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EANO guidelines on the diagnosis and treatment of diffuse gliomas of adulthood. Nat Rev Clin Oncol 2020; 18:170-186. [PMID: 33293629 PMCID: PMC7904519 DOI: 10.1038/s41571-020-00447-z] [Citation(s) in RCA: 864] [Impact Index Per Article: 216.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/19/2020] [Indexed: 01/16/2023]
Abstract
In response to major changes in diagnostic algorithms and the publication of mature results from various large clinical trials, the European Association of Neuro-Oncology (EANO) recognized the need to provide updated guidelines for the diagnosis and management of adult patients with diffuse gliomas. Through these evidence-based guidelines, a task force of EANO provides recommendations for the diagnosis, treatment and follow-up of adult patients with diffuse gliomas. The diagnostic component is based on the 2016 update of the WHO Classification of Tumors of the Central Nervous System and the subsequent recommendations of the Consortium to Inform Molecular and Practical Approaches to CNS Tumour Taxonomy — Not Officially WHO (cIMPACT-NOW). With regard to therapy, we formulated recommendations based on the results from the latest practice-changing clinical trials and also provide guidance for neuropathological and neuroradiological assessment. In these guidelines, we define the role of the major treatment modalities of surgery, radiotherapy and systemic pharmacotherapy, covering current advances and cognizant that unnecessary interventions and expenses should be avoided. This document is intended to be a source of reference for professionals involved in the management of adult patients with diffuse gliomas, for patients and caregivers, and for health-care providers. Herein, the European Association of Neuro-Oncology (EANO) provides recommendations for the diagnosis, treatment and follow-up of adult patients with diffuse gliomas. These evidence-based guidelines incorporate major changes in diagnostic algorithms based on the 2016 update of the WHO Classification of Tumors of the Central Nervous System as well as on evidence from recent large clinical trials.
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152
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Clement P, Booth T, Borovečki F, Emblem KE, Figueiredo P, Hirschler L, Jančálek R, Keil VC, Maumet C, Özsunar Y, Pernet C, Petr J, Pinto J, Smits M, Warnert EAH. GliMR: Cross-Border Collaborations to Promote Advanced MRI Biomarkers for Glioma. J Med Biol Eng 2020; 41:115-125. [PMID: 33293909 PMCID: PMC7712600 DOI: 10.1007/s40846-020-00582-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Accepted: 11/04/2020] [Indexed: 01/01/2023]
Abstract
Purpose There is an annual incidence of 50,000 glioma cases in Europe. The optimal treatment strategy is highly personalised, depending on tumour type, grade, spatial localization, and the degree of tissue infiltration. In research settings, advanced magnetic resonance imaging (MRI) has shown great promise as a tool to inform personalised treatment decisions. However, the use of advanced MRI in clinical practice remains scarce due to the downstream effects of siloed glioma imaging research with limited representation of MRI specialists in established consortia; and the associated lack of available tools and expertise in clinical settings. These shortcomings delay the translation of scientific breakthroughs into novel treatment strategy. As a response we have developed the network “Glioma MR Imaging 2.0” (GliMR) which we present in this article. Methods GliMR aims to build a pan-European and multidisciplinary network of experts and accelerate the use of advanced MRI in glioma beyond the current “state-of-the-art” in glioma imaging. The Action Glioma MR Imaging 2.0 (GliMR) was granted funding by the European Cooperation in Science and Technology (COST) in June 2019. Results GliMR’s first grant period ran from September 2019 to April 2020, during which several meetings were held and projects were initiated, such as reviewing the current knowledge on advanced MRI; developing a General Data Protection Regulation (GDPR) compliant consent form; and setting up the website. Conclusion The Action overcomes the pre-existing limitations of glioma research and is funded until September 2023. New members will be accepted during its entire duration.
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Affiliation(s)
- Patricia Clement
- Ghent Institute for Metabolic and Functional Imaging (GIfMI), Ghent University, Ghent, Belgium
| | - Thomas Booth
- School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas' Hospital, London, SE1 7EH UK.,Department of Neuroradiology, King's College Hospital NHS Foundation Trust, London, SE5 9RS UK
| | - Fran Borovečki
- Department of Neurology, University Hospital Centre Zagreb, Zagreb, Croatia
| | - Kyrre E Emblem
- Division of Radiology and Nuclear Medicine, Department of Diagnostic Physics, Oslo University Hospital, Oslo, Norway
| | - Patrícia Figueiredo
- Institute for Systems and Robotics - Lisboa and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Lydiane Hirschler
- Department of Radiology, C.J. Gorter Center for High Field MRI, Leiden University Medical Center, Leiden, The Netherlands
| | - Radim Jančálek
- Department of Neurosurgery, St. Anne's University Hospital and Medical Faculty, Masaryk University, Brno, Czech Republic
| | - Vera C Keil
- Department of Radiology, Amsterdam University Medical Center, VUmc, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | | | - Yelda Özsunar
- Department of Radiology, Faculty of Medicine, Adnan Menderes University, Aydın, Turkey
| | - Cyril Pernet
- Centre for Clinical Brain Sciences & Edinburgh Imaging, University of Edinburgh, Edinburgh, UK
| | - Jan Petr
- Institute of Radiopharmaceutical Cancer Research, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany
| | - Joana Pinto
- Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, UK
| | - Marion Smits
- Department of Radiology & Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Esther A H Warnert
- Department of Radiology & Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
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153
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Schell M, Pflüger I, Brugnara G, Isensee F, Neuberger U, Foltyn M, Kessler T, Sahm F, Wick A, Nowosielski M, Heiland S, Weller M, Platten M, Maier-Hein KH, Von Deimling A, Van Den Bent MJ, Gorlia T, Wick W, Bendszus M, Kickingereder P. Validation of diffusion MRI phenotypes for predicting response to bevacizumab in recurrent glioblastoma: post-hoc analysis of the EORTC-26101 trial. Neuro Oncol 2020; 22:1667-1676. [PMID: 32393964 PMCID: PMC7690360 DOI: 10.1093/neuonc/noaa120] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND This study validated a previously described diffusion MRI phenotype as a potential predictive imaging biomarker in patients with recurrent glioblastoma receiving bevacizumab (BEV). METHODS A total of 396/596 patients (66%) from the prospective randomized phase II/III EORTC-26101 trial (with n = 242 in the BEV and n = 154 in the non-BEV arm) met the inclusion criteria with availability of anatomical and diffusion MRI sequences at baseline prior treatment. Apparent diffusion coefficient (ADC) histograms from the contrast-enhancing tumor volume were fitted to a double Gaussian distribution and the mean of the lower curve (ADClow) was used for further analysis. The predictive ability of ADClow was assessed with biomarker threshold models and multivariable Cox regression for overall survival (OS) and progression-free survival (PFS). RESULTS ADClow was associated with PFS (hazard ratio [HR] = 0.625, P = 0.007) and OS (HR = 0.656, P = 0.031). However, no (predictive) interaction between ADClow and the treatment arm was present (P = 0.865 for PFS, P = 0.722 for OS). Independent (prognostic) significance of ADClow was retained after adjusting for epidemiological, clinical, and molecular characteristics (P ≤ 0.02 for OS, P ≤ 0.01 PFS). The biomarker threshold model revealed an optimal ADClow cutoff of 1241*10-6 mm2/s for OS. Thereby, median OS for BEV-patients with ADClow ≥ 1241 was 10.39 months versus 8.09 months for those with ADClow < 1241 (P = 0.004). Similarly, median OS for non-BEV patients with ADClow ≥ 1241 was 9.80 months versus 7.79 months for those with ADClow < 1241 (P = 0.054). CONCLUSIONS ADClow is an independent prognostic parameter for stratifying OS and PFS in patients with recurrent glioblastoma. Consequently, the previously suggested role of ADClow as predictive imaging biomarker could not be confirmed within this phase II/III trial.
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Affiliation(s)
- Marianne Schell
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Irada Pflüger
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Gianluca Brugnara
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Fabian Isensee
- Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
| | - Ulf Neuberger
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Martha Foltyn
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Tobias Kessler
- Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany
- Clinical Cooperation Unit Neurooncology, German Cancer Research Center, Heidelberg, Germany
| | - Felix Sahm
- Department of Neuropathology, Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany
- Clinical Cooperation Unit Neuropathology, German Cancer Research Center, Heidelberg, Germany
| | - Antje Wick
- Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany
| | - Martha Nowosielski
- Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany
- Clinical Cooperation Unit Neuropathology, German Cancer Research Center, Heidelberg, Germany
- Department of Neurology, Medical University, Innsbruck, Austria
| | - Sabine Heiland
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Michael Weller
- Department of Neurology, University Hospital and University of Zurich, Zurich, Switzerland
| | - Michael Platten
- Department of Neurology, Mannheim Medical Center, University of Heidelberg, Mannheim, Germany
| | - Klaus H Maier-Hein
- Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
- Department of Neurology, Mannheim Medical Center, University of Heidelberg, Mannheim, Germany
| | - Andreas Von Deimling
- Department of Neuropathology, Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany
- Clinical Cooperation Unit Neuropathology, German Cancer Research Center, Heidelberg, Germany
| | | | - Thierry Gorlia
- European Organisation for Research and Treatment of Cancer, Brussels, Belgium
| | - Wolfgang Wick
- Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany
- Clinical Cooperation Unit Neurooncology, German Cancer Research Center, Heidelberg, Germany
- Clinical Cooperation Unit Neuropathology, German Cancer Research Center, Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Philipp Kickingereder
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
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154
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Sequential implementation of DSC-MR perfusion and dynamic [ 18F]FET PET allows efficient differentiation of glioma progression from treatment-related changes. Eur J Nucl Med Mol Imaging 2020; 48:1956-1965. [PMID: 33241456 PMCID: PMC8113145 DOI: 10.1007/s00259-020-05114-0] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Accepted: 11/08/2020] [Indexed: 11/21/2022]
Abstract
Purpose Perfusion-weighted MRI (PWI) and O-(2-[18F]fluoroethyl-)-l-tyrosine ([18F]FET) PET are both applied to discriminate tumor progression (TP) from treatment-related changes (TRC) in patients with suspected recurrent glioma. While the combination of both methods has been reported to improve the diagnostic accuracy, the performance of a sequential implementation has not been further investigated. Therefore, we retrospectively analyzed the diagnostic value of consecutive PWI and [18F]FET PET. Methods We evaluated 104 patients with WHO grade II–IV glioma and suspected TP on conventional MRI using PWI and dynamic [18F]FET PET. Leakage corrected maximum relative cerebral blood volumes (rCBVmax) were obtained from dynamic susceptibility contrast PWI. Furthermore, we calculated static (i.e., maximum tumor to brain ratios; TBRmax) and dynamic [18F]FET PET parameters (i.e., Slope). Definitive diagnoses were based on histopathology (n = 42) or clinico-radiological follow-up (n = 62). The diagnostic performance of PWI and [18F]FET PET parameters to differentiate TP from TRC was evaluated by analyzing receiver operating characteristic and area under the curve (AUC). Results Across all patients, the differentiation of TP from TRC using rCBVmax or [18F]FET PET parameters was moderate (AUC = 0.69–0.75; p < 0.01). A rCBVmax cutoff > 2.85 had a positive predictive value for TP of 100%, enabling a correct TP diagnosis in 44 patients. In the remaining 60 patients, combined static and dynamic [18F]FET PET parameters (TBRmax, Slope) correctly discriminated TP and TRC in a significant 78% of patients, increasing the overall accuracy to 87%. A subgroup analysis of isocitrate dehydrogenase (IDH) mutant tumors indicated a superior performance of PWI to [18F]FET PET (AUC = 0.8/< 0.62, p < 0.01/≥ 0.3). Conclusion While marked hyperperfusion on PWI indicated TP, [18F]FET PET proved beneficial to discriminate TP from TRC when PWI remained inconclusive. Thus, our results highlight the clinical value of sequential use of PWI and [18F]FET PET, allowing an economical use of diagnostic methods. The impact of an IDH mutation needs further investigation. Supplementary Information The online version contains supplementary material available at 10.1007/s00259-020-05114-0.
