Furqan M, Abu-Hejleh T, Stephens LM, Hartwig SM, Mott SL, Pulliam CF, Petronek M, Henrich JB, Fath MA, Houtman JC, Varga SM, Bodeker KL, Bossler AD, Bellizzi AM, Zhang J, Monga V, Mani H, Ivanovic M, Smith BJ, Byrne MM, Zeitler W, Wagner BA, Buettner GR, Cullen JJ, Buatti JM, Spitz DR, Allen BG. Pharmacological ascorbate improves the response to platinum-based chemotherapy in advanced stage non-small cell lung cancer. Redox Biol 2022; 53:102318. [PMID: 35525024 PMCID: PMC9079696 DOI: 10.1016/j.redox.2022.102318] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 04/05/2022] [Accepted: 04/17/2022] [Indexed: 01/28/2023] Open
Abstract
PURPOSE Platinum-based chemotherapy with or without immunotherapy is the mainstay of treatment for advanced stage non-small cell lung cancer (NSCLC) lacking a molecular driver alteration. Pre-clinical studies have reported that pharmacological ascorbate (P-AscH-) enhances NSCLC response to platinum-based therapy. We conducted a phase II clinical trial combining P-AscH- with carboplatin-paclitaxel chemotherapy. EXPERIMENTAL DESIGN Chemotherapy naïve advanced stage NSCLC patients received 75 g ascorbate twice per week intravenously with carboplatin and paclitaxel every three weeks for four cycles. The primary endpoint was to improve tumor response per Response Evaluation Criteria in Solid Tumors (RECIST) v1.1 compared to the historical control of 20%. The trial was conducted as an optimal Simon's two-stage design. Blood samples were collected for exploratory analyses. RESULTS The study enrolled 38 patients and met its primary endpoint with an objective response rate of 34.2% (p = 0.03). All were confirmed partial responses (cPR). The disease control rate was 84.2% (stable disease + cPR). Median progression-free and overall survival were 5.7 months and 12.8 months, respectively. Treatment-related adverse events (TRAE) included one grade 5 (neutropenic fever) and five grade 4 events (cytopenias). Cytokine and chemokine data suggest that the combination elicits an immune response. Immunophenotyping of peripheral blood mononuclear cells demonstrated an increase in effector CD8 T-cells in patients with a progression-free survival (PFS) ≥ 6 months. CONCLUSIONS The addition of P-AscH- to platinum-based chemotherapy improved tumor response in advanced stage NSCLC. P-AscH- appears to alter the host immune response and needs further investigation as a potential adjuvant to immunotherapy.
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Affiliation(s)
- Muhammad Furqan
- Department of Internal Medicine, University of Iowa, 200 Hawkins Dr, Iowa City, IA, 52242, USA,Holden Comprehensive Cancer Center, University of Iowa, 200 Hawkins Dr, Iowa City, IA, 52242, USA,Corresponding author. Department of Internal Medicine, University of Iowa, 200 Hawkins Dr, C21-K GH, Iowa City, IA, 52242, USA.
| | - Taher Abu-Hejleh
- Department of Internal Medicine, University of Iowa, 200 Hawkins Dr, Iowa City, IA, 52242, USA,Holden Comprehensive Cancer Center, University of Iowa, 200 Hawkins Dr, Iowa City, IA, 52242, USA
| | - Laura M. Stephens
- Interdisciplinary Graduate Program in Immunology, University of Iowa, 200 Hawkins Dr, Iowa City, IA, 52242, USA
| | - Stacey M. Hartwig
- Department of Microbiology and Immunology, University of Iowa, 51 Newton Rd., Iowa City, IA, 52242, USA
| | - Sarah L. Mott
- Holden Comprehensive Cancer Center, University of Iowa, 200 Hawkins Dr, Iowa City, IA, 52242, USA
| | - Casey F. Pulliam
- Holden Comprehensive Cancer Center, University of Iowa, 200 Hawkins Dr, Iowa City, IA, 52242, USA,Department of Radiation Oncology, Free Radical and Radiation Biology Program, University of Iowa, 200 Hawkins Dr, Iowa City, IA, 52242, USA
| | - Michael Petronek
- Holden Comprehensive Cancer Center, University of Iowa, 200 Hawkins Dr, Iowa City, IA, 52242, USA,Department of Radiation Oncology, Free Radical and Radiation Biology Program, University of Iowa, 200 Hawkins Dr, Iowa City, IA, 52242, USA
| | - John B. Henrich
- Holden Comprehensive Cancer Center, University of Iowa, 200 Hawkins Dr, Iowa City, IA, 52242, USA,Department of Radiation Oncology, Free Radical and Radiation Biology Program, University of Iowa, 200 Hawkins Dr, Iowa City, IA, 52242, USA
| | - Melissa A. Fath
- Holden Comprehensive Cancer Center, University of Iowa, 200 Hawkins Dr, Iowa City, IA, 52242, USA,Department of Radiation Oncology, Free Radical and Radiation Biology Program, University of Iowa, 200 Hawkins Dr, Iowa City, IA, 52242, USA
| | - Jon C. Houtman
- Holden Comprehensive Cancer Center, University of Iowa, 200 Hawkins Dr, Iowa City, IA, 52242, USA,Interdisciplinary Graduate Program in Immunology, University of Iowa, 200 Hawkins Dr, Iowa City, IA, 52242, USA,Department of Microbiology and Immunology, University of Iowa, 51 Newton Rd., Iowa City, IA, 52242, USA
| | - Steven M. Varga
- Holden Comprehensive Cancer Center, University of Iowa, 200 Hawkins Dr, Iowa City, IA, 52242, USA,Interdisciplinary Graduate Program in Immunology, University of Iowa, 200 Hawkins Dr, Iowa City, IA, 52242, USA,Department of Microbiology and Immunology, University of Iowa, 51 Newton Rd., Iowa City, IA, 52242, USA,Department of Pathology, University of Iowa, 200 Hawkins Dr, Iowa City, IA, 52242, USA
| | - Kellie L. Bodeker
- Holden Comprehensive Cancer Center, University of Iowa, 200 Hawkins Dr, Iowa City, IA, 52242, USA,Department of Radiation Oncology, Free Radical and Radiation Biology Program, University of Iowa, 200 Hawkins Dr, Iowa City, IA, 52242, USA
| | - Aaron D. Bossler
- Department of Pathology, University of Iowa, 200 Hawkins Dr, Iowa City, IA, 52242, USA
| | - Andrew M. Bellizzi
- Holden Comprehensive Cancer Center, University of Iowa, 200 Hawkins Dr, Iowa City, IA, 52242, USA,Department of Pathology, University of Iowa, 200 Hawkins Dr, Iowa City, IA, 52242, USA
| | - Jun Zhang
- Department of Internal Medicine, University of Iowa, 200 Hawkins Dr, Iowa City, IA, 52242, USA,Holden Comprehensive Cancer Center, University of Iowa, 200 Hawkins Dr, Iowa City, IA, 52242, USA
| | - Varun Monga
- Department of Internal Medicine, University of Iowa, 200 Hawkins Dr, Iowa City, IA, 52242, USA,Holden Comprehensive Cancer Center, University of Iowa, 200 Hawkins Dr, Iowa City, IA, 52242, USA
| | - Hariharasudan Mani
- Department of Internal Medicine, University of Iowa, 200 Hawkins Dr, Iowa City, IA, 52242, USA
| | - Marina Ivanovic
- Holden Comprehensive Cancer Center, University of Iowa, 200 Hawkins Dr, Iowa City, IA, 52242, USA,Department of Pathology, University of Iowa, 200 Hawkins Dr, Iowa City, IA, 52242, USA
| | - Brian J. Smith
- Holden Comprehensive Cancer Center, University of Iowa, 200 Hawkins Dr, Iowa City, IA, 52242, USA,Department of Biostatistics, College of Public Health, University of Iowa, 145 N. Riverside Dr, Iowa City, IA, 52242, USA
| | - Margaret M. Byrne
- Department of Internal Medicine, University of Iowa, 200 Hawkins Dr, Iowa City, IA, 52242, USA,Holden Comprehensive Cancer Center, University of Iowa, 200 Hawkins Dr, Iowa City, IA, 52242, USA
| | - William Zeitler
- Department of Internal Medicine, University of Iowa, 200 Hawkins Dr, Iowa City, IA, 52242, USA,Holden Comprehensive Cancer Center, University of Iowa, 200 Hawkins Dr, Iowa City, IA, 52242, USA
| | - Brett A. Wagner
- Holden Comprehensive Cancer Center, University of Iowa, 200 Hawkins Dr, Iowa City, IA, 52242, USA,Department of Radiation Oncology, Free Radical and Radiation Biology Program, University of Iowa, 200 Hawkins Dr, Iowa City, IA, 52242, USA
| | - Garry R. Buettner
- Holden Comprehensive Cancer Center, University of Iowa, 200 Hawkins Dr, Iowa City, IA, 52242, USA,Department of Radiation Oncology, Free Radical and Radiation Biology Program, University of Iowa, 200 Hawkins Dr, Iowa City, IA, 52242, USA
| | - Joseph J. Cullen
- Holden Comprehensive Cancer Center, University of Iowa, 200 Hawkins Dr, Iowa City, IA, 52242, USA,Department of Radiation Oncology, Free Radical and Radiation Biology Program, University of Iowa, 200 Hawkins Dr, Iowa City, IA, 52242, USA
| | - John M. Buatti
- Holden Comprehensive Cancer Center, University of Iowa, 200 Hawkins Dr, Iowa City, IA, 52242, USA,Department of Radiation Oncology, Free Radical and Radiation Biology Program, University of Iowa, 200 Hawkins Dr, Iowa City, IA, 52242, USA
| | - Douglas R. Spitz
- Holden Comprehensive Cancer Center, University of Iowa, 200 Hawkins Dr, Iowa City, IA, 52242, USA,Department of Radiation Oncology, Free Radical and Radiation Biology Program, University of Iowa, 200 Hawkins Dr, Iowa City, IA, 52242, USA
| | - Bryan G. Allen
- Holden Comprehensive Cancer Center, University of Iowa, 200 Hawkins Dr, Iowa City, IA, 52242, USA,Department of Radiation Oncology, Free Radical and Radiation Biology Program, University of Iowa, 200 Hawkins Dr, Iowa City, IA, 52242, USA
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22
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Dunkerley DAP, Hyer DE, Snyder JE, St-Aubin JJ, Anderson CM, Caster JM, Smith MC, Buatti JM, Yaddanapudi S. Clinical Implementational and Site-Specific Workflows for a 1.5T MR-Linac. J Clin Med 2022; 11:jcm11061662. [PMID: 35329988 PMCID: PMC8954784 DOI: 10.3390/jcm11061662] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 03/11/2022] [Accepted: 03/15/2022] [Indexed: 01/27/2023] Open
Abstract
MR-guided adaptive radiotherapy (MRgART) provides opportunities to benefit patients through enhanced use of advanced imaging during treatment for many patients with various cancer treatment sites. This novel technology presents many new challenges which vary based on anatomic treatment location, technique, and potential changes of both tumor and normal tissue during treatment. When introducing new treatment sites, considerations regarding appropriate patient selection, treatment planning, immobilization, and plan-adaption criteria must be thoroughly explored to ensure adequate treatments are performed. This paper presents an institution’s experience in developing a MRgART program for a 1.5T MR-linac for the first 234 patients. The paper suggests practical treatment workflows and considerations for treating with MRgART at different anatomical sites, including imaging guidelines, patient immobilization, adaptive workflows, and utilization of bolus.
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23
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Xiong X, Smith BJ, Graves SA, Sunderland JJ, Graham MM, Gross BA, Buatti JM, Beichel RR. Quantification of uptake in pelvis F-18 FLT PET-CT images using a 3D localization and segmentation CNN. Med Phys 2022; 49:1585-1598. [PMID: 34982836 PMCID: PMC9447843 DOI: 10.1002/mp.15440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 12/01/2021] [Accepted: 12/02/2021] [Indexed: 11/12/2022] Open
Abstract
PURPOSE The purpose of this work was to develop and validate a deep convolutional neural network (CNN) approach for the automated pelvis segmentation in computed tomography (CT) scans to enable the quantification of active pelvic bone marrow by means of Fluorothymidine F-18 (FLT) tracer uptake measurement in positron emission tomography (PET) scans. This quantification is a critical step in calculating bone marrow dose for radiopharmaceutical therapy clinical applications as well as external beam radiation doses. METHODS An approach for the combined localization and segmentation of the pelvis in CT volumes of varying sizes, ranging from full-body to pelvis CT scans, was developed that utilizes a novel CNN architecture in combination with a random sampling strategy. The method was validated on 34 planning CT scans and 106 full-body FLT PET-CT scans using a cross-validation strategy. Specifically, two different training and CNN application options were studied, quantitatively assessed, and statistically compared. RESULTS The proposed method was able to successfully locate and segment the pelvis in all test cases. On all data sets, an average Dice coefficient of 0.9396 ± $\pm$ 0.0182 or better was achieved. The relative tracer uptake measurement error ranged between 0.065% and 0.204%. The proposed approach is time-efficient and shows a reduction in runtime of up to 95% compared to a standard U-Net-based approach without a localization component. CONCLUSIONS The proposed method enables the efficient calculation of FLT uptake in the pelvis. Thus, it represents a valuable tool to facilitate bone marrow preserving adaptive radiation therapy and radiopharmaceutical dose calculation. Furthermore, the method can be adapted to process other bone structures as well as organs.