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155
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Yao J, Chakhoyan A, Nathanson DA, Yong WH, Salamon N, Raymond C, Mareninov S, Lai A, Nghiemphu PL, Prins RM, Pope WB, Everson RG, Liau LM, Cloughesy TF, Ellingson BM. Metabolic characterization of human IDH mutant and wild type gliomas using simultaneous pH- and oxygen-sensitive molecular MRI. Neuro Oncol 2020; 21:1184-1196. [PMID: 31066901 DOI: 10.1093/neuonc/noz078] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Isocitrate dehydrogenase 1 (IDH1) mutant gliomas are thought to have distinct metabolic characteristics, including a blunted response to hypoxia and lower glycolytic flux. We hypothesized that non-invasive quantification of abnormal metabolic behavior in human IDH1 mutant gliomas could be performed using a new pH- and oxygen-sensitive molecular MRI technique. METHODS Simultaneous pH- and oxygen-sensitive MRI was obtained at 3T using amine CEST-SAGE-EPI. The pH-dependent measure of the magnetization transfer ratio asymmetry (MTRasym) at 3 ppm and oxygen-sensitive measure of R2' were quantified in 90 patients with gliomas. Additionally, stereotactic, image-guided biopsies were performed in 20 patients for a total of 52 samples. The association between imaging measurements and hypoxia-inducible factor 1 alpha (HIF1α) expression was identified using Pearson correlation analysis. RESULTS IDH1 mutant gliomas exhibited significantly lower MTRasym at 3 ppm, R2', and MTRasymxR2' (P = 0.007, P = 0.003, and P = 0.001, respectively). MTRasymxR2' could identify IDH1 mutant gliomas with a high sensitivity (81.0%) and specificity (81.3%). HIF1α was positively correlated with MTRasym at 3 ppm, R2' and MTRasymxR2' in IDH1 wild type (r = 0.610, P = 0.003; r = 0.667, P = 0.008; r = 0.635, P = 0.006), but only MTRasymxR2' in IDH1 mutant gliomas (r = 0.727, P = 0.039). CONCLUSIONS IDH1 mutant gliomas have distinct metabolic and microenvironment characteristics compared with wild type gliomas. An imaging biomarker combining tumor acidity and hypoxia (MTRasymxR2') can differentiate IDH1 mutation status and is correlated with tumor acidity and hypoxia.
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Affiliation(s)
- Jingwen Yao
- UCLA Brain Tumor Imaging Laboratory, Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, Los Angeles, California.,Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California.,Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California Los Angeles, Los Angeles, California
| | - Ararat Chakhoyan
- UCLA Brain Tumor Imaging Laboratory, Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, Los Angeles, California.,Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California
| | - David A Nathanson
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California
| | - William H Yong
- Department of Pathology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California
| | - Noriko Salamon
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California
| | - Catalina Raymond
- UCLA Brain Tumor Imaging Laboratory, Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, Los Angeles, California.,Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California
| | - Sergey Mareninov
- Department of Pathology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California
| | - Albert Lai
- UCLA Neuro-Oncology Program, University of California Los Angeles, Los Angeles, California.,Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California
| | - Phioanh L Nghiemphu
- UCLA Neuro-Oncology Program, University of California Los Angeles, Los Angeles, California.,Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California
| | - Robert M Prins
- Department of Neurosurgery, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California
| | - Whitney B Pope
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California
| | - Richard G Everson
- Department of Neurosurgery, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California
| | - Linda M Liau
- Department of Neurosurgery, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California
| | - Timothy F Cloughesy
- UCLA Neuro-Oncology Program, University of California Los Angeles, Los Angeles, California.,Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California
| | - Benjamin M Ellingson
- UCLA Brain Tumor Imaging Laboratory, Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, Los Angeles, California.,Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California.,Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California Los Angeles, Los Angeles, California.,UCLA Neuro-Oncology Program, University of California Los Angeles, Los Angeles, California
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156
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Shankar A, Bomanji J, Hyare H. Hybrid PET-MRI Imaging in Paediatric and TYA Brain Tumours: Clinical Applications and Challenges. J Pers Med 2020; 10:jpm10040218. [PMID: 33182433 PMCID: PMC7711629 DOI: 10.3390/jpm10040218] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2020] [Revised: 10/29/2020] [Accepted: 11/03/2020] [Indexed: 12/12/2022] Open
Abstract
(1) Background: Standard magnetic resonance imaging (MRI) remains the gold standard for brain tumour imaging in paediatric and teenage and young adult (TYA) patients. Combining positron emission tomography (PET) with MRI offers an opportunity to improve diagnostic accuracy. (2) Method: Our single-centre experience of 18F-fluorocholine (FCho) and 18fluoro-L-phenylalanine (FDOPA) PET–MRI in paediatric/TYA neuro-oncology patients is presented. (3) Results: Hybrid PET–MRI shows promise in the evaluation of gliomas and germ cell tumours in (i) assessing early treatment response and (ii) discriminating tumour from treatment-related changes. (4) Conclusions: Combined PET–MRI shows promise for improved diagnostic and therapeutic assessment in paediatric and TYA brain tumours.
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Affiliation(s)
- Ananth Shankar
- Children and Young People’s Cancer Services, University College London hospitals NHS Foundation Trust, London NW1 2PG, UK
- Correspondence: ; Tel.: +44-20-3447-9950
| | - Jamshed Bomanji
- Department of Nuclear Medicine, University College London hospitals NHS Foundation Trust, London NW1 2PG, UK;
| | - Harpreet Hyare
- Department of Radiology, University College London Hospitals NHS Foundation Trust, London NW1 2PG, UK;
- Department of Brain Repair and Rehabilitation, Institute of Neurology, University College London, London WC1N 3BG, UK
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157
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Goncalves Filho ALM, Conklin J, Longo MGF, Cauley SF, Polak D, Liu W, Splitthoff DN, Lo WC, Kirsch JE, Setsompop K, Schaefer PW, Huang SY, Rapalino O. Accelerated Post-contrast Wave-CAIPI T1 SPACE Achieves Equivalent Diagnostic Performance Compared With Standard T1 SPACE for the Detection of Brain Metastases in Clinical 3T MRI. Front Neurol 2020; 11:587327. [PMID: 33193054 PMCID: PMC7653188 DOI: 10.3389/fneur.2020.587327] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2020] [Accepted: 09/30/2020] [Indexed: 12/12/2022] Open
Abstract
Background and Purpose: Brain magnetic resonance imaging (MRI) examinations using high-resolution 3D post-contrast sequences offer increased sensitivity for the detection of metastases in the central nervous system but are usually long exams. We evaluated whether the diagnostic performance of a highly accelerated Wave-controlled aliasing in parallel imaging (Wave-CAIPI) post-contrast 3D T1 SPACE sequence was non-inferior to the standard high-resolution 3D T1 SPACE sequence for the evaluation of brain metastases. Materials and Methods: Thirty-three patients undergoing evaluation for brain metastases were prospectively evaluated with a standard post-contrast 3D T1 SPACE sequence and an optimized Wave-CAIPI 3D T1 SPACE sequence, which was three times faster than the standard sequence. Two blinded neuroradiologists performed a head-to-head comparison to evaluate the visualization of pathology, perception of artifacts, and the overall diagnostic quality. Wave-CAIPI post-contrast T1 SPACE was tested for non-inferiority relative to standard T1 SPACE using a 15% non-inferiority margin. Results: Wave-CAIPI post-contrast T1 SPACE was non-inferior to the standard T1 SPACE for visualization of enhancing lesions (P < 0.01) and offered equivalent diagnostic quality performance and only marginally higher background noise compared to the standard sequence. Conclusions: Our findings suggest that Wave-CAIPI post-contrast T1 SPACE provides equivalent visualization of pathology and overall diagnostic quality with three times reduced scan time compared to the standard 3D T1 SPACE.