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Affiliation(s)
- Xiaofan Xiong
- Department of Biomedical Engineering, The University of Iowa, Iowa City, IA 52242
| | - Brian J. Smith
- Department of Biostatistics, The University of Iowa, Iowa City, IA 52242
| | - Stephen A. Graves
- Department of Radiology, The University of Iowa, Iowa City, IA 52242
| | | | - Michael M. Graham
- Department of Radiology, The University of Iowa, Iowa City, IA 52242
| | - Brandie A. Gross
- Department of Radiation Oncology, University of Iowa Hospitals and Clinics, Iowa City, IA 52242
| | - John M. Buatti
- Department of Radiation Oncology, University of Iowa Hospitals and Clinics, Iowa City, IA 52242
| | - Reinhard R. Beichel
- Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA 52242
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24
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Kozak MM, Crompton D, Gross BA, Harshman L, Dickens D, Snyder J, Shepard A, St-Aubin J, Dunkerley D, Hyer D, Buatti JM. Initial clinical applications treating pediatric and adolescent patients using MR-guided radiotherapy. Front Oncol 2022; 12:962926. [PMID: 36419881 PMCID: PMC9676495 DOI: 10.3389/fonc.2022.962926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 09/21/2022] [Indexed: 11/09/2022] Open
Abstract
Purpose To demonstrate the clinical applications and feasibility of online adaptive magnetic resonance image guided radiotherapy (MRgRT) in the pediatric, adolescent and young adult (AYA) population. Methods This is a retrospective case series of patients enrolled onto a prospective study. All pediatric (age < 18) and AYA patients (age< 30), treated on the Elekta Unity MR linear accelerator (MRL) from 2019 to 2021 were enrolled onto a prospective registry. Rationale for MRgRT included improved visualization of and alignment to the primary tumor, re-irradiation in a critical area, ability to use smaller margins, and need for daily adaptive replanning to minimize dose to adjacent critical structures. Step-and-shoot intensity-modulated radiation treatment (IMRT) plans were generated for all Unity patients with a dose grid of 3 mm and a statistical uncertainty of < 1% per plan. Results A total of 15 pediatric and AYA patients have been treated with median age of 13 years (range: 6 mos - 27 yrs). Seven patients were <10 yo. The clinical applications of MRgRT included Wilms tumor with unresectable IVC thrombus (n=1), Ewing sarcoma (primary and metastatic, n=3), recurrent diffuse intrinsic pontine glioma (DIPG, n=2), nasopharyngeal carcinoma (n=1), clival chordoma (n=1), primitive neuroectodermal tumor of the pancreas (n=1), recurrent gluteo-sacral germ cell tumor (n=1), C-spine ependymoma (n=1), and posterior fossa ependymoma (n=1). Two children required general anesthesia. One AYA patient could not complete the MRgRT course due to tumor-related pain exacerbated by longer treatment times. Two AYA patients experienced anxiety related to treatment on the MRL, one of which required daily Ativan. No patient experienced treatment interruptions or unexpected toxicity. Conclusion MRgRT was well-tolerated by pediatric and AYA patients. There was no increased use of anesthesia outside of our usual practice. Dosimetric advantages were seen for patients with tumors in critical locations such as adjacent to or involving optic structures, stomach, kidney, bowel, and heart.
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Affiliation(s)
- Margaret M Kozak
- Department of Radiation Oncology, University of Iowa Hospitals and Clinics, Iowa, IA, United States
| | - David Crompton
- Department of Radiation Oncology, University of Iowa Hospitals and Clinics, Iowa, IA, United States
| | - Brandie A Gross
- Department of Radiation Oncology, University of Iowa Hospitals and Clinics, Iowa, IA, United States
| | - Lyndsay Harshman
- Department of Pediatrics, the University of Iowa Hospitals and Clinics, Iowa, IA, United States
| | - David Dickens
- Department of Hematology/Oncology, University of Iowa Hospitals and Clinics, Iowa, IA, United States
| | - Jeffrey Snyder
- Department of Radiation Oncology, University of Iowa Hospitals and Clinics, Iowa, IA, United States
| | - Andrew Shepard
- Department of Radiation Oncology, University of Iowa Hospitals and Clinics, Iowa, IA, United States
| | - Joël St-Aubin
- Department of Radiation Oncology, University of Iowa Hospitals and Clinics, Iowa, IA, United States
| | - David Dunkerley
- Department of Radiation Oncology, University of Iowa Hospitals and Clinics, Iowa, IA, United States
| | - Daniel Hyer
- Department of Radiation Oncology, University of Iowa Hospitals and Clinics, Iowa, IA, United States
| | - John M Buatti
- Department of Radiation Oncology, University of Iowa Hospitals and Clinics, Iowa, IA, United States
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25
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Plichta KA, Graves SA, Buatti JM. Prostate-Specific Membrane Antigen (PSMA) Theranostics for Treatment of Oligometastatic Prostate Cancer. Int J Mol Sci 2021; 22:12095. [PMID: 34829977 PMCID: PMC8621856 DOI: 10.3390/ijms222212095] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 11/01/2021] [Accepted: 11/06/2021] [Indexed: 11/17/2022] Open
Abstract
Theranostics, a combination of therapy and diagnostics, is a field of personalized medicine involving the use of the same or similar radiopharmaceutical agents for the diagnosis and treatment of patients. Prostate-specific membrane antigen (PSMA) is a promising theranostic target for the treatment of prostate cancers. Diagnostic PSMA radiopharmaceuticals are currently used for staging and diagnosis of prostate cancers, and imaging can predict response to therapeutic PSMA radiopharmaceuticals. While mainly used in the setting of metastatic, castrate-resistant disease, clinical trials are investigating the use of PSMA-based therapy at earlier stages, including in hormone-sensitive or hormone-naïve prostate cancers, and in oligometastatic prostate cancers. This review explores the use of PSMA as a theranostic target and investigates the potential use of PSMA in earlier stage disease, including hormone-sensitive metastatic prostate cancer, and oligometastatic prostate cancer.
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Affiliation(s)
- Kristin A. Plichta
- Department of Radiation Oncology, University of Iowa, LL-W PFP, 200 Hawkins Dr., Iowa City, IA 52242, USA; (S.A.G.); (J.M.B.)
| | - Stephen A. Graves
- Department of Radiation Oncology, University of Iowa, LL-W PFP, 200 Hawkins Dr., Iowa City, IA 52242, USA; (S.A.G.); (J.M.B.)
- Department of Radiology, University of Iowa, 3883 JPP, 200 Hawkins Dr., Iowa City, IA 52242, USA
| | - John M. Buatti
- Department of Radiation Oncology, University of Iowa, LL-W PFP, 200 Hawkins Dr., Iowa City, IA 52242, USA; (S.A.G.); (J.M.B.)
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26
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Seyedin SN, Watkins JM, Mayo Z, Snow AN, Laszewski M, Russo JK, Mott SL, Tracy CR, Smith MC, Buatti JM, Caster JM. A Recursive Partitioning Analysis Demonstrating Risk Subsets for 8-Year Biochemical Relapse After Margin-Positive Radical Prostatectomy Without Adjuvant Hormone or Radiation Therapy. Adv Radiat Oncol 2021; 6:100778. [PMID: 34934861 PMCID: PMC8655410 DOI: 10.1016/j.adro.2021.100778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Accepted: 08/09/2021] [Indexed: 11/23/2022] Open
Abstract
Purpose The cohort of patients with locally advanced prostate cancer (PC) and positive surgical margin(s) at radical prostatectomy (RP) who would benefit from salvage or adjuvant treatment is unclear. This study examines the risk of prostate-specific antigen (PSA) relapse in a large population of men with PC after margin-positive RP. Methods and Materials Using a multi-institutional database, patients with clinically localized PC who underwent RP between 2002 and 2010 with recorded follow-up PSA were retrospectively selected. Patients were excluded for pathologic seminal vesicle or lymph node involvement, metastatic disease, pre-RP PSA ≥ 30, or adjuvant (nonsalvage) radiation therapy or hormone therapy. The primary endpoint was biochemical relapse free survival (bRFS), where PSA failure was defined as PSA > 0.10 ng/mL and rising, or at salvage intervention. The Kaplan-Meier method was employed for bRFS estimates; recursive partitioning analysis using cumulative or single maximal margin extent (ME) and Gleason grade (GG) at RP was applied to identify variables associated with bRFS. Results At median follow-up of 105 months, 210 patients with positive margins at RP were eligible for analysis, and 89 had experienced PSA relapse. Median age was 61 years (range, 43-76), and median pre-RP PSA 5.8 ng/mL (1.6-26.0). Recursive partitioning analysis yielded 5 discrete risk groups, with the lowest risk group (GG1, ≤ 2 mm ME) demonstrating a bRFS of 92% at 8 years compared with the highest risk group (GG3-5, ≥ 3 mm ME) of 11%. Conclusions This retrospective study suggests that it may be possible to risk-stratify patients undergoing margin-positive RP using commonly acquired clinical and pathologic variables. Patients with low-grade tumors and minimally involved margins have a very low recurrence risk and may be able to forego postprostatectomy radiation. Meanwhile, those with higher grade and greater involvement could benefit from adjuvant or early salvage radiation therapy.
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27
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Seaman SC, Zanaty M, Crompton D, Piscopo A, Ankrah NK, Buatti JM, Greenlee JDW, Howard MA. Case series of sphenoid wing meningioma - What is a maximal safe resection? Neurochirurgie 2021; 67:547-555. [PMID: 34051247 DOI: 10.1016/j.neuchi.2021.05.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 04/05/2021] [Accepted: 05/09/2021] [Indexed: 11/28/2022]
Abstract
BACKGROUND Sphenoid wing meningiomas are a challenging surgical disease with relatively high perioperative morbidity. Most studies to date have focused on resection strategies as it relates to disease recurrence. Few have examined the optimal strategy as it relates to overall patient survival. We retrospectively reviewed our case series and evaluated extent of resection and perioperative stroke as it relates to all cause and disease-specific survival. PATIENTS/METHODS Ninety-four patients were included in the study. Demographics, clinical features, operative features and clinical course, and time to mortality evaluation were collected. Extent of resection (EOR) was defined as gross total (GTR, 100%), near total (NTR, ≥ 95%), and subtotal (STR,<95%). RESULTS The overall mean EOR was 94.5% with 70.2% of cases achieving GTR, 12.8% achieved NTR, and 17% achieved STR. Postoperative stroke only occurred with GTR or NTR (p=0.041). Age alone was significant on Cox regression analysis for all cause mortality (p=0.042, HR 1.054 [95% CI 1.002 - 1.109]). Postoperative stroke was associated with worse disease-specific mortality (p=0.046, HR 23.337 [95% CI 1.052 - 517.782) with no impact from extent of resection (p=0.258). CONCLUSIONS Although maximizing resection and minimizing recurrence is ideal, GTR or NTR confer a significantly higher stroke risk. Most patients do not die from their meningioma, as all cause mortality was associated only with age. However, perioperative stroke conferred decreased survival throughout follow up. This series demonstrates that an overly aggressive surgical philosophy negatively impacted disease specific survival.
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Affiliation(s)
- Scott C Seaman
- Department of Neurosurgery, University of Iowa Hospitals and Clinics, 200 Hawkins Drive, 52242 Iowa City, Iowa, USA.
| | - Mario Zanaty
- Department of Neurosurgery, University of Iowa Hospitals and Clinics, 200 Hawkins Drive, 52242 Iowa City, Iowa, USA
| | - David Crompton
- University of Iowa Carver College of Medicine, 200 Hawkins Drive, 52242 Iowa City, Iowa, USA
| | - Anthony Piscopo
- University of Iowa Carver College of Medicine, 200 Hawkins Drive, 52242 Iowa City, Iowa, USA
| | - Nii-Kwanche Ankrah
- Department of Neurosurgery, University of Iowa Hospitals and Clinics, 200 Hawkins Drive, 52242 Iowa City, Iowa, USA
| | - John M Buatti
- Department of Radiation Oncology, 200 Hawkins Drive, 52242 Iowa City, Iowa, USA
| | - Jeremy D W Greenlee
- Department of Neurosurgery, University of Iowa Hospitals and Clinics, 200 Hawkins Drive, 52242 Iowa City, Iowa, USA
| | - Matthew A Howard
- Department of Neurosurgery, University of Iowa Hospitals and Clinics, 200 Hawkins Drive, 52242 Iowa City, Iowa, USA.
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Blitzer GC, Parekh AD, Chen S, Taparra K, Kahn JM, Fields EC, Stahl JM, Rosenberg SA, Buatti JM, Laucis AM, Wang Y, Mayhew DL, McDonald AM, Harari PM, Brower JV. Why an Increasing Number of Unmatched Residency Positions in Radiation Oncology? A Survey of Fourth-Year Medical Students. Adv Radiat Oncol 2021; 6:100743. [PMID: 34466713 PMCID: PMC8385400 DOI: 10.1016/j.adro.2021.100743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 05/05/2021] [Accepted: 06/09/2021] [Indexed: 11/29/2022] Open
Abstract
Purpose The number of US fourth-year medical students applying to radiation oncology has decreased during the past few years. We conducted a survey of fourth-year medical students to examine factors that may be influencing the decision to pursue radiation oncology. Methods and Materials An anonymous online survey was sent to medical students at 9 participating US medical schools. Results A total of 232 medical students completed the survey. Of the 153 students who stated they were never interested in radiation oncology, 77 (50%) reported never having been exposed to the specialty as their reason for not pursuing radiation oncology. The job market was the most commonly cited factor among students who said they were once interested in but ultimately chose not to pursue radiation oncology. Conversely, the recent low pass rates for board examinations and a perception of a lack of diversity within radiation oncology had the least influence. Conclusions Despite discussion of potential measures to address this disquieting trend, there have been minimal formal attempts to characterize and address potential causes of a decreasing interest in radiation oncology. This study's data are consistent with previous research regarding the trend of decreased medical student interest in radiation oncology and may be used as part of ongoing introspective assessment to inform future change within radiation oncology.