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Affiliation(s)
- Augusto Lio M Goncalves Filho
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States.,Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States.,Harvard Medical School, Boston, MA, United States
| | - John Conklin
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States.,Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States.,Harvard Medical School, Boston, MA, United States
| | - Maria Gabriela F Longo
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States.,Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States.,Harvard Medical School, Boston, MA, United States
| | - Stephen F Cauley
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States.,Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States.,Harvard Medical School, Boston, MA, United States
| | - Daniel Polak
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States.,Siemens Healthcare GmbH, Erlangen, Germany
| | - Wei Liu
- Siemens Shenzhen Magnetic Resonance Ltd., Shenzhen, China
| | | | - Wei-Ching Lo
- Siemens Medical Solutions, Boston, MA, United States
| | - John E Kirsch
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States.,Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States.,Harvard Medical School, Boston, MA, United States
| | - Kawin Setsompop
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States.,Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States.,Harvard Medical School, Boston, MA, United States.,Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA, United States
| | - Pamela W Schaefer
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States.,Harvard Medical School, Boston, MA, United States
| | - Susie Y Huang
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States.,Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States.,Harvard Medical School, Boston, MA, United States.,Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA, United States
| | - Otto Rapalino
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States.,Harvard Medical School, Boston, MA, United States
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158
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Implementation of a dedicated 1.5 T MR scanner for radiotherapy treatment planning featuring a novel high-channel coil setup for brain imaging in treatment position. Strahlenther Onkol 2020; 197:246-256. [PMID: 33103231 PMCID: PMC7892740 DOI: 10.1007/s00066-020-01703-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Accepted: 09/29/2020] [Indexed: 12/17/2022]
Abstract
Purpose To share our experiences in implementing a dedicated magnetic resonance (MR) scanner for radiotherapy (RT) treatment planning using a novel coil setup for brain imaging in treatment position as well as to present developed core protocols with sequences specifically tuned for brain and prostate RT treatment planning. Materials and methods Our novel setup consists of two large 18-channel flexible coils and a specifically designed wooden mask holder mounted on a flat tabletop overlay, which allows patients to be measured in treatment position with mask immobilization. The signal-to-noise ratio (SNR) of this setup was compared to the vendor-provided flexible coil RT setup and the standard setup for diagnostic radiology. The occurrence of motion artifacts was quantified. To develop magnetic resonance imaging (MRI) protocols, we formulated site- and disease-specific clinical objectives. Results Our novel setup showed mean SNR of 163 ± 28 anteriorly, 104 ± 23 centrally, and 78 ± 14 posteriorly compared to 84 ± 8 and 102 ± 22 anteriorly, 68 ± 6 and 95 ± 20 centrally, and 56 ± 7 and 119 ± 23 posteriorly for the vendor-provided and diagnostic setup, respectively. All differences were significant (p > 0.05). Image quality of our novel setup was judged suitable for contouring by expert-based assessment. Motion artifacts were found in 8/60 patients in the diagnostic setup, whereas none were found for patients in the RT setup. Site-specific core protocols were designed to minimize distortions while optimizing tissue contrast and 3D resolution according to indication-specific objectives. Conclusion We present a novel setup for high-quality imaging in treatment position that allows use of several immobilization systems enabling MR-only workflows, which could reduce unnecessary dose and registration inaccuracies. Electronic supplementary material The online version of this article (10.1007/s00066-020-01703-y) contains supplementary material, which is available to authorized users.
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159
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Oughourlian TC, Yao J, Hagiwara A, Nathanson DA, Raymond C, Pope WB, Salamon N, Lai A, Ji M, Nghiemphu PL, Liau LM, Cloughesy TF, Ellingson BM. Relative oxygen extraction fraction (rOEF) MR imaging reveals higher hypoxia in human epidermal growth factor receptor (EGFR) amplified compared with non-amplified gliomas. Neuroradiology 2020; 63:857-868. [PMID: 33106922 DOI: 10.1007/s00234-020-02585-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Accepted: 10/13/2020] [Indexed: 10/23/2022]
Abstract
PURPOSE Epidermal growth factor receptor (EGFR) amplification promotes gliomagenesis and is linked to lack of oxygen within the tumor microenvironment. Using hypoxia-sensitive spin-and-gradient echo echo-planar imaging and perfusion MRI, we investigated the influence of EGFR amplification on tissue oxygen availability and utilization in human gliomas. METHODS This study included 72 histologically confirmed EGFR-amplified and non-amplified glioma patients. Reversible transverse relaxation rate (R2'), relative cerebral blood volume (rCBV), and relative oxygen extraction fraction (rOEF) were calculated for the contrast-enhancing and non-enhancing tumor regions. Using Student t test or Wilcoxon rank-sum test, median R2', rCBV, and rOEF were compared between EGFR-amplified and non-amplified gliomas. ROC analysis was performed to assess the ability of imaging characteristics to discriminate EGFR amplification status. Overall survival (OS) was determined using univariate and multivariate cox models. Kaplan-Meier survival curves were plotted and compared using the log-rank test. RESULTS EGFR amplified gliomas exhibited significantly higher median R2' and rOEF than non-amplified gliomas. ROC analysis suggested that R2' (AUC = 0.7190; P = 0.0048) and rOEF (AUC = 0.6959; P = 0.0156) could separate EGFR status. Patients with EGFR-amplified gliomas had a significantly shorter OS than non-amplified patients. Univariate cox regression analysis determined both R2' and rOEF significantly influence OS. No significant difference was observed in rCBV between patient cohorts nor was rCBV found to be an effective differentiator of EGFR status. CONCLUSION Imaging of tumor oxygen characteristics revealed EGFR-amplified gliomas to be more hypoxic and contribute to shorter patient survival than EGFR non-amplified gliomas.
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Affiliation(s)
- Talia C Oughourlian
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.,Neuroscience Interdepartmental Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Jingwen Yao
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.,Department of Bioengineering, Henry Samueli School of Engineering, University of California Los Angeles, Los Angeles, CA, USA
| | - Akifumi Hagiwara
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, 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, 924 Westwood Blvd., Suite 615, Los Angeles, CA, 90024, USA
| | - David A Nathanson
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Catalina Raymond
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, 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, 924 Westwood Blvd., Suite 615, Los Angeles, CA, 90024, USA
| | - Whitney B Pope
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, 924 Westwood Blvd., Suite 615, Los Angeles, CA, 90024, USA
| | - Noriko Salamon
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, 924 Westwood Blvd., Suite 615, Los Angeles, CA, 90024, USA
| | - Albert Lai
- UCLA Neuro-Oncology Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.,Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Matthew Ji
- UCLA Neuro-Oncology Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.,Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Phioanh L Nghiemphu
- UCLA Neuro-Oncology Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.,Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Linda M Liau
- Department of Neurosurgery, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Timothy F Cloughesy
- UCLA Neuro-Oncology Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.,Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Benjamin M Ellingson
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, 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, 924 Westwood Blvd., Suite 615, Los Angeles, CA, 90024, USA.
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160
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Lombardi G, Barresi V, Castellano A, Tabouret E, Pasqualetti F, Salvalaggio A, Cerretti G, Caccese M, Padovan M, Zagonel V, Ius T. Clinical Management of Diffuse Low-Grade Gliomas. Cancers (Basel) 2020; 12:E3008. [PMID: 33081358 PMCID: PMC7603014 DOI: 10.3390/cancers12103008] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Revised: 10/06/2020] [Accepted: 10/14/2020] [Indexed: 12/21/2022] Open
Abstract
Diffuse low-grade gliomas (LGG) represent a heterogeneous group of primary brain tumors arising from supporting glial cells and usually affecting young adults. Advances in the knowledge of molecular profile of these tumors, including mutations in the isocitrate dehydrogenase genes, or 1p/19q codeletion, and in neuroradiological techniques have contributed to the diagnosis, prognostic stratification, and follow-up of these tumors. Optimal post-operative management of LGG is still controversial, though radiation therapy and chemotherapy remain the optimal treatments after surgical resection in selected patients. In this review, we report the most important and recent research on clinical and molecular features, new neuroradiological techniques, the different therapeutic modalities, and new opportunities for personalized targeted therapy and supportive care.
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Affiliation(s)
- Giuseppe Lombardi
- Department of Oncology, Oncology 1, Veneto Institute of oncology-IRCCS, 35128 Padova, Italy; (G.C.); (M.C.); (M.P.); (V.Z.)
| | - Valeria Barresi
- Department of Diagnostics and Public Health, Section of Pathology, University of Verona, 37129 Verona, Italy;
| | - Antonella Castellano
- Neuroradiology Unit, IRCCS San Raffaele Scientific Institute and Vita-Salute San Raffaele University, 20132 Milan, Italy;
| | - Emeline Tabouret
- Team 8 GlioMe, CNRS, INP, Inst Neurophysiopathol, Aix-Marseille University, 13005 Marseille, France;
| | | | - Alessandro Salvalaggio
- Department of Neuroscience, University of Padova, 35128 Padova, Italy;
- Padova Neuroscience Center (PNC), University of Padova, 35128 Padova, Italy
| | - Giulia Cerretti
- Department of Oncology, Oncology 1, Veneto Institute of oncology-IRCCS, 35128 Padova, Italy; (G.C.); (M.C.); (M.P.); (V.Z.)
| | - Mario Caccese
- Department of Oncology, Oncology 1, Veneto Institute of oncology-IRCCS, 35128 Padova, Italy; (G.C.); (M.C.); (M.P.); (V.Z.)
| | - Marta Padovan
- Department of Oncology, Oncology 1, Veneto Institute of oncology-IRCCS, 35128 Padova, Italy; (G.C.); (M.C.); (M.P.); (V.Z.)
| | - Vittorina Zagonel
- Department of Oncology, Oncology 1, Veneto Institute of oncology-IRCCS, 35128 Padova, Italy; (G.C.); (M.C.); (M.P.); (V.Z.)
| | - Tamara Ius
- Neurosurgery Unit, Department of Neurosciences, Santa Maria della Misericordia University Hospital, 33100 Udine, Italy;
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161
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Boxerman JL, Quarles CC, Hu LS, Erickson BJ, Gerstner ER, Smits M, Kaufmann TJ, Barboriak DP, Huang RH, Wick W, Weller M, Galanis E, Kalpathy-Cramer J, Shankar L, Jacobs P, Chung C, van den Bent MJ, Chang S, Al Yung WK, Cloughesy TF, Wen PY, Gilbert MR, Rosen BR, Ellingson BM, Schmainda KM. Consensus recommendations for a dynamic susceptibility contrast MRI protocol for use in high-grade gliomas. Neuro Oncol 2020; 22:1262-1275. [PMID: 32516388 PMCID: PMC7523451 DOI: 10.1093/neuonc/noaa141] [Citation(s) in RCA: 112] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Despite the widespread clinical use of dynamic susceptibility contrast (DSC) MRI, DSC-MRI methodology has not been standardized, hindering its utilization for response assessment in multicenter trials. Recently, the DSC-MRI Standardization Subcommittee of the Jumpstarting Brain Tumor Drug Development Coalition issued an updated consensus DSC-MRI protocol compatible with the standardized brain tumor imaging protocol (BTIP) for high-grade gliomas that is increasingly used in the clinical setting and is the default MRI protocol for the National Clinical Trials Network. After reviewing the basis for controversy over DSC-MRI protocols, this paper provides evidence-based best practices for clinical DSC-MRI as determined by the Committee, including pulse sequence (gradient echo vs spin echo), BTIP-compliant contrast agent dosing (preload and bolus), flip angle (FA), echo time (TE), and post-processing leakage correction. In summary, full-dose preload, full-dose bolus dosing using intermediate (60°) FA and field strength-dependent TE (40-50 ms at 1.5 T, 20-35 ms at 3 T) provides overall best accuracy and precision for cerebral blood volume estimates. When single-dose contrast agent usage is desired, no-preload, full-dose bolus dosing using low FA (30°) and field strength-dependent TE provides excellent performance, with reduced contrast agent usage and elimination of potential systematic errors introduced by variations in preload dose and incubation time.