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Affiliation(s)
- Grace C Blitzer
- Department of Human Oncology, University of Wisconsin, Madison, Wisconsin
| | - Akash D Parekh
- Department of Radiation Oncology, University of Florida, Gainesville, Florida
| | - Shuai Chen
- Department of Public Health Sciences, University of California-Davis, Sacramento, California
| | - Kekoa Taparra
- Gundersen Lutheran Health System, La Crosse, Wisconsin
| | - Jenna M Kahn
- Department of Radiation Oncology, Oregon Health and Science University, Portland, Oregon
| | - Emma C Fields
- Department of Radiation Oncology, Virginia Commonwealth University, Richmond, Virginia
| | - John M Stahl
- Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, Alabama
| | | | - John M Buatti
- Department of Radiation Oncology, University of Iowa, Iowa City, Iowa
| | - Anna M Laucis
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | - Yichu Wang
- Department of Mathematical Sciences, University of Liverpool, Liverpool, United Kingdom
| | - David L Mayhew
- Department of Radiation Oncology, Tufts Medical Center, Boston, Massachusetts.,Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Andrew M McDonald
- Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, Alabama
| | - Paul M Harari
- Department of Human Oncology, University of Wisconsin, Madison, Wisconsin
| | - Jeffrey V Brower
- Department of Human Oncology, University of Wisconsin, Madison, Wisconsin.,Radiation Oncology Associates-New England, Manchester, New Hampshire
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29
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Mapuskar KA, Steinbach EJ, Zaher A, Riley DP, Beardsley RA, Keene JL, Holmlund JT, Anderson CM, Zepeda-Orozco D, Buatti JM, Spitz DR, Allen BG. Mitochondrial Superoxide Dismutase in Cisplatin-Induced Kidney Injury. Antioxidants (Basel) 2021; 10:antiox10091329. [PMID: 34572961 PMCID: PMC8469643 DOI: 10.3390/antiox10091329] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 08/16/2021] [Accepted: 08/18/2021] [Indexed: 02/06/2023] Open
Abstract
Cisplatin is a chemotherapy agent commonly used to treat a wide variety of cancers. Despite the potential for both severe acute and chronic side effects, it remains a preferred therapeutic option for many malignancies due to its potent anti-tumor activity. Common cisplatin-associated side-effects include acute kidney injury (AKI) and chronic kidney disease (CKD). These renal injuries may cause delays and potentially cessation of cisplatin therapy and have long-term effects on renal function reserve. Thus, developing mechanism-based interventional strategies that minimize cisplatin-associated kidney injury without reducing efficacy would be of great benefit. In addition to its action of cross-linking DNA, cisplatin has been shown to affect mitochondrial metabolism, resulting in mitochondrially derived reactive oxygen species (ROS). Increased ROS formation in renal proximal convoluted tubule cells is associated with cisplatin-induced AKI and CKD. We review the mechanisms by which cisplatin may induce AKI and CKD and discuss the potential of mitochondrial superoxide dismutase mimetics to prevent platinum-associated nephrotoxicity.
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Affiliation(s)
- Kranti A. Mapuskar
- Free Radical and Radiation Biology Program, Department of Radiation Oncology, University of Iowa, Iowa City, IA 52242, USA; (K.A.M.); (E.J.S.); (C.M.A.); (J.M.B.); (D.R.S.)
| | - Emily J. Steinbach
- Free Radical and Radiation Biology Program, Department of Radiation Oncology, University of Iowa, Iowa City, IA 52242, USA; (K.A.M.); (E.J.S.); (C.M.A.); (J.M.B.); (D.R.S.)
| | - Amira Zaher
- Biomedical Science Program, Holden Comprehensive Cancer Center, University of Iowa, Iowa City, IA 52242, USA;
| | - Dennis P. Riley
- Galera Therapeutics, Inc., Malvern, PA 19355, USA; (D.P.R.); (R.A.B.); (J.L.K.); (J.T.H.)
| | - Robert A. Beardsley
- Galera Therapeutics, Inc., Malvern, PA 19355, USA; (D.P.R.); (R.A.B.); (J.L.K.); (J.T.H.)
| | - Jeffery L. Keene
- Galera Therapeutics, Inc., Malvern, PA 19355, USA; (D.P.R.); (R.A.B.); (J.L.K.); (J.T.H.)
| | - Jon T. Holmlund
- Galera Therapeutics, Inc., Malvern, PA 19355, USA; (D.P.R.); (R.A.B.); (J.L.K.); (J.T.H.)
| | - Carryn M. Anderson
- Free Radical and Radiation Biology Program, Department of Radiation Oncology, University of Iowa, Iowa City, IA 52242, USA; (K.A.M.); (E.J.S.); (C.M.A.); (J.M.B.); (D.R.S.)
| | - Diana Zepeda-Orozco
- Center for Clinical and Translational Research, The Abigail Wexner Research Institute at Nationwide Children’s Hospital, Columbus, OH 43205, USA;
- College of Medicine, The Ohio State University, Columbus, OH 43210, USA
- Division of Nephrology, Department of Pediatrics, Nationwide Children’s Hospital, Columbus, OH 43205, USA
| | - John M. Buatti
- Free Radical and Radiation Biology Program, Department of Radiation Oncology, University of Iowa, Iowa City, IA 52242, USA; (K.A.M.); (E.J.S.); (C.M.A.); (J.M.B.); (D.R.S.)
| | - Douglas R. Spitz
- Free Radical and Radiation Biology Program, Department of Radiation Oncology, University of Iowa, Iowa City, IA 52242, USA; (K.A.M.); (E.J.S.); (C.M.A.); (J.M.B.); (D.R.S.)
| | - Bryan G. Allen
- Free Radical and Radiation Biology Program, Department of Radiation Oncology, University of Iowa, Iowa City, IA 52242, USA; (K.A.M.); (E.J.S.); (C.M.A.); (J.M.B.); (D.R.S.)
- Correspondence: ; Tel.: +1-319-335-8019; Fax: +1-319-335-8039
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Seravalli E, Kroon PS, Buatti JM, Hall MD, Mandeville HC, Marcus KJ, Onal C, Ozyar E, Paulino AC, Paulsen F, Saunders D, Tsang DS, Wolden SL, Janssens GO. The potential role of MR-guided adaptive radiotherapy in pediatric oncology: Results from a SIOPE-COG survey. Clin Transl Radiat Oncol 2021; 29:71-78. [PMID: 34159265 PMCID: PMC8202186 DOI: 10.1016/j.ctro.2021.05.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 05/22/2021] [Accepted: 05/24/2021] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND AND PURPOSE Magnetic resonance guided radiotherapy (MRgRT) has been successfully implemented for several routine clinical applications in adult patients. The purpose of this study is to map the potential benefit of MRgRT on toxicity reduction and outcome in pediatric patients treated with curative intent for primary and metastatic sites. MATERIALS AND METHODS Between May and August 2020, a survey was distributed among SIOPE- and COG-affiliated radiotherapy departments, treating at least 25 pediatrics patients annually and being (candidate) users of a MRgRT system. The survey consisted of a table with 45 rows (clinical scenarios for primary (n = 28) and metastatic (n = 17) tumors) and 7 columns (toxicity reduction, outcome improvement, PTV margin reduction, target volume daily adaptation, online re-planning, intrafraction motion compensation and on-board functional imaging) and the option to answer by 'yes/no' . Afterwards, the Dutch national radiotherapy cohort was used to estimate the percentage of pediatric treatments that may benefit from MRgRT. RESULTS The survey was completed by 12/17 (71% response rate) institutions meeting the survey inclusion criteria. Responders indicated an 'expected benefit' from MRgRT for toxicity/outcome in 7% (for thoracic lymphomas and abdominal rhabdomyosarcomas)/0% and 18% (for mediastinal lymph nodes, lymph nodes located in the liver/splenic hilum, and liver metastases)/0% of the considered scenarios for the primary and metastatic tumor sites, respectively, and a 'possible benefit' was estimated in 64%/46% and 47%/59% of the scenarios. When translating the survey outcome into a clinical perspective a toxicity/outcome benefit, either expected or possible, was anticipated for 55%/24% of primary sites and 62%/38% of the metastatic sites. CONCLUSION Although the benefit of MRgRT in pediatric radiation oncology is estimated to be modest, the potential role for reducing toxicity and improving clinical outcomes warrants further investigation. This fits best within the context of prospective studies or registration trials.
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Affiliation(s)
- Enrica Seravalli
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Petra S. Kroon
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - John M. Buatti
- Departments of Radiation Oncology, University of Iowa, Iowa City, USA
| | - Matthew D. Hall
- Department of Radiation Oncology, Miami Cancer Institute, Miami, FL, USA
| | - Henry C. Mandeville
- Department of Radiotherapy, The Royal Marsden Hospital and Institute of Cancer Research, Sutton, United Kingdom
| | - Karen J. Marcus
- Dana-Farber/Boston Children’s Cancer and Blood Disorders Center, Boston, MA, USA
| | - Cem Onal
- Department of Radiation Oncology, Baskent University, Ankara, Turkey
| | - Enis Ozyar
- Department of Radiation Oncology, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey
| | - Arnold C. Paulino
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, USA
| | - Frank Paulsen
- Department of Radiation Oncology, University Hospital Tuebingen, Tuebingen, Germany
| | | | - Derek S. Tsang
- Radiation Medicine Program, University Health Network – Princess Margaret Cancer Centre, Toronto, Canada
| | - Suzanne L. Wolden
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, NY, USA
| | - Geert O. Janssens
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
- Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands
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Furqan M, Abu-Hejleh T, Bodeker KL, Pietrok LM, Hartwig SM, Tremblay MM, Fosdick MG, Houtman J, Varga S, Pulliam CF, Petronek M, Fath MA, Mott SL, Bossler AD, Bellizzi AM, Zhang J, Mani H, Monga V, Smith BJ, Cullen J, Wagner BA, Buettner GR, Buatti JM, Spitz DR, Allen BG. Abstract CT164: Pharmacological ascorbate enhances platinum-based chemotherapy responses in metastatic non-small cell lung cancer (NSCLC): A phase II clinical trial. Cancer Res 2021. [DOI: 10.1158/1538-7445.am2021-ct164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
PURPOSE: First line treatment with platinum-based chemotherapy with or without immunotherapy improves survival in metastatic non-small cell lung cancer (NSCLC). Preclinical studies suggest that pharmacological ascorbate (P-AscH-) enhances tumor response to platinum therapy. Hence, we conducted a single-arm phase II study to evaluate the efficacy of P-AscH- in combination with platinum-doublet chemotherapy in patients with advanced stage NSCLC (NCT02420314). METHODS: Chemotherapy naïve advanced stage NSCLC patients with an ECOG PS of 0-2 were enrolled to receive 4-cycles of carboplatin (AUC 6) and paclitaxel (200 mg/m2) every 3 weeks (wks). Ascorbate (75 g) infusions were given twice a wk for 12-wks. The primary endpoint was to assess tumor objective response per RECIST v1.1. The trial was conducted as an optimal Simon two-stage design. After initial therapy, patients could receive maintenance or consolidation treatment. Secondary endpoints were to evaluate tolerability, progression-free survival (PFS) and overall survival (OS). Serum cytokines and chemokines were measured at baseline, C2d1, and C4d21± 7d. RESULTS: Forty subjects were enrolled. The study met its primary endpoint with 38 efficacy evaluable subjects. The objective response rate was 34.2%. All were confirmed partial responses (cPR). Disease control rate was 84.2% (stable disease + cPR). Median duration of follow up was 11.7 months (mo), mPFS was 5.7 mo (95% CI:4.2-6.7), and mOS was 12.5 mo (95% CI:7.5-21.4). Treatment-related adverse events (TRAE) included one grade 5 (neutropenic fever) and five grade 4 (cytopenia) events. These events were not attributed to P-AscH-. Common (≥5%) grade 3 TRAE included transient hypertension (27.5%), lymphopenia (22.5%), fatigue (7.5%), anemia (7.5%) and hypokalemia (5%). Cytokine and chemokines data suggest that protocol regimen elicited an immune response with multiple distinct cytokine signatures. Immunophenotyping of peripheral blood mononuclear cells (n=7) demonstrated a mean fold increase in effector CD8 T cells of 4.9 in patients with PFS ≥ 6 mo compare to 1.6 in patients with PFS < 6 mo. Assessments of serum iron profile and somatic alterations in KRAS, KEAP1, NFE2L2 and STK11 genes are underway. CONCLUSIONS: This phase II trial met the primary objective of improving the tumor response rate in advanced stage NSCLC by adding P-AscH- to platinum-based chemotherapy. P-AscH- appears to alter the host immune response. These promising findings warrant further investigation.
Citation Format: Muhammad Furqan, Taher Abu-Hejleh, Kellie L. Bodeker, Laura M. Pietrok, Stacey M. Hartwig, Mikaela M. Tremblay, Micaela G. Fosdick, Jon Houtman, Steven Varga, Casey F. Pulliam, Michael Petronek, Melissa A. Fath, Sarah L. Mott, Aaron D. Bossler, Andrew M. Bellizzi, Jun Zhang, Hariharasudan Mani, Varun Monga, Brian J. Smith, Joseph Cullen, Brett A. Wagner, Garry R. Buettner, John M. Buatti, Douglas R. Spitz, Bryan G. Allen. Pharmacological ascorbate enhances platinum-based chemotherapy responses in metastatic non-small cell lung cancer (NSCLC): A phase II clinical trial [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr CT164.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Jun Zhang
- 3University of Kansas Medical Center, Kansas City, KS
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Smith BJ, Buatti JM, Bauer C, Ulrich EJ, Ahmadvand P, Budzevich MM, Gillies RJ, Goldgof D, Grkovski M, Hamarneh G, Kinahan PE, Muzi JP, Muzi M, Laymon CM, Mountz JM, Nehmeh S, Oborski MJ, Zhao B, Sunderland JJ, Beichel RR. Multisite Technical and Clinical Performance Evaluation of Quantitative Imaging Biomarkers from 3D FDG PET Segmentations of Head and Neck Cancer Images. ACTA ACUST UNITED AC 2021; 6:65-76. [PMID: 32548282 PMCID: PMC7289247 DOI: 10.18383/j.tom.2020.00004] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Quantitative imaging biomarkers (QIBs) provide medical image-derived intensity, texture, shape, and size features that may help characterize cancerous tumors and predict clinical outcomes. Successful clinical translation of QIBs depends on the robustness of their measurements. Biomarkers derived from positron emission tomography images are prone to measurement errors owing to differences in image processing factors such as the tumor segmentation method used to define volumes of interest over which to calculate QIBs. We illustrate a new Bayesian statistical approach to characterize the robustness of QIBs to different processing factors. Study data consist of 22 QIBs measured on 47 head and neck tumors in 10 positron emission tomography/computed tomography scans segmented manually and with semiautomated methods used by 7 institutional members of the NCI Quantitative Imaging Network. QIB performance is estimated and compared across institutions with respect to measurement errors and power to recover statistical associations with clinical outcomes. Analysis findings summarize the performance impact of different segmentation methods used by Quantitative Imaging Network members. Robustness of some advanced biomarkers was found to be similar to conventional markers, such as maximum standardized uptake value. Such similarities support current pursuits to better characterize disease and predict outcomes by developing QIBs that use more imaging information and are robust to different processing factors. Nevertheless, to ensure reproducibility of QIB measurements and measures of association with clinical outcomes, errors owing to segmentation methods need to be reduced.
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Affiliation(s)
| | | | | | - Ethan J Ulrich
- Electrical and Computer Engineering.,Biomedical Engineering, The University of Iowa, Iowa City, IA
| | - Payam Ahmadvand
- School of Computing Science, Simon Fraser University, Burnaby, Canada
| | - Mikalai M Budzevich
- H. Lee Moffitt Cancer Center & Research Institute, Department of Cancer Physiology, FL
| | - Robert J Gillies
- H. Lee Moffitt Cancer Center & Research Institute, Department of Cancer Physiology, FL
| | - Dmitry Goldgof
- Department of Computer Science and Engineering, University of South Florida, FL
| | - Milan Grkovski
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Ghassan Hamarneh
- School of Computing Science, Simon Fraser University, Burnaby, Canada
| | - Paul E Kinahan
- Department of Radiology, The University of Washington Medical Center, Seattle, WA
| | - John P Muzi
- Department of Radiology, The University of Washington Medical Center, Seattle, WA
| | - Mark Muzi
- Department of Radiology, The University of Washington Medical Center, Seattle, WA
| | - Charles M Laymon
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA.,Department of Radiology, University of Pittsburgh, Pittsburgh, PA
| | - James M Mountz
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA
| | - Sadek Nehmeh
- Department of Radiology, Weill Cornell Medical College, NY; and
| | - Matthew J Oborski
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA
| | - Binsheng Zhao
- Department of Radiology, Columbia University Medical Center, New York, NY
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Tamplin MR, Deng W, Garvin MK, Binkley EM, Hyer DE, Buatti JM, Ledolter J, Boldt HC, Kardon RH, Grumbach IM. Temporal Relationship Between Visual Field, Retinal and Microvascular Pathology Following 125I-Plaque Brachytherapy for Uveal Melanoma. Invest Ophthalmol Vis Sci 2021; 62:3. [PMID: 33393969 PMCID: PMC7794259 DOI: 10.1167/iovs.62.1.3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
Purpose To define the temporal relationship of vascular versus neuronal abnormalities in radiation retinopathy. Methods Twenty-five patients with uveal melanoma treated with brachytherapy and sixteen controls were tested. Functional outcome measures included visual acuity and threshold perimetry (HVF 10-2), while structural outcomes included retinal thickness by OCT and vascular measures by OCT angiography and digital fundus photography. The degree of structural abnormality was determined by intereye asymmetry compared with normal subject asymmetry. Diagnostic sensitivity and specificity of each measure were determined using receiver operating characteristic curves. The relationships between the outcome measures were quantified by Spearman correlation. The effect of time from brachytherapy on visual function, retinal layer thickness, and capillary density was also determined. Results Within the first 2 years of brachytherapy, outcome measures revealed visual field loss and microvascular abnormalities in 38% and 31% of subjects, respectively. After 2 years, they became more prevalent, increasing to 67% and 67%, respectively, as did retinal thinning (50%). Visual field loss, loss of capillary density, and inner retinal thickness were highly correlated with one another. Diagnostic sensitivity and specificity were highest for abnormalities in digital fundus photography, visual field loss within the central 10°, and decrease in vessel density. Conclusions Using quantitative approaches, radiation microvasculopathy and visual field defects were detected earlier than loss of inner retinal structure after brachytherapy. Strong correlations eventually developed between vascular pathology, change in retinal thickness, neuronal dysfunction, and radiation dose. Radiation-induced ischemia seems to be a primary early manifestation of radiation retinopathy preceding visual loss.
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Affiliation(s)
- Michelle R Tamplin
- Free Radical and Radiation Biology Program, Department of Radiation Oncology, University of Iowa, Iowa City, Iowa, United States
| | - Wenxiang Deng
- Iowa City VA Center for the Prevention and Treatment of Visual Loss, Iowa City, Iowa, United States.,Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa, United States
| | - Mona K Garvin
- Iowa City VA Center for the Prevention and Treatment of Visual Loss, Iowa City, Iowa, United States.,Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa, United States
| | - Elaine M Binkley
- Department of Ophthalmology and Visual Sciences, University of Iowa, Iowa City, Iowa, United States
| | - Daniel E Hyer
- Department of Radiation Oncology, University of Iowa, Iowa City, Iowa, United States
| | - John M Buatti
- Department of Radiation Oncology, University of Iowa, Iowa City, Iowa, United States
| | - Johannes Ledolter
- Iowa City VA Center for the Prevention and Treatment of Visual Loss, Iowa City, Iowa, United States.,Henry B. Tippie College of Business, University of Iowa, Iowa City, Iowa, United States
| | - H Culver Boldt
- Department of Ophthalmology and Visual Sciences, University of Iowa, Iowa City, Iowa, United States
| | - Randy H Kardon
- Iowa City VA Center for the Prevention and Treatment of Visual Loss, Iowa City, Iowa, United States.,Department of Ophthalmology and Visual Sciences, University of Iowa, Iowa City, Iowa, United States
| | - Isabella M Grumbach
- Free Radical and Radiation Biology Program, Department of Radiation Oncology, University of Iowa, Iowa City, Iowa, United States.,Iowa City VA Center for the Prevention and Treatment of Visual Loss, Iowa City, Iowa, United States.,Abboud Cardiovascular Research Center, Division of Cardiovascular Medicine, Department of Internal Medicine, Carver College of Medicine, University of Iowa, Iowa City, Iowa, United States
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Sperduto PW, Mesko S, Li J, Cagney D, Aizer A, Lin NU, Nesbit E, Kruser TJ, Chan J, Braunstein S, Lee J, Kirkpatrick JP, Breen W, Brown PD, Shi D, Shih HA, Soliman H, Sahgal A, Shanley R, Sperduto W, Lou E, Everett A, Boggs DH, Masucci L, Roberge D, Remick J, Plichta K, Buatti JM, Jain S, Gaspar LE, Wu CC, Wang TJC, Bryant J, Chuong M, Yu J, Chiang V, Nakano T, Aoyama H, Mehta MP. Estrogen/progesterone receptor and HER2 discordance between primary tumor and brain metastases in breast cancer and its effect on treatment and survival. Neuro Oncol 2021; 22:1359-1367. [PMID: 32034917 PMCID: PMC7523450 DOI: 10.1093/neuonc/noaa025] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Breast cancer treatment is based on estrogen receptors (ERs), progesterone receptors (PRs), and human epidermal growth factor receptor 2 (HER2). At the time of metastasis, receptor status can be discordant from that at initial diagnosis. The purpose of this study was to determine the incidence of discordance and its effect on survival and subsequent treatment in patients with breast cancer brain metastases (BCBM). METHODS A retrospective database of 316 patients who underwent craniotomy for BCBM between 2006 and 2017 was created. Discordance was considered present if the ER, PR, or HER2 status differed between the primary tumor and the BCBM. RESULTS The overall receptor discordance rate was 132/316 (42%), and the subtype discordance rate was 100/316 (32%). Hormone receptors (HR, either ER or PR) were gained in 40/160 (25%) patients with HR-negative primary tumors. HER2 was gained in 22/173 (13%) patients with HER2-negative primary tumors. Subsequent treatment was not adjusted for most patients who gained receptors-nonetheless, median survival (MS) improved but did not reach statistical significance (HR, 17-28 mo, P = 0.12; HER2, 15-19 mo, P = 0.39). MS for patients who lost receptors was worse (HR, 27-18 mo, P = 0.02; HER2, 30-18 mo, P = 0.08). CONCLUSIONS Receptor discordance between primary tumor and BCBM is common, adversely affects survival if receptors are lost, and represents a missed opportunity for use of effective treatments if receptors are gained. Receptor analysis of BCBM is indicated when clinically appropriate. Treatment should be adjusted accordingly. KEY POINTS 1. Receptor discordance alters subtype in 32% of BCBM patients.2. The frequency of receptor gain for HR and HER2 was 25% and 13%, respectively.3. If receptors are lost, survival suffers. If receptors are gained, consider targeted treatment.
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Affiliation(s)
- Paul W Sperduto
- Minneapolis Radiation Oncology and University of Minnesota Gamma Knife Center, Minneapolis, Minnesota, USA
| | - Shane Mesko
- The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Jing Li
- The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Daniel Cagney
- Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Ayal Aizer
- Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Nancy U Lin
- Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Eric Nesbit
- Northwestern University, Chicago, Illinois, USA
| | | | - Jason Chan
- University of California San Francisco, San Francisco, California, USA
| | - Steve Braunstein
- University of California San Francisco, San Francisco, California, USA
| | - Jessica Lee
- Duke University, Durham, North Carolina, USA
| | | | | | | | - Diana Shi
- Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Helen A Shih
- Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Hany Soliman
- Sunnybrook Odette Cancer Centre University of Toronto, Toronto, Canada
| | - Arjun Sahgal
- Sunnybrook Odette Cancer Centre University of Toronto, Toronto, Canada
| | - Ryan Shanley
- University of Minnesota, Minneapolis, Minnesota, USA
| | | | - Emil Lou
- University of Minnesota, Minneapolis, Minnesota, USA
| | - Ashlyn Everett
- University of Alabama Birmingham, Birmingham, Alabama, USA
| | | | - Laura Masucci
- Centre Hospitalier de l' Université de Montréal, Montreal, Canada
| | - David Roberge
- Centre Hospitalier de l' Université de Montréal, Montreal, Canada
| | - Jill Remick
- University of Maryland, Baltimore, Maryland, USA
| | | | | | - Supriya Jain
- University of Colorado Denver, Denver, Colorado, USA
| | | | | | | | | | | | - James Yu
- Yale University, New Haven, Connecticut, USA
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Petronek MS, Steinbach EJ, Kalen AL, Builta ZJ, Callaghan CM, Hyer DE, Spitz DR, Flynn RT, Buatti JM, Magnotta VA, Zepeda-Orozco D, St-Aubin JJ, Allen BG. Assessment of Gadobutrol Safety in Combination with Ionizing Radiation Using a Preclinical MRI-Guided Radiotherapy Model. Radiat Res 2021; 195:230-234. [PMID: 33347596 DOI: 10.1667/rade-20-00199.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Accepted: 11/20/2020] [Indexed: 11/03/2022]
Abstract
MR-linac technology enhances the precision of therapeutic radiation by clarifying the tumor-normal tissue interface and provides the potential for adaptive treatment planning. Accurate delineation of tumors on diagnostic magnetic resonance imaging (MRI) frequently requires gadolinium-based contrast agents (GBCAs). Despite generally being considered safe, previous literature suggests that GBCAs are capable of contrast-induced acute kidney injury (AKI). It is unclear if the risk for AKI is enhanced when GBCAs are administered concurrently with ionizing radiotherapy. During irradiation, gadolinium may be liberated from its chelator which may induce AKI. The goal of this work was to determine if radiation combined with GBCAs increased the incidence of AKI. Using a preclinical MRI-guided irradiation system, where MRI acquisitions and radiation delivery are performed in rapid succession, tumor-bearing mice with normal kidney function were injected with GBCA and treated with 2, 8 or 18 Gy irradiation. Renal function was assessed on days three and seven postirradiation to assess for AKI. No clinically relevant changes in blood urea nitrogen and creatinine were observed in any combination of GBCA and radiation dose. From these data, we conclude that GBCA in combination with radiation does not increase the risk for AKI in mice. Additional investigation of multiple doses of GBCA administered concurrently with irradiation is warranted to evaluate the risk of chronic kidney injury.