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Affiliation(s)
- Jerrold L Boxerman
- Department of Diagnostic Imaging, Warren Alpert Medical School, Brown University, Providence, Rhode Island, USA
- Representative of the Eastern Cooperative Oncology Group–American College of Radiology Imaging Network (ECOG-ACRIN) Cancer Research Group
- Representative of the American Society of Neuroradiology (ASNR)
- Representative of the American Society of Functional Neuroradiology (ASFNR)
| | - Chad C Quarles
- Department of Neuroimaging Research and Barrow Neuroimaging Innovation Center, Barrow Neurological Institute, Phoenix, Arizona, USA
| | - Leland S Hu
- Department of Radiology, Mayo Clinic, Phoenix, Arizona, USA
- Representative of the Alliance for Clinical Trials in Oncology
- Representative of the American Society of Neuroradiology (ASNR)
| | - Bradley J Erickson
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
- Representative of the Alliance for Clinical Trials in Oncology
- Representative of the RSNA Quantitative Imaging Biomarker Alliance (QIBA)
- Representative of the American Society of Neuroradiology (ASNR)
| | - Elizabeth R Gerstner
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Representative of the Adult Brain Tumor Consortium (ABTC)
| | - Marion Smits
- Department of Radiology and Nuclear Medicine, Erasmus MC–University Medical Center Rotterdam, Rotterdam, Netherlands
- Representative of the European Organisation for Research and Treatment of Cancer (EORTC)
| | - Timothy J Kaufmann
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
- Representative of the Alliance for Clinical Trials in Oncology
| | - Daniel P Barboriak
- Department of Radiology, Duke University School of Medicine, Durham, North Carolina, USA
- Representative of the Eastern Cooperative Oncology Group–American College of Radiology Imaging Network (ECOG-ACRIN) Cancer Research Group
- Representative of the RSNA Quantitative Imaging Biomarker Alliance (QIBA)
- Representative of the American Society of Neuroradiology (ASNR)
| | - Raymond H Huang
- Department of Radiology, Brigham and Women’s Hospital, Boston, Massachusetts, USA
- Center for Neuro-Oncology, Dana-Farber/Brigham and Women’s Cancer Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Wolfgang Wick
- Department of Neurooncology, National Center of Tumor Disease, University Clinic Heidelberg, Heidelberg, Germany
- Representative of the European Organisation for Research and Treatment of Cancer (EORTC)
| | - Michael Weller
- Department of Neurology, University Hospital and University of Zurich, Zurich, Switzerland
- Representative of the European Organisation for Research and Treatment of Cancer (EORTC)
| | - Evanthia Galanis
- Division of Medical Oncology, Department of Oncology, Mayo Clinic, Rochester, Minnesota, USA
- Representative of the Alliance for Clinical Trials in Oncology
| | - Jayashree Kalpathy-Cramer
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Lalitha Shankar
- Division of Cancer Treatment and Diagnosis, National Cancer Institute (NCI), Bethesda, Maryland, USA
| | - Paula Jacobs
- Division of Cancer Treatment and Diagnosis, National Cancer Institute (NCI), Bethesda, Maryland, USA
| | - Caroline Chung
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
- Representative of the Alliance for Clinical Trials in Oncology
| | - Martin J van den Bent
- Department of Neuro-Oncology, Erasmus MC Cancer Institute, Rotterdam, Netherlands
- Representative of the European Organisation for Research and Treatment of Cancer (EORTC)
| | - Susan Chang
- Department of Neurological Surgery, University of California San Francisco, San Francisco, California, USA
| | - W K Al Yung
- Department of Neuro-Oncology, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Timothy F Cloughesy
- UCLA Neuro-Oncology Program and UCLA Brain Tumor Imaging Laboratory (BTIL), David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Patrick Y Wen
- Center for Neuro-Oncology, Dana-Farber/Brigham and Women’s Cancer Center, Harvard Medical School, Boston, Massachusetts, USA
- Representative of the Adult Brain Tumor Consortium (ABTC)
| | - Mark R Gilbert
- Neuro-Oncology Branch, National Cancer Institute (NCI), Bethesda, Maryland, USA
- Representative of the Radiation Therapy Oncology Group (RTOG)
| | - Bruce R Rosen
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Benjamin M Ellingson
- UCLA Neuro-Oncology Program and UCLA Brain Tumor Imaging Laboratory (BTIL), David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
- Departments of Radiological Sciences, Psychiatry, and Biobehavioral Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
- Representative of the Adult Brain Tumor Consortium (ABTC)
- Representative of the Ivy Consortium for Early Phase Clinical Trials
- Representative of the Eastern Cooperative Oncology Group–American College of Radiology Imaging Network (ECOG-ACRIN) Cancer Research Group
- Representative of the RSNA Quantitative Imaging Biomarker Alliance (QIBA)
- Representative of the American Society of Neuroradiology (ASNR)
| | - Kathleen M Schmainda
- Department of Biophysics, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
- Representative of the Eastern Cooperative Oncology Group–American College of Radiology Imaging Network (ECOG-ACRIN) Cancer Research Group
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162
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Machine Learning Model to Predict Pseudoprogression Versus Progression in Glioblastoma Using MRI: A Multi-Institutional Study (KROG 18-07). Cancers (Basel) 2020; 12:cancers12092706. [PMID: 32967367 PMCID: PMC7564954 DOI: 10.3390/cancers12092706] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Revised: 09/16/2020] [Accepted: 09/17/2020] [Indexed: 01/28/2023] Open
Abstract
Simple Summary Even after the introduction of a standard regimen consisting of concurrent chemoradiotherapy and adjuvant temozolomide, patients with glioblastoma multiforme mostly experience disease progression. Clinicians often encounter a situation where they need to distinguish progressive disease from pseudoprogression after treatment. We tried to investigate the feasibility of machine learning algorithm to distinguish pseudoprogression from progressive disease. In multi-institutional dataset, the developed machine learning model showed an acceptable performance. This algorithm involving MRI data and clinical features could help making decision during patients’ disease course. For the practical use, we calibrated the machine learning model to offer the probability of pseudoprogression to clinicians, then we constructed the web-based user interface to access the model. Abstract Some patients with glioblastoma show a worsening presentation in imaging after concurrent chemoradiation, even when they receive gross total resection. Previously, we showed the feasibility of a machine learning model to predict pseudoprogression (PsPD) versus progressive disease (PD) in glioblastoma patients. The previous model was based on the dataset from two institutions (termed as the Seoul National University Hospital (SNUH) dataset, N = 78). To test this model in a larger dataset, we collected cases from multiple institutions that raised the problem of PsPD vs. PD diagnosis in clinics (Korean Radiation Oncology Group (KROG) dataset, N = 104). The dataset was composed of brain MR images and clinical information. We tested the previous model in the KROG dataset; however, that model showed limited performance. After hyperparameter optimization, we developed a deep learning model based on the whole dataset (N = 182). The 10-fold cross validation revealed that the micro-average area under the precision-recall curve (AUPRC) was 0.86. The calibration model was constructed to estimate the interpretable probability directly from the model output. After calibration, the final model offers clinical probability in a web-user interface.
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163
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Young RJ, Demétrio De Souza França P, Pirovano G, Piotrowski AF, Nicklin PJ, Riedl CC, Schwartz J, Bale TA, Donabedian PL, Kossatz S, Burnazi EM, Roberts S, Lyashchenko SK, Miller AM, Moss NS, Fiasconaro M, Zhang Z, Mauguen A, Reiner T, Dunphy MP. Preclinical and first-in-human-brain-cancer applications of [ 18F]poly (ADP-ribose) polymerase inhibitor PET/MR. Neurooncol Adv 2020; 2:vdaa119. [PMID: 33392502 PMCID: PMC7758909 DOI: 10.1093/noajnl/vdaa119] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Background We report preclinical and first-in-human-brain-cancer data using a targeted poly (ADP-ribose) polymerase 1 (PARP1) binding PET tracer, [18F]PARPi, as a diagnostic tool to differentiate between brain cancers and treatment-related changes. Methods We applied a glioma model in p53-deficient nestin/tv-a mice, which were injected with [18F]PARPi and then sacrificed 1 h post-injection for brain examination. We also prospectively enrolled patients with brain cancers to undergo dynamic [18F]PARPi acquisition on a dedicated positron emission tomography/magnetic resonance (PET/MR) scanner. Lesion diagnosis was established by pathology when available or by Response Assessment in Neuro-Oncology (RANO) or RANO-BM response criteria. Resected tissue also underwent PARPi-FL staining and PARP1 immunohistochemistry. Results In a preclinical mouse model, we illustrated that [18F]PARPi crossed the blood–brain barrier and specifically bound to PARP1 overexpressed in cancer cell nuclei. In humans, we demonstrated high [18F]PARPi uptake on PET/MR in active brain cancers and low uptake in treatment-related changes independent of blood–brain barrier disruption. Immunohistochemistry results confirmed higher PARP1 expression in cancerous than in noncancerous tissue. Specificity was also corroborated by blocking fluorescent tracer uptake with an excess unlabeled PARP inhibitor in patient cancer biospecimen. Conclusions Although larger studies are necessary to confirm and further explore this tracer, we describe the promising performance of [18F]PARPi as a diagnostic tool to evaluate patients with brain cancers and possible treatment-related changes.