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Affiliation(s)
| | - Emily J Steinbach
- Department of Radiation Oncology, University of Iowa, Iowa City, Iowa
| | - Amanda L Kalen
- Department of Radiation Oncology, University of Iowa, Iowa City, Iowa
| | | | | | - Dan E Hyer
- Department of Radiation Oncology, University of Iowa, Iowa City, Iowa
| | - Douglas R Spitz
- Department of Radiation Oncology, University of Iowa, Iowa City, Iowa
| | - Ryan T Flynn
- Department of Radiation Oncology, University of Iowa, Iowa City, Iowa
| | - John M Buatti
- Department of Radiation Oncology, University of Iowa, Iowa City, Iowa
| | | | | | - Joël J St-Aubin
- Department of Radiation Oncology, University of Iowa, Iowa City, Iowa
| | - Bryan G Allen
- Department of Radiation Oncology, University of Iowa, Iowa City, Iowa
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36
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Affiliation(s)
- John M Buatti
- University of Iowa, Department of Radiation Oncology, Iowa,.
| | - Ana P Kiess
- Johns Hopkins University, Department of Radiation Oncology and Molecular Radiation Sciences, Baltimore, Maryland
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Buatti JM, Pryma DA, Kiess AP, Mailman J, Ennis RD, Menda Y, White GA, Pandit-Taskar N. A Framework for Patient-Centered Pathways of Care for Radiopharmaceutical Therapy: An ASTRO Consensus Document. Int J Radiat Oncol Biol Phys 2020; 109:913-922. [PMID: 33249143 DOI: 10.1016/j.ijrobp.2020.11.048] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 11/12/2020] [Indexed: 11/16/2022]
Abstract
Radiopharmaceutical therapy (RPT) is an area of projected growth and importance with several agents in clinical use, new agents in late-phase clinical trials, and many others under testing and development. This article proposes a framework for developing pathways of care that can be broadly applied to all RPTs, representing the current status of RPT. It suggests foundational elements for many pathways of care for patients with cancer and concludes with areas in active development and the future horizon for RPT treatment centers. Developing a framework for patient-centered pathways of care is a critical step in establishing RPT as standard therapy for patients with a diverse spectrum of cancers. This expected increase in RPT treatment options will affect a much larger population of patients with complex cancer. It will also require enhanced coordination and collaboration among appropriately qualified personnel with diverse expertise in image acquisition, image interpretation, quantitative imaging, dosimetry calculation, radiation quality assurance and safety as well as oncology care and RPT-induced sequelae and response assessment. The essential role of this evolving RPT care team within multidisciplinary oncology care is a cornerstone of this framework for a patient-centered pathway of care for RPT. Given the status of current RPT practice and the horizon for future applications, this patient-centered pathway of care guidance is timely and should help inform future clinical RPT practice paradigms. A task force was recruited from the Theranostic Working Group of the American Society for Radiation Oncology (ASTRO) in May 2019 with equal representation from the nuclear medicine community. The task force expanded on a framework that was originally conceived by the Working Group for patient-centered care. This framework was developed to incorporate the strengths of both radiation oncologists and nuclear medicine physicians. The manuscript was then developed by the task force and posted on the ASTRO website for a 6-week public comment period ending in July 2020. Comments were adjudicated, and the draft was sent to external organizations for potential endorsement. This document was sent to the ASTRO Board of Directors in October 2020 for approval.
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Affiliation(s)
- John M Buatti
- Department of Radiation Oncology, University of Iowa Carver School of Medicine, Iowa City, Iowa.
| | - Daniel A Pryma
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Ana P Kiess
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland
| | | | - Ronald D Ennis
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, New Brunswick, New Jersey
| | - Yusuf Menda
- Department of Radiology, University of Iowa Carver School of Medicine, Iowa City, Iowa
| | - Gerald A White
- Colorado Associates in Medical Physics, Colorado Springs, Colorado
| | - Neeta Pandit-Taskar
- Molecular Imaging and Therapy Service, Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, New York
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Buatti JS, Buatti JM, Yaddanapudi S, Pennington EC, Wang D, Gross B, St‐Aubin JJ, Hyer DE, Smith MC, Flynn RT. Stereotactic radiotherapy of appropriately selected meningiomas and metastatic brain tumor beds with gamma knife icon versus volumetric modulated arc therapy. J Appl Clin Med Phys 2020; 21:246-252. [PMID: 33207030 PMCID: PMC7769414 DOI: 10.1002/acm2.13100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 09/23/2020] [Accepted: 09/27/2020] [Indexed: 11/07/2022] Open
Abstract
PURPOSE To determine if the gamma knife icon (GKI) can provide superior stereotactic radiotherapy (SRT) dose distributions for appropriately selected meningioma and post-resection brain tumor bed treatments to volumetric modulated arc therapy (VMAT). MATERIALS AND METHODS Appropriately selected targets were not proximal to great vessels, did not have sensitive soft tissue including organs-at-risk (OARs) within the planning target volume (PTV), and did not have concave tumors containing excessive normal brain tissue. Four of fourteen candidate meningioma patients and six of six candidate patients with brain tumor cavities were considered for this treatment planning comparison study. PTVs were generated for GKI and VMAT by adding 1 mm and 3 mm margins, respectively, to the GTVs. Identical PTV V100% -values were obtained for the GKI and VMAT plans for each patient. Meningioma and tumor bed prescription doses were 52.7-54.0 in 1.7-1.8 Gy fractions and 25 Gy in 5 Gy fractions, respectively. GKI dose rate was 3.735 Gy/min for 16 mm collimators. RESULTS PTV radical dose homogeneity index was 3.03 ± 0.35 for GKI and 1.27 ± 0.19 for VMAT. Normal brain D1% , D5% , and D10% were lower for GKI than VMAT by 45.8 ± 10.9%, 38.9 ± 11.5%, and 35.4 ± 16.5% respectively. All OARs considered received lower maximum doses for GKI than VMAT. GKI and VMAT treatment times for meningioma plans were 12.1 ± 4.13 min and 6.2 ± 0.32 min, respectively, and, for tumor cavities, were 18.1 ± 5.1 min and 11.0 ± 0.56 min, respectively. CONCLUSIONS Appropriately selected meningioma and brain tumor bed patients may benefit from GKI-based SRT due to the decreased normal brain and OAR doses relative to VMAT enabled by smaller margins. Care must be taken in meningioma patient selection for SRT with the GKI, even if they are clinically appropriate for VMAT.
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Affiliation(s)
- Jacob S. Buatti
- Department of Radiation OncologyUniversity of Iowa Hospital and Clinics200 Hawkins DriveIowa CityIA52242USA
| | - John M. Buatti
- Department of Radiation OncologyUniversity of Iowa Hospital and Clinics200 Hawkins DriveIowa CityIA52242USA
| | - Sridhar Yaddanapudi
- Department of Radiation OncologyUniversity of Iowa Hospital and Clinics200 Hawkins DriveIowa CityIA52242USA
| | - Edward C. Pennington
- Department of Radiation OncologyUniversity of Iowa Hospital and Clinics200 Hawkins DriveIowa CityIA52242USA
| | - Dongxu Wang
- Department of Radiation OncologyUniversity of Iowa Hospital and Clinics200 Hawkins DriveIowa CityIA52242USA
| | - Brandie Gross
- Department of Radiation OncologyUniversity of Iowa Hospital and Clinics200 Hawkins DriveIowa CityIA52242USA
| | - Joël J. St‐Aubin
- Department of Radiation OncologyUniversity of Iowa Hospital and Clinics200 Hawkins DriveIowa CityIA52242USA
| | - Daniel E. Hyer
- Department of Radiation OncologyUniversity of Iowa Hospital and Clinics200 Hawkins DriveIowa CityIA52242USA
| | - Mark C. Smith
- Department of Radiation OncologyUniversity of Iowa Hospital and Clinics200 Hawkins DriveIowa CityIA52242USA
| | - Ryan T. Flynn
- Department of Radiation OncologyUniversity of Iowa Hospital and Clinics200 Hawkins DriveIowa CityIA52242USA
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Cushing CM, Petronek MS, Bodeker KL, Vollstedt S, Brown HA, Opat E, Hollenbeck NJ, Shanks T, Berg DJ, Smith BJ, Smith MC, Monga V, Furqan M, Howard MA, Greenlee JD, Mapuskar KA, St-Aubin J, Flynn RT, Cullen JJ, Buettner GR, Spitz DR, Buatti JM, Allen BG, Magnotta VA. Magnetic resonance imaging (MRI) of pharmacological ascorbate-induced iron redox state as a biomarker in subjects undergoing radio-chemotherapy. Redox Biol 2020; 38:101804. [PMID: 33260088 PMCID: PMC7708874 DOI: 10.1016/j.redox.2020.101804] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Revised: 10/29/2020] [Accepted: 11/13/2020] [Indexed: 12/14/2022] Open
Abstract
Pharmacological ascorbate (P-AscH-) combined with standard of care (SOC) radiation and temozolomide is being evaluated in a phase 2 clinical trial (NCT02344355) in the treatment of glioblastoma (GBM). Previously published data demonstrated that paramagnetic iron (Fe3+) catalyzes ascorbate's oxidation to form diamagnetic iron (Fe2+). Because paramagnetic Fe3+ may influence relaxation times observed in MR imaging, quantitative MR imaging of P-AscH--induced changes in redox-active Fe was assessed as a biomarker for therapy response. Gel phantoms containing either Fe3+ or Fe2+ were imaged with T2* and quantitative susceptibility mapping (QSM). Fifteen subjects receiving P-AscH- plus SOC underwent T2* and QSM imaging four weeks into treatment. Subjects were scanned: pre-P-AscH- infusion, post-P-AscH- infusion, and post-radiation (3-4 h between scans). Changes in T2* and QSM relaxation times in tumor and normal tissue were calculated and compared to changes in Fe3+ and Fe2+ gel phantoms. A GBM mouse model was used to study the relationship between the imaging findings and the labile iron pool. Phantoms containing Fe3+ demonstrated detectable changes in T2* and QSM relaxation times relative to Fe2+ phantoms. Compared to pre-P-AscH-, GBM T2* and QSM imaging were significantly changed post-P-AscH- infusion consistent with conversion of Fe3+ to Fe2+. No significant changes in T2* or QSM were observed in normal brain tissue. There was moderate concordance between T2* and QSM changes in both progression free survival and overall survival. The GBM mouse model showed similar results with P-AscH- inducing greater changes in tumor labile iron pools compared to the normal tissue. CONCLUSIONS: T2* and QSM MR-imaging responses are consistent with P-AscH- reducing Fe3+ to Fe2+, selectively in GBM tumor volumes and represent a potential biomarker of response. This study is the first application using MR imaging in humans to measure P-AscH--induced changes in redox-active iron.
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Affiliation(s)
- Cameron M Cushing
- Free Radical and Radiation Biology Program, Department of Radiation Oncology, Holden Comprehensive Cancer Center, University of Iowa Hospitals and Clinics, USA
| | - Michael S Petronek
- Free Radical and Radiation Biology Program, Department of Radiation Oncology, Holden Comprehensive Cancer Center, University of Iowa Hospitals and Clinics, USA
| | - Kellie L Bodeker
- Free Radical and Radiation Biology Program, Department of Radiation Oncology, Holden Comprehensive Cancer Center, University of Iowa Hospitals and Clinics, USA
| | - Sandy Vollstedt
- Free Radical and Radiation Biology Program, Department of Radiation Oncology, Holden Comprehensive Cancer Center, University of Iowa Hospitals and Clinics, USA
| | - Heather A Brown
- Free Radical and Radiation Biology Program, Department of Radiation Oncology, Holden Comprehensive Cancer Center, University of Iowa Hospitals and Clinics, USA
| | - Emyleigh Opat
- Free Radical and Radiation Biology Program, Department of Radiation Oncology, Holden Comprehensive Cancer Center, University of Iowa Hospitals and Clinics, USA
| | - Nancy J Hollenbeck
- Free Radical and Radiation Biology Program, Department of Radiation Oncology, Holden Comprehensive Cancer Center, University of Iowa Hospitals and Clinics, USA
| | - Thomas Shanks
- Free Radical and Radiation Biology Program, Department of Radiation Oncology, Holden Comprehensive Cancer Center, University of Iowa Hospitals and Clinics, USA
| | - Daniel J Berg
- Division of Hematology and Oncology, Department of Internal Medicine, Holden Comprehensive Cancer Center, University of Iowa Hospitals & Clinics, Iowa City, IA, USA
| | - Brian J Smith
- Department of Biostatistics, Holden Comprehensive Cancer Center, The University of Iowa, Iowa City, IA, USA
| | - Mark C Smith
- Free Radical and Radiation Biology Program, Department of Radiation Oncology, Holden Comprehensive Cancer Center, University of Iowa Hospitals and Clinics, USA
| | - Varun Monga
- Division of Hematology and Oncology, Department of Internal Medicine, Holden Comprehensive Cancer Center, University of Iowa Hospitals & Clinics, Iowa City, IA, USA
| | - Muhammad Furqan
- Division of Hematology and Oncology, Department of Internal Medicine, Holden Comprehensive Cancer Center, University of Iowa Hospitals & Clinics, Iowa City, IA, USA
| | - Matthew A Howard
- Department of Neurosurgery, University of Iowa Hospitals & Clinics, Iowa City, IA, USA
| | - Jeremy D Greenlee
- Department of Neurosurgery, University of Iowa Hospitals & Clinics, Iowa City, IA, USA
| | - Kranti A Mapuskar
- Free Radical and Radiation Biology Program, Department of Radiation Oncology, Holden Comprehensive Cancer Center, University of Iowa Hospitals and Clinics, USA
| | - Joel St-Aubin
- Free Radical and Radiation Biology Program, Department of Radiation Oncology, Holden Comprehensive Cancer Center, University of Iowa Hospitals and Clinics, USA
| | - Ryan T Flynn
- Free Radical and Radiation Biology Program, Department of Radiation Oncology, Holden Comprehensive Cancer Center, University of Iowa Hospitals and Clinics, USA
| | - Joseph J Cullen
- Department of Surgery, University of Iowa College of Medicine, Iowa City, IA, USA; Department of Radiation Oncology, University of Iowa College of Medicine, Iowa City, IA, USA; Holden Comprehensive Cancer Center, Iowa City, IA, USA; Veterans Affairs Medical Center, Iowa City, IA, USA
| | - Garry R Buettner
- Free Radical and Radiation Biology Program, Department of Radiation Oncology, Holden Comprehensive Cancer Center, University of Iowa Hospitals and Clinics, USA
| | - Douglas R Spitz
- Free Radical and Radiation Biology Program, Department of Radiation Oncology, Holden Comprehensive Cancer Center, University of Iowa Hospitals and Clinics, USA
| | - John M Buatti
- Free Radical and Radiation Biology Program, Department of Radiation Oncology, Holden Comprehensive Cancer Center, University of Iowa Hospitals and Clinics, USA
| | - Bryan G Allen
- Free Radical and Radiation Biology Program, Department of Radiation Oncology, Holden Comprehensive Cancer Center, University of Iowa Hospitals and Clinics, USA.
| | - Vincent A Magnotta
- Department of Radiology, Holden Comprehensive Cancer Center, University of Iowa Hospitals and Clinics, USA.