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Affiliation(s)
- Robert J Young
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA.,The Brain Tumor Center, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Paula Demétrio De Souza França
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA.,Department of Otorhinolaryngology and Head and Neck Surgery, Federal University of São Paulo, São Paulo, Brazil
| | - Giacomo Pirovano
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Anna F Piotrowski
- Department of Neurology, Memorial Sloan Kettering Cancer Center, New York, New York, USA.,The Brain Tumor Center, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Philip J Nicklin
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Christopher C Riedl
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Jazmin Schwartz
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA.,Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, New York, USA.,Department of Radiology, Weill Cornell Medical College, New York, New York, USA
| | - Tejus A Bale
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York, USA.,The Brain Tumor Center, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Patrick L Donabedian
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Susanne Kossatz
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Eva M Burnazi
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Sheryl Roberts
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Serge K Lyashchenko
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Alexandra M Miller
- Department of Neurology, Memorial Sloan Kettering Cancer Center, New York, New York, USA.,The Brain Tumor Center, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Nelson S Moss
- Department of Neurosurgery and Brain Metastasis Center, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Megan Fiasconaro
- Department of Biostatistics and Epidemiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Zhigang Zhang
- Department of Biostatistics and Epidemiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Audrey Mauguen
- Department of Biostatistics and Epidemiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Thomas Reiner
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA.,Weill Cornell Medical College, New York, New York, USA.,Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Mark P Dunphy
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
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164
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Decorin expression is associated with predictive diffusion MR phenotypes of anti-VEGF efficacy in glioblastoma. Sci Rep 2020; 10:14819. [PMID: 32908231 PMCID: PMC7481206 DOI: 10.1038/s41598-020-71799-w] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Accepted: 08/04/2020] [Indexed: 12/13/2022] Open
Abstract
Previous data suggest that apparent diffusion coefficient (ADC) imaging phenotypes predict survival response to anti-VEGF monotherapy in glioblastoma. However, the mechanism by which imaging may predict clinical response is unknown. We hypothesize that decorin (DCN), a proteoglycan implicated in the modulation of the extracellular microenvironment and sequestration of pro-angiogenic signaling, may connect ADC phenotypes to survival benefit to anti-VEGF therapy. Patients undergoing resection for glioblastoma as well as patients included in The Cancer Genome Atlas (TCGA) and IVY Glioblastoma Atlas Project (IVY GAP) databases had pre-operative imaging analyzed to calculate pre-operative ADCL values, the average ADC in the lower distribution using a double Gaussian mixed model. ADCL values were correlated to available RNA expression from these databases as well as from RNA sequencing from patient derived mouse orthotopic xenograft samples. Targeted biopsies were selected based on ADC values and prospectively collected during resection. Surgical specimens were used to evaluate for DCN RNA and protein expression by ADC value. The IVY Glioblastoma Atlas Project Database was used to evaluate DCN localization and relationship with VEGF pathway via in situ hybridization maps and RNA sequencing data. In a cohort of 35 patients with pre-operative ADC imaging and surgical specimens, DCN RNA expression levels were significantly larger in high ADCL tumors (41.6 vs. 1.5; P = 0.0081). In a cohort of 17 patients with prospectively targeted biopsies there was a positive linear correlation between ADCL levels and DCN protein expression between tumors (Pearson R2 = 0.3977; P = 0.0066) and when evaluating different targets within the same tumor (Pearson R2 = 0.3068; P = 0.0139). In situ hybridization data localized DCN expression to areas of microvascular proliferation and immunohistochemical studies localized DCN protein expression to the tunica adventitia of blood vessels within the tumor. DCN expression positively correlated with VEGFR1 & 2 expression and localized to similar areas of tumor. Increased ADCL on diffusion MR imaging is associated with high DCN expression as well as increased survival with anti-VEGF therapy in glioblastoma. DCN may play an important role linking the imaging features on diffusion MR and anti-VEGF treatment efficacy. DCN may serve as a target for further investigation and modulation of anti-angiogenic therapy in GBM.
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165
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Galldiks N, Abdulla DSY, Scheffler M, Wolpert F, Werner JM, Hüllner M, Stoffels G, Schweinsberg V, Schlaak M, Kreuzberg N, Landsberg J, Lohmann P, Ceccon G, Baues C, Trommer M, Celik E, Ruge MI, Kocher M, Marnitz S, Fink GR, Tonn JC, Weller M, Langen KJ, Wolf J, Mauch C. Treatment Monitoring of Immunotherapy and Targeted Therapy Using 18F-FET PET in Patients with Melanoma and Lung Cancer Brain Metastases: Initial Experiences. J Nucl Med 2020; 62:464-470. [PMID: 32887757 DOI: 10.2967/jnumed.120.248278] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Accepted: 07/30/2020] [Indexed: 12/15/2022] Open
Abstract
We investigated the value of O-(2-18F-fluoroethyl)-l-tyrosine (18F-FET) PET for treatment monitoring of immune checkpoint inhibition (ICI) or targeted therapy (TT) alone or in combination with radiotherapy in patients with brain metastasis (BM) since contrast-enhanced MRI often remains inconclusive. Methods: We retrospectively identified 40 patients with 107 BMs secondary to melanoma (n = 29 with 75 BMs) or non-small cell lung cancer (n = 11 with 32 BMs) treated with ICI or TT who had 18F-FET PET (n = 60 scans) for treatment monitoring from 2015 to 2019. Most patients (n = 37; 92.5%) had radiotherapy during the course of the disease. In 27 patients, 18F-FET PET was used to differentiate treatment-related changes from BM relapse after ICI or TT. In 13 patients, 18F-FET PET was performed for response assessment to ICI or TT using baseline and follow-up scans (median time between scans, 4.2 mo). In all lesions, static and dynamic 18F-FET PET parameters were obtained (i.e., mean tumor-to-brain ratios [TBR], time-to-peak values). Diagnostic accuracies of PET parameters were evaluated by receiver-operating-characteristic analyses using the clinical follow-up or neuropathologic findings as a reference. Results: A TBR threshold of 1.95 differentiated BM relapse from treatment-related changes with an accuracy of 85% (P = 0.003). Metabolic responders to ICI or TT on 18F-FET PET had a significantly longer stable follow-up (threshold of TBR reduction relative to baseline, ≥10%; accuracy, 82%; P = 0.004). Furthermore, at follow-up, time to peak in metabolic responders increased significantly (P = 0.019). Conclusion: 18F-FET PET may add valuable information for treatment monitoring in BM patients treated with ICI or TT.
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Affiliation(s)
- Norbert Galldiks
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany .,Center of Integrated Oncology, Universities of Aachen, Bonn, Cologne, and Duesseldorf, Germany.,Institute of Neuroscience and Medicine (INM-3, -4), Research Center Juelich, Juelich, Germany
| | - Diana S Y Abdulla
- Center of Integrated Oncology, Universities of Aachen, Bonn, Cologne, and Duesseldorf, Germany.,Lung Cancer Group, Department I of Internal Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Matthias Scheffler
- Center of Integrated Oncology, Universities of Aachen, Bonn, Cologne, and Duesseldorf, Germany.,Lung Cancer Group, Department I of Internal Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Fabian Wolpert
- Department of Neurology and Brain Tumor Center, University Hospital and University of Zurich, Zurich, Switzerland
| | - Jan-Michael Werner
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Martin Hüllner
- Department of Nuclear Medicine, University Hospital and University of Zurich, Zurich, Switzerland
| | - Gabriele Stoffels
- Institute of Neuroscience and Medicine (INM-3, -4), Research Center Juelich, Juelich, Germany
| | - Viola Schweinsberg
- Center of Integrated Oncology, Universities of Aachen, Bonn, Cologne, and Duesseldorf, Germany.,Department of Dermatology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Max Schlaak
- Center of Integrated Oncology, Universities of Aachen, Bonn, Cologne, and Duesseldorf, Germany.,Department of Dermatology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Nicole Kreuzberg
- Center of Integrated Oncology, Universities of Aachen, Bonn, Cologne, and Duesseldorf, Germany.,Department of Dermatology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Jennifer Landsberg
- Center of Integrated Oncology, Universities of Aachen, Bonn, Cologne, and Duesseldorf, Germany.,Department of Dermatology, University Hospital Bonn, Bonn, Germany
| | - Philipp Lohmann
- Institute of Neuroscience and Medicine (INM-3, -4), Research Center Juelich, Juelich, Germany.,Department of Stereotaxy and Functional Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Garry Ceccon
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Christian Baues
- Center of Integrated Oncology, Universities of Aachen, Bonn, Cologne, and Duesseldorf, Germany.,Department of Radiation Oncology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Maike Trommer
- Center of Integrated Oncology, Universities of Aachen, Bonn, Cologne, and Duesseldorf, Germany.,Department of Radiation Oncology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Eren Celik
- Center of Integrated Oncology, Universities of Aachen, Bonn, Cologne, and Duesseldorf, Germany.,Department of Stereotaxy and Functional Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Maximilian I Ruge
- Center of Integrated Oncology, Universities of Aachen, Bonn, Cologne, and Duesseldorf, Germany.,Department of Stereotaxy and Functional Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Martin Kocher
- Institute of Neuroscience and Medicine (INM-3, -4), Research Center Juelich, Juelich, Germany.,Department of Stereotaxy and Functional Neurosurgery, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Simone Marnitz
- Center of Integrated Oncology, Universities of Aachen, Bonn, Cologne, and Duesseldorf, Germany.,Department of Radiation Oncology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Gereon R Fink
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.,Institute of Neuroscience and Medicine (INM-3, -4), Research Center Juelich, Juelich, Germany
| | - Jörg-Christian Tonn
- Department of Neurosurgery, University Hospital LMU Munich, Munich, Germany; and
| | - Michael Weller
- Department of Neurology and Brain Tumor Center, University Hospital and University of Zurich, Zurich, Switzerland
| | - Karl-Josef Langen
- Institute of Neuroscience and Medicine (INM-3, -4), Research Center Juelich, Juelich, Germany.,Department of Nuclear Medicine, RWTH University Hospital Aachen, Aachen, Germany
| | - Jürgen Wolf
- Center of Integrated Oncology, Universities of Aachen, Bonn, Cologne, and Duesseldorf, Germany.,Lung Cancer Group, Department I of Internal Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Cornelia Mauch
- Center of Integrated Oncology, Universities of Aachen, Bonn, Cologne, and Duesseldorf, Germany.,Department of Dermatology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
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166
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Park YW, Ahn SS, Kim EH, Kang SG, Chang JH, Kim SH, Zhou J, Lee SK. Differentiation of recurrent diffuse glioma from treatment-induced change using amide proton transfer imaging: incremental value to diffusion and perfusion parameters. Neuroradiology 2020; 63:363-372. [PMID: 32879995 DOI: 10.1007/s00234-020-02542-5] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2020] [Accepted: 08/26/2020] [Indexed: 02/07/2023]
Abstract
PURPOSE To evaluate the incremental value of amide proton transfer (APT) imaging to diffusion tensor imaging (DTI), dynamic susceptibility contrast (DSC) imaging, and dynamic contrast-enhanced (DCE) imaging in differentiating recurrent diffuse gliomas (World Health Organization grade II-IV) from treatment-induced change after concurrent chemoradiotherapy or radiotherapy. METHODS This study included 36 patients (25 patients with recurrent gliomas and 11 with treatment-induced changes) with post-treatment gliomas. The mean values of apparent diffusion coefficient (ADC), fractional anisotropy (FA), normalized cerebral blood volume (nCBV), normalized cerebral blood flow, volume transfer constant, rate transfer coefficient, extravascular extracellular volume fraction, plasma volume fraction, and APT asymmetry index were assessed. Independent quantitative parameters were investigated to predict recurrent glioma using multivariable logistic regression. The incremental value of APT signal to other parameters was assessed by the increase of the area under the curve, net reclassification index, and integrated discrimination improvement. RESULTS Univariable analysis showed that lower ADC (p = 0.018), higher FA (p = 0.031), higher nCBV (p = 0.021), and higher APT signal (p = 0.009) were associated with recurrent gliomas. In multivariable logistic regression, the diagnostic performance of the model with ADC, FA, and nCBV significantly increased when APT signal was added, with areas under the curve of 0.87 and 0.92, respectively (net reclassification index of 0.77 and integrated discrimination improvement of 0.13). CONCLUSION APT imaging may be a useful imaging biomarker that adds value to DTI, DCE, and DSC parameters for distinguishing between recurrent gliomas and treatment-induced changes.