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Sperduto PW, Mesko S, Li J, Cagney D, Aizer A, Lin NU, Nesbit E, Kruser TJ, Chan J, Braunstein S, Lee J, Kirkpatrick JP, Breen W, Brown PD, Shi D, Shih HA, Soliman H, Sahgal A, Shanley R, Sperduto WA, Lou E, Everett A, Boggs DH, Masucci L, Roberge D, Remick J, Plichta K, Buatti JM, Jain S, Gaspar LE, Wu CC, Wang TJ, Bryant J, Chuong M, An Y, Chiang V, Nakano T, Aoyama H, Mehta MP. Survival in Patients With Brain Metastases: Summary Report on the Updated Diagnosis-Specific Graded Prognostic Assessment and Definition of the Eligibility Quotient. J Clin Oncol 2020; 38:3773-3784. [PMID: 32931399 PMCID: PMC7655019 DOI: 10.1200/jco.20.01255] [Citation(s) in RCA: 191] [Impact Index Per Article: 47.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/15/2020] [Indexed: 12/18/2022] Open
Abstract
PURPOSE Conventional wisdom has rendered patients with brain metastases ineligible for clinical trials for fear that poor survival could mask the benefit of otherwise promising treatments. Our group previously published the diagnosis-specific Graded Prognostic Assessment (GPA). Updates with larger contemporary cohorts using molecular markers and newly identified prognostic factors have been published. The purposes of this work are to present all the updated indices in a single report to guide treatment choice, stratify research, and define an eligibility quotient to expand eligibility. METHODS A multi-institutional database of 6,984 patients with newly diagnosed brain metastases underwent multivariable analyses of prognostic factors and treatments associated with survival for each primary site. Significant factors were used to define the updated GPA. GPAs of 4.0 and 0.0 correlate with the best and worst prognoses, respectively. RESULTS Significant prognostic factors varied by diagnosis and new prognostic factors were identified. Those factors were incorporated into the updated GPA with robust separation (P < .01) between subgroups. Survival has improved, but varies widely by GPA for patients with non-small-cell lung, breast, melanoma, GI, and renal cancer with brain metastases from 7-47 months, 3-36 months, 5-34 months, 3-17 months, and 4-35 months, respectively. CONCLUSION Median survival varies widely and our ability to estimate survival for patients with brain metastases has improved. The updated GPA (available free at brainmetgpa.com) provides an accurate tool with which to estimate survival, individualize treatment, and stratify clinical trials. Instead of excluding patients with brain metastases, enrollment should be encouraged and those trials should be stratified by the GPA to ensure those trials make appropriate comparisons. Furthermore, we recommend the expansion of eligibility to allow for the enrollment of patients with previously treated brain metastases who have a 50% or greater probability of an additional year of survival (eligibility quotient > 0.50).
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Affiliation(s)
- Paul W. Sperduto
- Minneapolis Radiation Oncology and University of Minnesota Gamma Knife Center, Minneapolis, MN
| | | | - Jing Li
- MD Anderson Cancer Center, Houston, TX
| | | | - Ayal Aizer
- Dana-Farber Cancer Institute, Boston, MA
| | | | | | | | - Jason Chan
- University of California, San Francisco, San Francisco, CA
| | | | | | | | | | | | - Diana Shi
- Massachusetts General Hospital, Boston, MA
| | | | - Hany Soliman
- Sunnybrook Odette Cancer Centre, University of Toronto, Toronto, Ontario, Canada
| | - Arjun Sahgal
- Sunnybrook Odette Cancer Centre, University of Toronto, Toronto, Ontario, Canada
| | | | | | - Emil Lou
- University of Minnesota, Minneapolis, MN
| | | | | | - Laura Masucci
- Centre Hospitalier de l'Université de Montreal, Montreal, Quebec, Canada
| | - David Roberge
- Centre Hospitalier de l'Université de Montreal, Montreal, Quebec, Canada
| | | | | | | | | | | | | | | | | | | | - Yi An
- Yale University, New Haven, CT
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Ali MY, Oliva CR, Noman ASM, Allen BG, Goswami PC, Zakharia Y, Monga V, Spitz DR, Buatti JM, Griguer CE. Radioresistance in Glioblastoma and the Development of Radiosensitizers. Cancers (Basel) 2020; 12:E2511. [PMID: 32899427 PMCID: PMC7564557 DOI: 10.3390/cancers12092511] [Citation(s) in RCA: 65] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Revised: 08/24/2020] [Accepted: 08/28/2020] [Indexed: 02/07/2023] Open
Abstract
Ionizing radiation is a common and effective therapeutic option for the treatment of glioblastoma (GBM). Unfortunately, some GBMs are relatively radioresistant and patients have worse outcomes after radiation treatment. The mechanisms underlying intrinsic radioresistance in GBM has been rigorously investigated over the past several years, but the complex interaction of the cellular molecules and signaling pathways involved in radioresistance remains incompletely defined. A clinically effective radiosensitizer that overcomes radioresistance has yet to be identified. In this review, we discuss the current status of radiation treatment in GBM, including advances in imaging techniques that have facilitated more accurate diagnosis, and the identified mechanisms of GBM radioresistance. In addition, we provide a summary of the candidate GBM radiosensitizers being investigated, including an update of subjects enrolled in clinical trials. Overall, this review highlights the importance of understanding the mechanisms of GBM radioresistance to facilitate the development of effective radiosensitizers.
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Affiliation(s)
- Md Yousuf Ali
- Interdisciplinary Graduate Program in Human Toxicology, University of Iowa, Iowa City, IA 52242, USA;
- Free Radical & Radiation Biology Program, Department of Radiation Oncology, Holden Comprehensive Cancer Center, University of Iowa, Iowa City, IA 52242, USA; (C.R.O.); (B.G.A.); (P.C.G.); (D.R.S.)
- Department of Radiation Oncology, University of Iowa Hospitals and Clinics, Iowa City, IA 52242, USA;
| | - Claudia R. Oliva
- Free Radical & Radiation Biology Program, Department of Radiation Oncology, Holden Comprehensive Cancer Center, University of Iowa, Iowa City, IA 52242, USA; (C.R.O.); (B.G.A.); (P.C.G.); (D.R.S.)
- Department of Radiation Oncology, University of Iowa Hospitals and Clinics, Iowa City, IA 52242, USA;
| | - Abu Shadat M. Noman
- Department of Biochemistry and Molecular Biology, The University of Chittagong, Chittagong 4331, Bangladesh;
- Department of Pathology, McGill University, Montreal, QC H3A 2B4, Canada
| | - Bryan G. Allen
- Free Radical & Radiation Biology Program, Department of Radiation Oncology, Holden Comprehensive Cancer Center, University of Iowa, Iowa City, IA 52242, USA; (C.R.O.); (B.G.A.); (P.C.G.); (D.R.S.)
- Department of Radiation Oncology, University of Iowa Hospitals and Clinics, Iowa City, IA 52242, USA;
| | - Prabhat C. Goswami
- Free Radical & Radiation Biology Program, Department of Radiation Oncology, Holden Comprehensive Cancer Center, University of Iowa, Iowa City, IA 52242, USA; (C.R.O.); (B.G.A.); (P.C.G.); (D.R.S.)
- Department of Radiation Oncology, University of Iowa Hospitals and Clinics, Iowa City, IA 52242, USA;
| | - Yousef Zakharia
- Department of Internal Medicine, University of Iowa, Iowa City, IA 52242, USA; (Y.Z.); (V.M.)
| | - Varun Monga
- Department of Internal Medicine, University of Iowa, Iowa City, IA 52242, USA; (Y.Z.); (V.M.)
| | - Douglas R. Spitz
- Free Radical & Radiation Biology Program, Department of Radiation Oncology, Holden Comprehensive Cancer Center, University of Iowa, Iowa City, IA 52242, USA; (C.R.O.); (B.G.A.); (P.C.G.); (D.R.S.)
- Department of Radiation Oncology, University of Iowa Hospitals and Clinics, Iowa City, IA 52242, USA;
| | - John M. Buatti
- Department of Radiation Oncology, University of Iowa Hospitals and Clinics, Iowa City, IA 52242, USA;
| | - Corinne E. Griguer
- Free Radical & Radiation Biology Program, Department of Radiation Oncology, Holden Comprehensive Cancer Center, University of Iowa, Iowa City, IA 52242, USA; (C.R.O.); (B.G.A.); (P.C.G.); (D.R.S.)
- Department of Radiation Oncology, University of Iowa Hospitals and Clinics, Iowa City, IA 52242, USA;
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Callaghan CM, Seyedin SN, Mohiuddin IH, Hawkes KL, Petronek MS, Anderson CM, Buatti JM, Milhem MM, Monga V, Allen BG. The Effect of Concurrent Stereotactic Body Radiation and Anti-PD-1 Therapy for Recurrent Metastatic Sarcoma. Radiat Res 2020; 194:124-132. [PMID: 32845986 PMCID: PMC10443123 DOI: 10.1667/rade-20-00017] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2020] [Accepted: 04/29/2020] [Indexed: 11/03/2022]
Abstract
Patients diagnosed with metastatic sarcoma have limited options for achieving both local and distant tumor control. While SBRT can achieve local control, distant response rates remain low. There is limited evidence demonstrating the safety and efficacy for combining SBRT with concurrent PD-1 checkpoint blockade in metastatic sarcoma. In this prospective case-series, we examined five patients with metastatic sarcoma on pembrolizumab treated concurrently with SBRT from July 1, 2016-October 30, 2018. Acute and chronic toxicity were recorded using Common Terminology Criteria for Adverse Events (CTCAE, version 5.0). SBRT-treated tumor control was assessed using Response Evaluation Criteria in Solid Tumors (RECIST version 1.1). With median follow-up of 14.9 months, three patients with undifferentiated pleomorphic sarcoma, one with intimal, and one with chondroblastic osteosarcoma received SBRT with concurrent pembrolizumab to 10 sites of metastatic disease. No grade 5 toxicities were observed. There was a single incidence of transient grade 4 lymphopenia which resolved without intervention. Grade 3 toxicities included anemia, thrombocytopenia, lymphopenia and colitis. One tumor demonstrated local progression after SBRT, and all others remained stable or with response. In conclusion, combining SBRT with PD-1 inhibition appeared to be safe in this patient population. Expected high rates of treated-tumor local control after SBRT were observed. Two of five patients demonstrated either enhanced local tumor regression, or possible abscopal effect.
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Affiliation(s)
| | - Steven N. Seyedin
- Department of Radiation Oncology, The University of Iowa, Iowa City, Iowa 52242
| | - Imran H. Mohiuddin
- Department of Radiation Oncology, The University of Iowa, Iowa City, Iowa 52242
| | - Kelli L. Hawkes
- Department of Radiation Oncology, The University of Iowa, Iowa City, Iowa 52242
| | - Michael S. Petronek
- Department of Radiation Oncology, The University of Iowa, Iowa City, Iowa 52242
| | - Carryn M. Anderson
- Department of Radiation Oncology, The University of Iowa, Iowa City, Iowa 52242
| | - John M. Buatti
- Department of Radiation Oncology, The University of Iowa, Iowa City, Iowa 52242
| | - Mohammed M. Milhem
- Division of Hematology, Oncology, and Blood and Marrow Transplantation, Holden Comprehensive Cancer Center, Department of Internal Medicine, The University of Iowa, Iowa City, Iowa 52242
| | - Varun Monga
- Division of Hematology, Oncology, and Blood and Marrow Transplantation, Holden Comprehensive Cancer Center, Department of Internal Medicine, The University of Iowa, Iowa City, Iowa 52242
| | - Bryan G. Allen
- Department of Radiation Oncology, The University of Iowa, Iowa City, Iowa 52242
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Watkins JM, Russo JK, Andresen N, Rountree CR, Zahra A, Mott SL, Herr DJ, O'Keefe J, Allen BG, Sharma AK, Buatti JM. Long-term outcome comparison for standard fractionation (>59 Gy) versus hyperfractionated (>45 Gy) radiotherapy plus concurrent chemotherapy for limited-stage small-cell lung cancer. Rep Pract Oncol Radiother 2020; 25:489-493. [PMID: 32477014 DOI: 10.1016/j.rpor.2020.03.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Revised: 01/15/2020] [Accepted: 03/27/2020] [Indexed: 11/25/2022] Open
Abstract
Background Concurrent chemoradiotherapy (CCRT) is commonly employed in limited-stage small-cell lung cancer (LS-SCLC); however, the optimal radiotherapy regimen is still unknown. This 3-institution analysis compares long-term disease control and survival outcomes for once- (QD) versus twice-daily (BID) radiotherapy at contemporary doses. Methods and Materials Data were collected for LS-SCLC patients treated with platinum-based CCRT and planned RT doses of >5940 cGy at >180 cGy QD or >4500 cGy at 150 cGy BID. Comparative outcome analyses were performed for treatment groups. Results From 2005 through 2014, 132 patients met inclusion criteria for analysis (80 QD, 52 BID). Treatment groups were well-balanced, excepting higher rate of advanced mediastinal staging, longer interval from biopsy to treatment initiation, and lower rate of prophylactic cranial irradiation for the QD group, as well as institutional practice variation. At median survivor follow-up of 33.5 months (range, 4.6-105.8), 80 patients experienced disease failure (44 QD, 36 BID), and 106 died (62 QD, 44 BID). No differences in disease control or survival were demonstrated between treatment groups. Conclusion The present analysis did not detect a difference in disease control or survival outcomes for contemporary dose QD versus BID CCRT in LS-SCLC.
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Affiliation(s)
| | - J Kyle Russo
- Bismarck Cancer Center, Bismarck, North Dakota, US
| | - Nicholas Andresen
- Department of Otolaryngology - Head & Neck Surgery, Johns Hopkins Hospital, Baltimore, Maryland, US
| | - Coyt R Rountree
- Department of Radiation Oncology, Medical University of South Carolina, Charleston, South Carolina, US
| | - Amir Zahra
- Department of Radiation Oncology, University of Iowa, Iowa City, Iowa, US
| | - Sarah L Mott
- Holden Comprehensive Cancer Center, University of Iowa, Iowa City, IA, US
| | - Daniel J Herr
- Department of Radiation Oncology, Medical University of South Carolina, Charleston, South Carolina, US
| | - Jacy O'Keefe
- Bismarck Cancer Center, Bismarck, North Dakota, US
| | - Bryan G Allen
- Department of Radiation Oncology, University of Iowa, Iowa City, Iowa, US
| | - Anand K Sharma
- Department of Radiation Oncology, Medical University of South Carolina, Charleston, South Carolina, US
| | - John M Buatti
- Department of Radiation Oncology, University of Iowa, Iowa City, Iowa, US
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Jones EF, Buatti JM, Shu HK, Wahl RL, Kurland BF, Linden HM, Mankoff DA, Rubin DL, Tata D, Nordstrom RJ, Hadjiyski L, Holdhoff M, Schwartz LH. Clinical Trial Design and Development Work Group Within the Quantitative Imaging Network. Tomography 2020; 6:60-64. [PMID: 32548281 PMCID: PMC7289239 DOI: 10.18383/j.tom.2019.00022] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
The Clinical Trial Design and Development Working Group within the Quantitative Imaging Network focuses on providing support for the development, validation, and harmonization of quantitative imaging (QI) methods and tools for use in cancer clinical trials. In the past 10 years, the Group has been working in several areas to identify challenges and opportunities in clinical trials involving QI and radiation oncology. The Group has been working with Quantitative Imaging Network members and the Quantitative Imaging Biomarkers Alliance leadership to develop guidelines for standardizing the reporting of quantitative imaging. As a validation platform, the Group led a multireader study to test a semi-automated positron emission tomography quantification software. Clinical translation of QI tools cannot be possible without a continuing dialogue with clinical users. This article also highlights the outreach activities extended to cooperative groups and other organizations that promote the use of QI tools to support clinical decisions.
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Affiliation(s)
- Ella F. Jones
- School of Medicine, University of California San Francisco, San Francisco, CA
| | - John M. Buatti
- Carver College of Medicine, The University of Iowa, Iowa City, IA
| | - Hui-Kuo Shu
- Winship Cancer Institute, Emory University, Atlanta, GA
| | | | - Brenda F. Kurland
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA
- School of Medicine, University of Washington, Seattle, WA
| | | | - David A. Mankoff
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | | | - Darrell Tata
- Cancer Imaging Program, National Cancer Institute, Bethesda, MD
| | | | | | - Matthias Holdhoff
- Sidney Kimmel Comprehensive Cancer Center, John Hopkins University, Baltimore, MD; and
| | - Lawrence H. Schwartz
- Irving Medical Center, Columbia University, New York Presbyterian Hospital, New York, NY
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45
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Sperduto PW, Mesko S, Li J, Cagney D, Aizer A, Lin NU, Nesbit E, Kruser TJ, Chan J, Braunstein S, Lee J, Kirkpatrick JP, Breen W, Brown PD, Shi D, Shih HA, Soliman H, Sahgal A, Shanley R, Sperduto W, Lou E, Everett A, Boggs DH, Masucci L, Roberge D, Remick J, Plichta K, Buatti JM, Jain S, Gaspar LE, Wu CC, Wang TJC, Bryant J, Chuong M, Yu J, Chiang V, Nakano T, Aoyama H, Mehta MP. Beyond an Updated Graded Prognostic Assessment (Breast GPA): A Prognostic Index and Trends in Treatment and Survival in Breast Cancer Brain Metastases From 1985 to Today. Int J Radiat Oncol Biol Phys 2020; 107:334-343. [PMID: 32084525 PMCID: PMC7276246 DOI: 10.1016/j.ijrobp.2020.01.051] [Citation(s) in RCA: 73] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Revised: 01/28/2020] [Accepted: 01/31/2020] [Indexed: 02/02/2023]
Abstract
PURPOSE Brain metastases are a common sequelae of breast cancer. Survival varies widely based on diagnosis-specific prognostic factors (PF). We previously published a prognostic index (Graded Prognostic Assessment [GPA]) for patients with breast cancer with brain metastases (BCBM), based on cohort A (1985-2007, n = 642), then updated it, reporting the effect of tumor subtype in cohort B (1993-2010, n = 400). The purpose of this study is to update the Breast GPA with a larger contemporary cohort (C) and compare treatment and survival across the 3 cohorts. METHODS AND MATERIALS A multi-institutional (19), multinational (3), retrospective database of 2473 patients with breast cancer with newly diagnosed brain metastases (BCBM) diagnosed from January 1, 2006, to December 31, 2017, was created and compared with prior cohorts. Associations of PF and treatment with survival were analyzed. Kaplan-Meier survival estimates were compared with log-rank tests. PF were weighted and the Breast GPA was updated such that a GPA of 0 and 4.0 correlate with the worst and best prognoses, respectively. RESULTS Median survival (MS) for cohorts A, B, and C improved over time (from 11, to 14 to 16 months, respectively; P < .01), despite the subtype distribution becoming less favorable. PF significant for survival were tumor subtype, Karnofsky Performance Status, age, number of BCBMs, and extracranial metastases (all P < .01). MS for GPA 0 to 1.0, 1.5-2.0, 2.5-3.0, and 3.5-4.0 was 6, 13, 24, and 36 months, respectively. Between cohorts B and C, the proportion of human epidermal receptor 2 + subtype decreased from 31% to 18% (P < .01) and MS in this subtype increased from 18 to 25 months (P < .01). CONCLUSIONS MS has improved modestly but varies widely by diagnosis-specific PF. New PF are identified and incorporated into an updated Breast GPA (free online calculator available at brainmetgpa.com). The Breast GPA facilitates clinical decision-making and will be useful for stratification of future clinical trials. Furthermore, these data suggest human epidermal receptor 2-targeted therapies improve clinical outcomes in some patients with BCBM.
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Affiliation(s)
- Paul W Sperduto
- Minneapolis Radiation Oncology & University of Minnesota Gamma Knife Center, Minneapolis, Minnesota.
| | | | - Jing Li
- MD Anderson Cancer Center, Houston, Texas
| | | | - Ayal Aizer
- Dana Farber Cancer Institute, Boston, Massachusetts
| | - Nancy U Lin
- Dana Farber Cancer Institute, Boston, Massachusetts
| | | | | | - Jason Chan
- University of California San Francisco, San Francisco, California
| | - Steve Braunstein
- University of California San Francisco, San Francisco, California
| | | | | | | | | | - Diana Shi
- Massachusetts General Hospital, Massachusetts, Boston, Massachusetts
| | - Helen A Shih
- Massachusetts General Hospital, Massachusetts, Boston, Massachusetts
| | - Hany Soliman
- Sunnybrook Odette Cancer Centre, University of Toronto, Toronto, Canada
| | - Arjun Sahgal
- Sunnybrook Odette Cancer Centre, University of Toronto, Toronto, Canada
| | | | | | - Emil Lou
- University of Minnesota, Minneapolis, Minnesota
| | | | | | - Laura Masucci
- Centre Hospitalier de l' Université de Montréal, Montreal, Quebec, Canada
| | - David Roberge
- Centre Hospitalier de l' Université de Montréal, Montreal, Quebec, Canada
| | | | | | | | | | | | | | | | | | | | - James Yu
- Yale University, New Haven, Connecticut
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46
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Ulrich EJ, Menda Y, Boles Ponto LL, Anderson CM, Smith BJ, Sunderland JJ, Graham MM, Buatti JM, Beichel RR. FLT PET Radiomics for Response Prediction to Chemoradiation Therapy in Head and Neck Squamous Cell Cancer. ACTA ACUST UNITED AC 2020; 5:161-169. [PMID: 30854454 PMCID: PMC6403029 DOI: 10.18383/j.tom.2018.00038] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Radiomics is an image analysis approach for extracting large amounts of quantitative information from medical images using a variety of computational methods. Our goal was to evaluate the utility of radiomic feature analysis from 18F-fluorothymidine positron emission tomography (FLT PET) obtained at baseline in prediction of treatment response in patients with head and neck cancer. Thirty patients with advanced-stage oropharyngeal or laryngeal cancer, treated with definitive chemoradiation therapy, underwent FLT PET imaging before treatment. In total, 377 radiomic features of FLT uptake and feature variants were extracted from volumes of interest; these features variants were defined by either the primary tumor or the total lesion burden, which consisted of the primary tumor and all FLT-avid nodes. Feature variants included normalized measurements of uptake, which were calculated by dividing lesion uptake values by the mean uptake value in the bone marrow. Feature reduction was performed using clustering to remove redundancy, leaving 172 representative features. Effects of these features on progression-free survival were modeled with Cox regression and P-values corrected for multiple comparisons. In total, 9 features were considered significant. Our results suggest that smaller, more homogenous lesions at baseline were associated with better prognosis. In addition, features extracted from total lesion burden had a higher concordance index than primary tumor features for 8 of the 9 significant features. Furthermore, total lesion burden features showed lower interobserver variability.
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Affiliation(s)
- Ethan J Ulrich
- Departments of Electrical and Computer Engineering.,Biomedical Engineering
| | | | | | | | | | | | | | | | - Reinhard R Beichel
- Departments of Electrical and Computer Engineering.,Internal Medicine, University of Iowa, Iowa City, IA
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47
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Xiong X, Linhardt TJ, Liu W, Smith BJ, Sun W, Bauer C, Sunderland JJ, Graham MM, Buatti JM, Beichel RR. A 3D deep convolutional neural network approach for the automated measurement of cerebellum tracer uptake in FDG PET-CT scans. Med Phys 2019; 47:1058-1066. [PMID: 31855287 DOI: 10.1002/mp.13970] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2019] [Revised: 12/05/2019] [Accepted: 12/05/2019] [Indexed: 01/12/2023] Open
Abstract
PURPOSE The purpose of this work was to assess the potential of deep convolutional neural networks in automated measurement of cerebellum tracer uptake in F-18 fluorodeoxyglucose (FDG) positron emission tomography (PET) scans. METHODS Three different three-dimensional (3D) convolutional neural network architectures (U-Net, V-Net, and modified U-Net) were implemented and compared regarding their performance in 3D cerebellum segmentation in FDG PET scans. For network training and testing, 134 PET scans with corresponding manual volumetric segmentations were utilized. For segmentation performance assessment, a fivefold cross-validation was used, and the Dice coefficient as well as signed and unsigned distance errors were calculated. In addition, standardized uptake value (SUV) uptake measurement performance was assessed by means of a statistical comparison to an independent reference standard. Furthermore, a comparison to a previously reported active-shape-model-based approach was performed. RESULTS Out of the three convolutional neural networks investigated, the modified U-Net showed significantly better segmentation performance. It achieved a Dice coefficient of 0.911 ± 0.026, a signed distance error of 0.220 ± 0.103 mm, and an unsigned distance error of 1.048 ± 0.340 mm. When compared to the independent reference standard, SUV uptake measurements produced with the modified U-Net showed no significant error in slope and intercept. The estimated reduction in total SUV measurement error was 95.1%. CONCLUSIONS The presented work demonstrates the potential of deep convolutional neural networks in automated SUV measurement of reference regions. While it focuses on the cerebellum, utilized methods can be generalized to other reference regions like the liver or aortic arch. Future work will focus on combining lesion and reference region analysis into one approach.