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Affiliation(s)
- Yae Won Park
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, Korea
| | - Sung Soo Ahn
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, Korea.
| | - Eui Hyun Kim
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Korea
| | - Seok-Gu Kang
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Korea
| | - Jong Hee Chang
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Korea
| | - Se Hoon Kim
- Department of Pathology, Yonsei University College of Medicine, Seoul, Korea
| | - Jinyuan Zhou
- Division of MRI Research, Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Seung-Koo Lee
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, Korea
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167
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Suh CH, Jung SC, Kim B, Cho SJ, Woo DC, Oh WY, Lee JG, Kim KW. Neuroimaging in Randomized, Multi-Center Clinical Trials of Endovascular Treatment for Acute Ischemic Stroke: A Systematic Review. Korean J Radiol 2020; 21:42-57. [PMID: 31920028 PMCID: PMC6960311 DOI: 10.3348/kjr.2019.0354] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2019] [Accepted: 10/01/2019] [Indexed: 01/01/2023] Open
Abstract
Appropriate use and analysis of neuroimaging techniques is an inevitable aspect of clinical trials for patients with acute ischemic stroke. Neuroimaging examinations were recently used to define the core eligibility criteria and outcomes in acute ischemic stroke research. Recent clinical trials for endovascular treatment in acute ischemic stroke have also demonstrated the efficacy or safety of endovascular treatment using various imaging modalities as well as clinical indices. Furthermore, independent imaging reviews and imaging core laboratory assessments are essential to manage and analyze imaging data in order to enhance the reliability of the outcomes. Therefore, we systematically reviewed the use of neuroimaging in recent randomized clinical trials for endovascular treatment of acute ischemic stroke in order to provide a thorough summary, which would serve as a resource guiding the use of appropriate imaging protocols and analyses in future clinical trials for acute ischemic stroke. This review will help researchers select appropriate imaging biomarkers among the various imaging protocols available and apply the selected type of imaging examination for each study in accordance with the academic purpose.
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Affiliation(s)
- Chong Hyun Suh
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Seung Chai Jung
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
| | - Byungjun Kim
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea
| | - Se Jin Cho
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Dong Cheol Woo
- Bioimaging Center, Biomedical Research Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, Korea
| | - Woo Yong Oh
- Clinical Research Division, National Institute of Food and Drug Safety Evaluation, Ministry of Food and Drug Safety, Cheongju, Korea
| | - Jong Gu Lee
- Clinical Research Division, National Institute of Food and Drug Safety Evaluation, Ministry of Food and Drug Safety, Cheongju, Korea
| | - Kyung Won Kim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.,Asan Image Metrics, Clinical Trial Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, Korea
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168
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Conventional MRI features of adult diffuse glioma molecular subtypes: a systematic review. Neuroradiology 2020; 63:353-362. [PMID: 32840682 DOI: 10.1007/s00234-020-02532-7] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Accepted: 08/17/2020] [Indexed: 12/21/2022]
Abstract
PURPOSE Molecular parameters have become integral to glioma diagnosis. Much of radiogenomics research has focused on the use of advanced MRI techniques, but conventional MRI sequences remain the mainstay of clinical assessments. The aim of this research was to synthesize the current published data on the accuracy of standard clinical MRI for diffuse glioma genotyping, specifically targeting IDH and 1p19q status. METHODS A systematic search was performed in September 2019 using PubMed and the Cochrane Library, identifying studies on the diagnostic value of T1 pre-/post-contrast, T2, FLAIR, T2*/SWI and/or 3-directional diffusion-weighted imaging sequences for the prediction of IDH and/or 1p19q status in WHO grade II-IV diffuse astrocytic and oligodendroglial tumours as defined in the WHO 2016 Classification of CNS Tumours. RESULTS Forty-four studies including a total of 5286 patients fulfilled the inclusion criteria. Correlations between key glioma molecular markers, namely IDH and 1p19q, and distinctive MRI findings have been established, including tumour location, signal composition (including the T2-FLAIR mismatch sign) and apparent diffusion coefficient values. CONCLUSION Consistent trends have emerged indicating that conventional MRI is valuable for glioma genotyping, particularly in presumed lower grade glioma. However, due to limited interobserver testing, the reproducibility of qualitatively assessed visual features remains an area of uncertainty.
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169
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Wen PY, Weller M, Lee EQ, Alexander BM, Barnholtz-Sloan JS, Barthel FP, Batchelor TT, Bindra RS, Chang SM, Chiocca EA, Cloughesy TF, DeGroot JF, Galanis E, Gilbert MR, Hegi ME, Horbinski C, Huang RY, Lassman AB, Le Rhun E, Lim M, Mehta MP, Mellinghoff IK, Minniti G, Nathanson D, Platten M, Preusser M, Roth P, Sanson M, Schiff D, Short SC, Taphoorn MJB, Tonn JC, Tsang J, Verhaak RGW, von Deimling A, Wick W, Zadeh G, Reardon DA, Aldape KD, van den Bent MJ. Glioblastoma in adults: a Society for Neuro-Oncology (SNO) and European Society of Neuro-Oncology (EANO) consensus review on current management and future directions. Neuro Oncol 2020; 22:1073-1113. [PMID: 32328653 PMCID: PMC7594557 DOI: 10.1093/neuonc/noaa106] [Citation(s) in RCA: 610] [Impact Index Per Article: 152.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Glioblastomas are the most common form of malignant primary brain tumor and an important cause of morbidity and mortality. In recent years there have been important advances in understanding the molecular pathogenesis and biology of these tumors, but this has not translated into significantly improved outcomes for patients. In this consensus review from the Society for Neuro-Oncology (SNO) and the European Association of Neuro-Oncology (EANO), the current management of isocitrate dehydrogenase wildtype (IDHwt) glioblastomas will be discussed. In addition, novel therapies such as targeted molecular therapies, agents targeting DNA damage response and metabolism, immunotherapies, and viral therapies will be reviewed, as well as the current challenges and future directions for research.
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Affiliation(s)
- Patrick Y Wen
- Dana-Farber Cancer Institute, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Michael Weller
- Department of Neurology and Brain Tumor Center, University Hospital and University of Zurich, Zurich, Switzerland
| | - Eudocia Quant Lee
- Dana-Farber Cancer Institute, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Brian M Alexander
- Dana-Farber Cancer Institute, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Jill S Barnholtz-Sloan
- Case Western Reserve University School of Medicine and University Hospitals of Cleveland, Cleveland, Ohio, USA
| | - Floris P Barthel
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, USA
| | - Tracy T Batchelor
- Department of Neurology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute and Harvard Medical School
| | - Ranjit S Bindra
- Department of Therapeutic Radiology, Yale School of Medicine, New Haven, Connecticut, USA
| | - Susan M Chang
- University of California San Francisco, San Francisco, California, USA
| | - E Antonio Chiocca
- Department of Neurosurgery, Brigham and Women’s Hospital, Dana-Farber Cancer Institute and Harvard Medical School, Boston, Massachusetts, USA
| | - Timothy F Cloughesy
- David Geffen School of Medicine, Department of Neurology, University of California Los Angeles, Los Angeles, California, USA
| | - John F DeGroot
- Department of Neuro-Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | | | - Mark R Gilbert
- Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Monika E Hegi
- Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Craig Horbinski
- Department of Pathology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Raymond Y Huang
- Division of Neuroradiology, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Andrew B Lassman
- Department of Neurology and Herbert Irving Comprehensive Cancer Center, NewYork-Presbyterian Hospital/Columbia University Irving Medical Center, New York, New York, USA
| | - Emilie Le Rhun
- University of Lille, Inserm, Neuro-oncology, General and Stereotaxic Neurosurgery service, University Hospital of Lille, Lille, France; Breast Cancer Department, Oscar Lambret Center, Lille, France and Department of Neurology & Brain Tumor Center, University Hospital and University of Zurich, Zurich, Switzerland
| | - Michael Lim
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | | | - Ingo K Mellinghoff
- Department of Neurology and Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Giuseppe Minniti
- Radiation Oncology Unit, Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - David Nathanson
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, California, USA
| | - Michael Platten
- Department of Neurology, Medical Faculty Mannheim, MCTN, Heidelberg University, Heidelberg, Germany
| | - Matthias Preusser
- Division of Oncology, Department of Medicine, Medical University of Vienna, Vienna, Austria
| | - Patrick Roth
- Department of Neurology and Brain Tumor Center, University Hospital and University of Zurich, Zurich, Switzerland
| | - Marc Sanson
- Sorbonne Université, Inserm, CNRS, UMR S 1127, Institut du Cerveau et de la Moelle épinière, ICM, AP-HP, Hôpitaux Universitaires La Pitié Salpêtrière – Charles Foix, Service de Neurologie 2-Mazarin, Paris, France
| | - David Schiff
- University of Virginia School of Medicine, Division of Neuro-Oncology, Department of Neurology, University of Virginia, Charlottesville, Virginia, USA
| | - Susan C Short
- Leeds Institute of Medical Research at St James’s, University of Leeds, Leeds, UK
| | - Martin J B Taphoorn
- Department of Neurology, Medical Center Haaglanden, The Hague and Department of Neurology, Leiden University Medical Center, the Netherlands
| | | | - Jonathan Tsang
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, California, USA
| | - Roel G W Verhaak
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, USA
| | - Andreas von Deimling
- Neuropathology and Clinical Cooperation Unit Neuropathology, University Heidelberg and German Cancer Center, Heidelberg, Germany
| | - Wolfgang Wick
- Department of Neurology and Neuro-oncology Program, National Center for Tumor Diseases, Heidelberg University Hospital, Heidelberg, Germany
| | - Gelareh Zadeh
- MacFeeters Hamilton Centre for Neuro-Oncology Research, Princess Margaret Cancer Centre, Toronto, Canada
| | - David A Reardon
- Dana-Farber Cancer Institute, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Kenneth D Aldape
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland, USA
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170
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Jain AK, Aneja S, Fuller CD, Dicker AP, Chung C, Kim E, Kirby JS, Quon H, Lam CJK, Louv WC, Ahern C, Xiao Y, McNutt TR, Housri N, Ennis RD, Kang J, Tang Y, Higley H, Berny-Lang MA, Camphausen KA. Provider Engagement in Radiation Oncology Data Science: Workshop Report. JCO Clin Cancer Inform 2020; 4:700-710. [PMID: 32755458 PMCID: PMC7469584 DOI: 10.1200/cci.20.00051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/02/2020] [Indexed: 11/20/2022] Open
Affiliation(s)
- Anshu K. Jain
- National Cancer Institute, Rockville, MD
- Food and Drug Administration, Silver Spring, MD
| | | | | | - Adam P. Dicker
- Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA
| | | | - Erika Kim
- National Cancer Institute, Rockville, MD
| | - Justin S. Kirby
- Frederick National Laboratory for Cancer Research, Frederick, MD
| | - Harry Quon
- Johns Hopkins School of Medicine, Baltimore, MD
| | | | | | | | - Ying Xiao
- University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | | | - Nadine Housri
- Yale School of Medicine, New Haven, CT
- theMednet, New Haven, CT
| | | | - John Kang
- University of Rochester Medical Center, Rochester, NY
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171
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Yuan T, Ying J, Zuo Z, Jin L, Gui S, Gao Z, Li G, Wang R, Zhang Y, Li C. Contrahemispheric Cortex Predicts Survival and Molecular Markers in Patients With Unilateral High-Grade Gliomas. Front Oncol 2020; 10:953. [PMID: 32793462 PMCID: PMC7390929 DOI: 10.3389/fonc.2020.00953] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Accepted: 05/15/2020] [Indexed: 11/13/2022] Open
Abstract
Background: Malignant high-grade gliomas are characterized by infiltration and destruction of surrounding brain tissue. Alterations in the contrahemispheric brain structure and their roles that may offer prognostically valuable information have not been investigated in high-grade gliomas. Methods: In total, 153 patients with unilateral glioma (low-grade, n = 77; high-grade, n = 76) and 115 healthy controls (HCs) were recruited and scanned with 3-D T1 imaging. The gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) volume in the contrahemisphere were examined. Partial correlation, logistic regression, and multivariate Cox's regression analyses were performed. Results: The contrahemispheric GM volume (CHGMV) in the high-grade glioma patients was significantly decreased compared to that in the HCs/low-grade gliomas (one-way ANOVA, Bonferroni corrected, p < 0.05). The CHGMV is significantly correlated with the WHO grade (r = -0.22, p < 0.05) and contrast-enhanced volume (r = -0.33, p < 0.01). In the high-grade gliomas, the binary logistic regression revealed that the CHGMV can independently predict isocitrate dehydrogenase 1 (IDH1) and P53 mutations. The survival curves revealed that the patients with a low CHGMV had a shorter overall survival (OS) than the patients with a high CHGMV (p = 0.001). The multivariate Cox's regression analysis showed that a low CHGMV can independently predict unfavorable OS with a hazard ratio of 2.883 (p = 0.035). Conclusions: Volume of the contrahemispheric cortex can be potentially used in clinical practice as an imaging biomarker to predict survival and molecular markers in patients with unilateral high-grade gliomas.
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Affiliation(s)
- Taoyang Yuan
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Jianyou Ying
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Zhentao Zuo
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
| | - Lu Jin
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Songbai Gui
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Zhixian Gao
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Guilin Li
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Rui Wang
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Yazhuo Zhang
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
- Beijing Institute for Brain Disorders Brain Tumor Center, Beijing, China
| | - Chuzhong Li
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- Beijing Institute for Brain Disorders Brain Tumor Center, Beijing, China
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172
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Human IDH mutant 1p/19q co-deleted gliomas have low tumor acidity as evidenced by molecular MRI and PET: a retrospective study. Sci Rep 2020; 10:11922. [PMID: 32681084 PMCID: PMC7367867 DOI: 10.1038/s41598-020-68733-5] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Accepted: 06/01/2020] [Indexed: 01/19/2023] Open
Abstract
Co-deletion of 1p/19q is a hallmark of oligodendroglioma and predicts better survival. However, little is understood about its metabolic characteristics. In this study, we aimed to explore the extracellular acidity of WHO grade II and III gliomas associated with 1p/19q co-deletion. We included 76 glioma patients who received amine chemical exchange saturation transfer (CEST) imaging at 3 T. Magnetic transfer ratio asymmetry (MTRasym) at 3.0 ppm was used as the pH-sensitive CEST biomarker, with higher MTRasym indicating lower pH. To control for the confounder factors, T2 relaxometry and l-6-18F-fluoro-3,4-dihydroxyphenylalnine (18F-FDOPA) PET data were collected in a subset of patients. We found a significantly lower MTRasym in 1p/19q co-deleted gliomas (co-deleted, 1.17% ± 0.32%; non-co-deleted, 1.72% ± 0.41%, P = 1.13 × 10−7), while FDOPA (P = 0.92) and T2 (P = 0.61) were not significantly affected. Receiver operating characteristic analysis confirmed that MTRasym could discriminate co-deletion status with an area under the curve of 0.85. In analysis of covariance, 1p/19q co-deletion status was the only significant contributor to the variability in MTRasym when controlling for age and FDOPA (P = 2.91 × 10−3) or T2 (P = 8.03 × 10−6). In conclusion, 1p/19q co-deleted gliomas were less acidic, which may be related to better prognosis. Amine CEST-MRI may serve as a non-invasive biomarker for identifying 1p/19q co-deletion status.
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173
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Zakharova NE, Pronin IN, Batalov AI, Shults EI, Tyurina AN, Baev AA, Fadeeva LM. [Modern standards for magnetic resonance imaging of the brain tumors]. ZHURNAL VOPROSY NEĬROKHIRURGII IMENI N. N. BURDENKO 2020; 84:102-112. [PMID: 32649820 DOI: 10.17116/neiro202084031102] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Neuroimaging is essential in survey of patients with brain tumors. An important objectives of neuroimaging are highly reliable non-invasive diagnosis, treatment planning and evaluation of treatment outcomes. Magnetic resonance imaging (MRI) is one of the modern neuroimaging methods. This technique ensures analysis of structural cerebral changes, vascular and metabolic characteristics of brain tumors. It is necessary to standardize imaging parameters and unify protocols and methods considering a widespread use of MRI for brain tumors. In our practice, we use our own experience, world literature data and evidence-based international guidelines on the diagnosis of various brain diseases. The purpose of this review is to study the modern principles of magnetic resonance imaging in adults with brain tumors in neurosurgical practice.
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Affiliation(s)
| | - I N Pronin
- Burdenko Neurosurgical Center, Moscow, Russia
| | - A I Batalov
- Burdenko Neurosurgical Center, Moscow, Russia
| | - E I Shults
- Burdenko Neurosurgical Center, Moscow, Russia
| | - A N Tyurina
- Burdenko Neurosurgical Center, Moscow, Russia
| | - A A Baev
- Burdenko Neurosurgical Center, Moscow, Russia
| | - L M Fadeeva
- Burdenko Neurosurgical Center, Moscow, Russia
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174
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Verburg N, de Witt Hamer PC. State-of-the-art imaging for glioma surgery. Neurosurg Rev 2020; 44:1331-1343. [PMID: 32607869 PMCID: PMC8121714 DOI: 10.1007/s10143-020-01337-9] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Revised: 05/25/2020] [Accepted: 06/15/2020] [Indexed: 11/29/2022]
Abstract
Diffuse gliomas are infiltrative primary brain tumors with a poor prognosis despite multimodal treatment. Maximum safe resection is recommended whenever feasible. The extent of resection (EOR) is positively correlated with survival. Identification of glioma tissue during surgery is difficult due to its diffuse nature. Therefore, glioma resection is imaging-guided, making the choice for imaging technique an important aspect of glioma surgery. The current standard for resection guidance in non-enhancing gliomas is T2 weighted or T2w-fluid attenuation inversion recovery magnetic resonance imaging (MRI), and in enhancing gliomas T1-weighted MRI with a gadolinium-based contrast agent. Other MRI sequences, like magnetic resonance spectroscopy, imaging modalities, such as positron emission tomography, as well as intraoperative imaging techniques, including the use of fluorescence, are also available for the guidance of glioma resection. The neurosurgeon’s goal is to find the balance between maximizing the EOR and preserving brain functions since surgery-induced neurological deficits result in lower quality of life and shortened survival. This requires localization of important brain functions and white matter tracts to aid the pre-operative planning and surgical decision-making. Visualization of brain functions and white matter tracts is possible with functional MRI, diffusion tensor imaging, magnetoencephalography, and navigated transcranial magnetic stimulation. In this review, we discuss the current available imaging techniques for the guidance of glioma resection and the localization of brain functions and white matter tracts.
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Affiliation(s)
- Niels Verburg
- Department of Neurosurgery and Cancer Center Amsterdam, Amsterdam UMC location VU University Medical Center, Amsterdam, The Netherlands. .,Division of Neurosurgery, Department of Clinical Neurosciences, Cambridge Brain Tumor Imaging Laboratory, University of Cambridge, Addenbrooke's Hospital, Hill Rd, Cambridge, CB2 0QQ, UK.
| | - Philip C de Witt Hamer
- Department of Neurosurgery and Cancer Center Amsterdam, Amsterdam UMC location VU University Medical Center, Amsterdam, The Netherlands
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175
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Gatson NTN, Bross SP, Odia Y, Mongelluzzo GJ, Hu Y, Lockard L, Manikowski JJ, Mahadevan A, Kazmi SAJ, Lacroix M, Conger AR, Vadakara J, Nayak L, Chi TL, Mehta MP, Puduvalli VK. Early imaging marker of progressing glioblastoma: a window of opportunity. J Neurooncol 2020; 148:629-640. [PMID: 32602020 DOI: 10.1007/s11060-020-03565-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Accepted: 06/17/2020] [Indexed: 12/28/2022]
Abstract
PURPOSE Therapeutic intervention at glioblastoma (GBM) progression, as defined by current assessment criteria, is arguably too late as second-line therapies fail to extend survival. Still, most GBM trials target recurrent disease. We propose integration of a novel imaging biomarker to more confidently and promptly define progression and propose a critical timepoint for earlier intervention to extend therapeutic exposure. METHODS A retrospective review of 609 GBM patients between 2006 and 2019 yielded 135 meeting resection, clinical, and imaging inclusion criteria. We qualitatively and quantitatively analyzed 2000+ sequential brain MRIs (initial diagnosis to first progression) for development of T2 FLAIR signal intensity (SI) within the resection cavity (RC) compared to the ventricles (V) for quantitative inter-image normalization. PFS and OS were evaluated using Kaplan-Meier curves stratified by SI. Specificity and sensitivity were determined using a 2 × 2 table and pathology confirmation at progression. Multivariate analysis evaluated SI effect on the hazard rate for death after adjusting for established prognostic covariates. Recursive partitioning determined successive quantifiers and cutoffs associated with outcomes. Neurological deficits correlated with SI. RESULTS Seventy-five percent of patients developed SI on average 3.4 months before RANO-assessed progression with 84% sensitivity. SI-positivity portended neurological decline and significantly poorer outcomes for PFS (median, 10 vs. 15 months) and OS (median, 20 vs. 29 months) compared to SI-negative. RC/V ratio ≥ 4 was the most significant prognostic indicator of death. CONCLUSION Implications of these data are far-reaching, potentially shifting paradigms for glioma treatment response assessment, altering timepoints for salvage therapeutic intervention, and reshaping glioma clinical trial design.