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Affiliation(s)
- Xiaofan Xiong
- Department of Biomedical Engineering, The University of Iowa, Iowa City, IA, 52242, USA
| | - Timothy J Linhardt
- Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, 52242, USA
| | - Weiren Liu
- Roy J. and Lucille A. Carver College of Medicine, The University of Iowa, Iowa City, IA, 52242, USA
| | - Brian J Smith
- Department of Biostatistics, The University of Iowa, Iowa City, IA, 52242, USA
| | - Wenqing Sun
- Department of Radiation Oncology, University of Iowa Hospitals and Clinics, Iowa City, IA, 52242, USA
| | - Christian Bauer
- Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, 52242, USA
| | - John J Sunderland
- Department of Radiology, The University of Iowa, Iowa City, IA, 52242, USA
| | - Michael M Graham
- Department of Radiology, The University of Iowa, Iowa City, IA, 52242, USA
| | - John M Buatti
- Department of Radiation Oncology, University of Iowa Hospitals and Clinics, Iowa City, IA, 52242, USA
| | - Reinhard R Beichel
- Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, 52242, USA
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48
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Baek S, He Y, Allen BG, Buatti JM, Smith BJ, Tong L, Sun Z, Wu J, Diehn M, Loo BW, Plichta KA, Seyedin SN, Gannon M, Cabel KR, Kim Y, Wu X. Deep segmentation networks predict survival of non-small cell lung cancer. Sci Rep 2019; 9:17286. [PMID: 31754135 PMCID: PMC6872742 DOI: 10.1038/s41598-019-53461-2] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2019] [Accepted: 10/31/2019] [Indexed: 12/11/2022] Open
Abstract
Non-small-cell lung cancer (NSCLC) represents approximately 80-85% of lung cancer diagnoses and is the leading cause of cancer-related death worldwide. Recent studies indicate that image-based radiomics features from positron emission tomography/computed tomography (PET/CT) images have predictive power for NSCLC outcomes. To this end, easily calculated functional features such as the maximum and the mean of standard uptake value (SUV) and total lesion glycolysis (TLG) are most commonly used for NSCLC prognostication, but their prognostic value remains controversial. Meanwhile, convolutional neural networks (CNN) are rapidly emerging as a new method for cancer image analysis, with significantly enhanced predictive power compared to hand-crafted radiomics features. Here we show that CNNs trained to perform the tumor segmentation task, with no other information than physician contours, identify a rich set of survival-related image features with remarkable prognostic value. In a retrospective study on pre-treatment PET-CT images of 96 NSCLC patients before stereotactic-body radiotherapy (SBRT), we found that the CNN segmentation algorithm (U-Net) trained for tumor segmentation in PET and CT images, contained features having strong correlation with 2- and 5-year overall and disease-specific survivals. The U-Net algorithm has not seen any other clinical information (e.g. survival, age, smoking history, etc.) than the images and the corresponding tumor contours provided by physicians. In addition, we observed the same trend by validating the U-Net features against an extramural data set provided by Stanford Cancer Institute. Furthermore, through visualization of the U-Net, we also found convincing evidence that the regions of metastasis and recurrence appear to match with the regions where the U-Net features identified patterns that predicted higher likelihoods of death. We anticipate our findings will be a starting point for more sophisticated non-intrusive patient specific cancer prognosis determination. For example, the deep learned PET/CT features can not only predict survival but also visualize high-risk regions within or adjacent to the primary tumor and hence potentially impact therapeutic outcomes by optimal selection of therapeutic strategy or first-line therapy adjustment.
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Affiliation(s)
- Stephen Baek
- University of Iowa, Department of Industrial and Systems Engineering, Iowa City, IA, 52242, United States
- University of Iowa, Department of Radiation Oncology, Iowa City, IA, 52242, United States
- University of Iowa, Department of Electrical and Computer Engineering, Iowa City, IA, 52242, United States
| | - Yusen He
- University of Iowa, Department of Industrial and Systems Engineering, Iowa City, IA, 52242, United States
| | - Bryan G Allen
- University of Iowa, Department of Radiation Oncology, Iowa City, IA, 52242, United States
| | - John M Buatti
- University of Iowa, Department of Radiation Oncology, Iowa City, IA, 52242, United States
| | - Brian J Smith
- University of Iowa, Department of Biostatistics, Iowa City, IA, 52242, United States
| | - Ling Tong
- University of Iowa, Department of Business Analytics, Iowa City, IA, 52242, United States
| | - Zhiyu Sun
- University of Iowa, Department of Industrial and Systems Engineering, Iowa City, IA, 52242, United States
| | - Jia Wu
- Stanford University, Stanford Cancer Institute, Palo Alto, CA, 94304, United States
| | - Maximilian Diehn
- Stanford University, Stanford Cancer Institute, Palo Alto, CA, 94304, United States
| | - Billy W Loo
- Stanford University, Stanford Cancer Institute, Palo Alto, CA, 94304, United States
| | - Kristin A Plichta
- University of Iowa, Department of Radiation Oncology, Iowa City, IA, 52242, United States
| | - Steven N Seyedin
- University of Iowa, Department of Radiation Oncology, Iowa City, IA, 52242, United States
| | - Maggie Gannon
- University of Iowa, Department of Radiation Oncology, Iowa City, IA, 52242, United States
| | - Katherine R Cabel
- University of Iowa, Department of Radiation Oncology, Iowa City, IA, 52242, United States
| | - Yusung Kim
- University of Iowa, Department of Radiation Oncology, Iowa City, IA, 52242, United States.
| | - Xiaodong Wu
- University of Iowa, Department of Radiation Oncology, Iowa City, IA, 52242, United States.
- University of Iowa, Department of Electrical and Computer Engineering, Iowa City, IA, 52242, United States.
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49
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Sperduto PW, Deegan BJ, Li J, Jethwa KR, Brown PD, Lockney N, Beal K, Rana NG, Attia A, Tseng CL, Sahgal A, Shanley R, Sperduto WA, Lou E, Zahra A, Buatti JM, Yu JB, Chiang V, Molitoris JK, Masucci L, Roberge D, Shi DD, Shih HA, Olson A, Kirkpatrick JP, Braunstein S, Sneed P, Mehta MP. Estimating survival for renal cell carcinoma patients with brain metastases: an update of the Renal Graded Prognostic Assessment tool. Neuro Oncol 2019; 20:1652-1660. [PMID: 30418657 DOI: 10.1093/neuonc/noy099] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
Background Brain metastases are a common complication of renal cell carcinoma (RCC). Our group previously published the Renal Graded Prognostic Assessment (GPA) tool. In our prior RCC study (n = 286, 1985-2005), we found marked heterogeneity and variation in outcomes. In our recent update in a larger, more contemporary cohort, we identified additional significant prognostic factors. The purpose of this study is to update the original Renal-GPA based on the newly identified prognostic factors. Methods A multi-institutional retrospective institutional review board-approved database of 711 RCC patients with new brain metastases diagnosed from January 1, 2006 to December 31, 2015 was created. Clinical parameters and treatment were correlated with survival. A revised Renal GPA index was designed by weighting the most significant factors in proportion to their hazard ratios and assigning scores such that the patients with the best and worst prognoses would have a GPA of 4.0 and 0.0, respectively. Results The 4 most significant factors were Karnofsky performance status, number of brain metastases, extracranial metastases, and hemoglobin. The overall median survival was 12 months. Median survival for GPA groups 0-1.0, 1.5-2.0, 2.5-3, and 3.5-4.0 (% n = 25, 27, 30 and 17) was 4, 12, 17, and 35 months, respectively. Conclusion The updated Renal GPA is a user-friendly tool that will help clinicians and patients better understand prognosis, individualize clinical decision making and treatment selection, provide a means to compare retrospective literature, and provide more robust stratification of future clinical trials in this heterogeneous population. To simplify use of this tool in daily practice, a free online application is available at brainmetgpa.com.
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Affiliation(s)
- Paul W Sperduto
- Minneapolis Radiation Oncology and University of Minnesota Gamma Knife Center, Minneapolis, Minnesota
| | - Brian J Deegan
- MD Anderson Cancer Center, Department of Radiation Oncology, Houston, Texas
| | - Jing Li
- MD Anderson Cancer Center, Department of Radiation Oncology, Houston, Texas
| | - Krishan R Jethwa
- Mayo Clinic, Department of Radiation Oncology, Rochester, Minnesota
| | - Paul D Brown
- Mayo Clinic, Department of Radiation Oncology, Rochester, Minnesota
| | - Natalie Lockney
- Memorial Sloan Kettering Cancer Center, Department of Radiation Oncology, New York, New York
| | - Kathryn Beal
- Memorial Sloan Kettering Cancer Center, Department of Radiation Oncology, New York, New York
| | - Nitesh G Rana
- Vanderbilt University, Department of Radiation Oncology, Nashville, Tennessee
| | - Albert Attia
- Vanderbilt University, Department of Radiation Oncology, Nashville, Tennessee
| | - Chia-Lin Tseng
- Sunnybrook-University of Toronto, Department of Radiation Oncology, Toronto, Ontario, Canada
| | - Arjun Sahgal
- Sunnybrook-University of Toronto, Department of Radiation Oncology, Toronto, Ontario, Canada
| | - Ryan Shanley
- University of Minnesota Biostatistics, Minneapolis, Minnesota
| | - William A Sperduto
- University of Minnesota Cancer Center, Department of Medical Oncology, Minneapolis, Minnesota
| | - Emil Lou
- University of Minnesota Cancer Center, Department of Medical Oncology, Minneapolis, Minnesota
| | - Amir Zahra
- University of Iowa, Department of Radiation Oncology, Iowa City, Iowa
| | - John M Buatti
- University of Iowa, Department of Radiation Oncology, Iowa City, Iowa
| | - James B Yu
- Yale University, Department of Radiation Oncology, New Haven, Connecticut
| | - Veronica Chiang
- Yale University, Department of Neurosurgery, New Haven, Connecticut
| | - Jason K Molitoris
- University of Maryland, Department of Radiation Oncology, Baltimore, Maryland
| | - Laura Masucci
- Centre Hospitalier de l' Université de Montreal, Department of Radiation Oncology, Montreal, Quebec, Canada
| | - David Roberge
- Centre Hospitalier de l' Université de Montreal, Department of Radiation Oncology, Montreal, Quebec, Canada
| | - Diana D Shi
- Massachusetts General Hospital, Department of Radiation Oncology, Boston, Massachusetts
| | - Helen A Shih
- Massachusetts General Hospital, Department of Radiation Oncology, Boston, Massachusetts
| | - Adam Olson
- Duke University, Department of Radiation Oncology, Durham, North Carolina
| | - John P Kirkpatrick
- Duke University, Department of Radiation Oncology, Durham, North Carolina
| | - Steve Braunstein
- University of California San Francisco, Department of Radiation Oncology, San Francisco, California
| | - Penny Sneed
- University of California San Francisco, Department of Radiation Oncology, San Francisco, California
| | - Minesh P Mehta
- Miami Cancer Institute, Department of Radiation Oncology, Miami, Florida
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50
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Anderson CM, Lee CM, Saunders DP, Curtis A, Dunlap N, Nangia C, Lee AS, Gordon SM, Kovoor P, Arevalo-Araujo R, Bar-Ad V, Peddada A, Colvett K, Miller D, Jain AK, Wheeler J, Blakaj D, Bonomi M, Agarwala SS, Garg M, Worden F, Holmlund J, Brill JM, Downs M, Sonis ST, Katz S, Buatti JM. Phase IIb, Randomized, Double-Blind Trial of GC4419 Versus Placebo to Reduce Severe Oral Mucositis Due to Concurrent Radiotherapy and Cisplatin For Head and Neck Cancer. J Clin Oncol 2019; 37:3256-3265. [PMID: 31618127 PMCID: PMC6881100 DOI: 10.1200/jco.19.01507] [Citation(s) in RCA: 59] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
PURPOSE Oral mucositis (OM) remains a common, debilitating toxicity of radiation therapy (RT) for head and neck cancer. The goal of this phase IIb, multi-institutional, randomized, double-blind trial was to compare the efficacy and safety of GC4419, a superoxide dismutase mimetic, with placebo to reduce the duration, incidence, and severity of severe OM (SOM). PATIENTS AND METHODS A total of 223 patients (from 44 institutions) with locally advanced oral cavity or oropharynx cancer planned to be treated with definitive or postoperative intensity-modulated RT (IMRT; 60 to 72 Gy [≥ 50 Gy to two or more oral sites]) plus cisplatin (weekly or every 3 weeks) were randomly assigned to receive 30 mg (n = 73) or 90 mg (n = 76) of GC4419 or to receive placebo (n = 74) by 60-minute intravenous administration before each IMRT fraction. WHO grade of OM was assessed biweekly during IMRT and then weekly for up to 8 weeks after IMRT. The primary endpoint was duration of SOM tested for each active dose level versus placebo (intent-to-treat population, two-sided α of .05). The National Cancer Institute Common Terminology Criteria for Adverse Events, version 4.03, was used for adverse event grading. RESULTS Baseline patient and tumor characteristics as well as treatment delivery were balanced. With 90 mg GC4419 versus placebo, SOM duration was significantly reduced (P = .024; median, 1.5 v 19 days). SOM incidence (43% v 65%; P = .009) and severity (grade 4 incidence, 16% v 30%; P = .045) also were improved. Intermediate improvements were seen with the 30-mg dose. Safety was comparable across arms, with no significant GC4419-specific toxicity nor increase of known toxicities of IMRT plus cisplatin. The 2-year follow-up for tumor outcomes is ongoing. CONCLUSION GC4419 at a dose of 90 mg produced a significant, clinically meaningful reduction of SOM duration, incidence, and severity with acceptable safety. A phase III trial (ROMAN; ClinicalTrials.gov identifier: NCT03689712) has begun.
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Affiliation(s)
| | | | - Deborah P Saunders
- North East Cancer Centre, Health Sciences North, Northern Ontario School of Medicine, Sudbury, Ontario, Canada
| | | | - Neal Dunlap
- University of Louisville/James Graham Brown Cancer Center, Louisville, KY
| | | | | | | | | | | | | | | | - Kyle Colvett
- Mountain States Health Alliance, Johnson City, TN
| | | | - Anshu K Jain
- Ashland-Bellefonte Cancer Center, Ashland, KY.,Yale School of Medicine, New Haven, CT
| | | | - Dukagjin Blakaj
- James Cancer Hospital and Solove Research Institute, The Ohio State University, Columbus, OH
| | - Marcelo Bonomi
- James Cancer Hospital and Solove Research Institute, The Ohio State University, Columbus, OH
| | | | | | | | | | | | - Matt Downs
- Statistics Collaborative, Washington, DC
| | | | | | - John M Buatti
- University of Iowa Hospitals and Clinics, Iowa City, IA
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