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Affiliation(s)
- Na Tosha N Gatson
- Neuroscience Institute, Geisinger Health, Danville, PA, 17822, USA. .,Cancer Institute, Geisinger Health, Danville, PA, 17822, USA. .,Geisinger Commonwealth School of Medicine, Scranton, PA, 18509, USA. .,Geisinger Medical Center, Neuroscience Institute MC 14-03, 100 N. Academy Ave, Danville, PA, 17822, USA.
| | - Shane P Bross
- Neuroscience Institute, Geisinger Health, Danville, PA, 17822, USA
| | - Yazmin Odia
- Department of Neuro-Oncology, Miami Cancer Institute/Baptist Health South Florida, Miami, FL, 33176, USA
| | | | - Yirui Hu
- Department of Population Health Sciences, Geisinger Health, Danville, PA, 17822, USA
| | - Laura Lockard
- Geisinger Commonwealth School of Medicine, Scranton, PA, 18509, USA
| | | | - Anand Mahadevan
- Cancer Institute, Geisinger Health, Danville, PA, 17822, USA
| | - Syed A J Kazmi
- Department of Pathology, Geisinger Health, Danville, PA, 17822, USA
| | - Michel Lacroix
- Neuroscience Institute, Geisinger Health, Danville, PA, 17822, USA
| | - Andrew R Conger
- Neuroscience Institute, Geisinger Health, Danville, PA, 17822, USA.,Geisinger Commonwealth School of Medicine, Scranton, PA, 18509, USA
| | - Joseph Vadakara
- Cancer Institute, Geisinger Health, Danville, PA, 17822, USA
| | - Lakshmi Nayak
- Harvard Medical School, Center for Neuro-Oncology,, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - T Linda Chi
- Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Minesh P Mehta
- Department of Radiation Oncology, Miami Cancer Institute/Baptist Health South Florida, Miami, FL, 33176, USA
| | - Vinay K Puduvalli
- Division of Neuro-Oncology, The OH State University Comprehensive Cancer Center - James and OSU Neurological Institute, Columbus, OH, 43210, USA.,Department of Neuro-Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
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176
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Mellinghoff IK, Ellingson BM, Touat M, Maher E, De La Fuente MI, Holdhoff M, Cote GM, Burris H, Janku F, Young RJ, Huang R, Jiang L, Choe S, Fan B, Yen K, Lu M, Bowden C, Steelman L, Pandya SS, Cloughesy TF, Wen PY. Ivosidenib in Isocitrate Dehydrogenase 1 -Mutated Advanced Glioma. J Clin Oncol 2020; 38:3398-3406. [PMID: 32530764 PMCID: PMC7527160 DOI: 10.1200/jco.19.03327] [Citation(s) in RCA: 170] [Impact Index Per Article: 42.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
PURPOSE Diffuse gliomas are malignant brain tumors that include lower-grade gliomas (LGGs) and glioblastomas. Transformation of low-grade glioma into a higher tumor grade is typically associated with contrast enhancement on magnetic resonance imaging. Mutations in the isocitrate dehydrogenase 1 (IDH1) gene occur in most LGGs (> 70%). Ivosidenib is an inhibitor of mutant IDH1 (mIDH1) under evaluation in patients with solid tumors. METHODS We conducted a multicenter, open-label, phase I, dose escalation and expansion study of ivosidenib in patients with mIDH1 solid tumors. Ivosidenib was administered orally daily in 28-day cycles. RESULTS In 66 patients with advanced gliomas, ivosidenib was well tolerated, with no dose-limiting toxicities reported. The maximum tolerated dose was not reached; 500 mg once per day was selected for the expansion cohort. The grade ≥ 3 adverse event rate was 19.7%; 3% (n = 2) were considered treatment related. In patients with nonenhancing glioma (n = 35), the objective response rate was 2.9%, with 1 partial response. Thirty of 35 patients (85.7%) with nonenhancing glioma achieved stable disease compared with 14 of 31 (45.2%) with enhancing glioma. Median progression-free survival was 13.6 months (95% CI, 9.2 to 33.2 months) and 1.4 months (95% CI, 1.0 to 1.9 months) for the nonenhancing and enhancing glioma cohorts, respectively. In an exploratory analysis, ivosidenib reduced the volume and growth rates of nonenhancing tumors. CONCLUSION In patients with mIDH1 advanced glioma, ivosidenib 500 mg once per day was associated with a favorable safety profile, prolonged disease control, and reduced growth of nonenhancing tumors.
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Affiliation(s)
- Ingo K Mellinghoff
- Department of Neurology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Benjamin M Ellingson
- UCLA Brain Tumor Imaging Laboratory, Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA
| | - Mehdi Touat
- Drug Development Department, Gustave Roussy Cancer Center, Villejuif, France
| | - Elizabeth Maher
- Department of Neurology and Neurotherapeutics, University of Texas Southwestern Medical Center, Dallas, TX
| | - Macarena I De La Fuente
- Department of Neurology and Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL
| | - Matthias Holdhoff
- The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Baltimore, MD
| | - Gregory M Cote
- Henri and Belinda Termeer Center for Targeted Therapies, Massachusetts General Hospital Cancer Center, Boston, MA
| | | | - Filip Janku
- Department of Investigational Cancer Therapeutics, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Robert J Young
- Radiology, Neuroradiology Service, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Raymond Huang
- Department of Radiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Boston, MA
| | - Liewen Jiang
- Biostatistics, Agios Pharmaceuticals, Cambridge, MA
| | - Sung Choe
- Bioinformatics, Agios Pharmaceuticals, Cambridge, MA
| | - Bin Fan
- Pharmacology, Agios Pharmaceuticals, Cambridge, MA
| | - Katharine Yen
- Clinical Sciences, Agios Pharmaceuticals, Cambridge, MA
| | - Min Lu
- Clinical Sciences, Agios Pharmaceuticals, Cambridge, MA
| | | | | | | | - Timothy F Cloughesy
- Department of Neurology, Ronald Reagan UCLA Medical Center, University of California, Los Angeles, Los Angeles, CA
| | - Patrick Y Wen
- Center for Neuro-Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
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177
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Belani P, Kihira S, Pacheco F, Pawha P, Cruciata G, Nael K. Addition of arterial spin-labelled MR perfusion to conventional brain MRI: clinical experience in a retrospective cohort study. BMJ Open 2020; 10:e036785. [PMID: 32532776 PMCID: PMC7295400 DOI: 10.1136/bmjopen-2020-036785] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
OBJECTIVE The usage of arterial spin labelling (ASL) perfusion has exponentially increased due to improved and faster acquisition time and ease of postprocessing. We aimed to report potential additional findings obtained by adding ASL to routine unenhanced brain MRI for patients being scanned in a hospital setting for various neurological indications. DESIGN Retrospective. SETTING Large tertiary hospital. PARTICIPANTS 676 patients. PRIMARY OUTCOME Additional findings from ASL sequence compared with conventional MRI. RESULTS Our patient cohorts consisted of 676 patients with 257 with acute infarcts and 419 without an infarct. Additional findings from ASL were observed in 13.9% (94/676) of patients. In the non-infarct group, additional findings from ASL were observed in 7.4% (31/419) of patients, whereas in patients with an acute infarct, supplemental information was obtained in 24.5% (63/257) of patients. CONCLUSION The addition of an ASL sequence to routine brain MRI in a hospital setting provides additional findings compared with conventional brain MRI in about 7.4% of patients with additional supplementary information in 24.5% of patients with acute infarct.
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Affiliation(s)
- Puneet Belani
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Shingo Kihira
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Felipe Pacheco
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Puneet Pawha
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Giuseppe Cruciata
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Kambiz Nael
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
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178
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Houston Z, Bunt J, Chen KS, Puttick S, Howard CB, Fletcher NL, Fuchs AV, Cui J, Ju Y, Cowin G, Song X, Boyd AW, Mahler SM, Richards LJ, Caruso F, Thurecht KJ. Understanding the Uptake of Nanomedicines at Different Stages of Brain Cancer Using a Modular Nanocarrier Platform and Precision Bispecific Antibodies. ACS CENTRAL SCIENCE 2020; 6:727-738. [PMID: 32490189 PMCID: PMC7256936 DOI: 10.1021/acscentsci.9b01299] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Indexed: 06/11/2023]
Abstract
Increasing accumulation and retention of nanomedicines within tumor tissue is a significant challenge, particularly in the case of brain tumors where access to the tumor through the vasculature is restricted by the blood-brain barrier (BBB). This makes the application of nanomedicines in neuro-oncology often considered unfeasible, with efficacy limited to regions of significant disease progression and compromised BBB. However, little is understood about how the evolving tumor-brain physiology during disease progression affects the permeability and retention of designer nanomedicines. We report here the development of a modular nanomedicine platform that, when used in conjunction with a unique model of how tumorigenesis affects BBB integrity, allows investigation of how nanomaterial properties affect uptake and retention in brain tissue. By combining different in vivo longitudinal imaging techniques (including positron emission tomography and magnetic resonance imaging), we have evaluated the retention of nanomedicines with predefined physicochemical properties (size and surface functionality) and established a relationship between structure and tissue accumulation as a function of a new parameter that measures BBB leakiness; this offers significant advancements in our ability to relate tumor accumulation of nanomedicines to more physiologically relevant parameters. Our data show that accumulation of nanomedicines in brain tumor tissue is better correlated with the leakiness of the BBB than actual tumor volume. This was evaluated by establishing brain tumors using a spontaneous and endogenously derived glioblastoma model providing a unique opportunity to assess these parameters individually and compare the results across multiple mice. We also quantitatively demonstrate that smaller nanomedicines (20 nm) can indeed cross the BBB and accumulate in tumors at earlier stages of the disease than larger analogues, therefore opening the possibility of developing patient-specific nanoparticle treatment interventions in earlier stages of the disease. Importantly, these results provide a more predictive approach for designing efficacious personalized nanomedicines based on a particular patient's condition.
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Affiliation(s)
- Zachary
H. Houston
- Centre
for Advanced Imaging, The University of
Queensland, St Lucia, Queensland 4072, Australia
- Australian
Institute for Bioengineering and Nanotechnology, The University of Queensland, St Lucia, Queensland 4072, Australia
- ARC
Centre of Excellence in Convergent BioNano Science and Technology, The University of Queensland, St Lucia, Queensland 4072, Australia
| | - Jens Bunt
- Queensland
Brain Institute, The University of Queensland, St Lucia, Queensland 4072, Australia
| | - Kok-Siong Chen
- Queensland
Brain Institute, The University of Queensland, St Lucia, Queensland 4072, Australia
- Brigham
and Women’s Hospital, Harvard Medical
School, Boston, Massachusetts 02115, United States
| | - Simon Puttick
- Australian
Institute for Bioengineering and Nanotechnology, The University of Queensland, St Lucia, Queensland 4072, Australia
- Commonwealth
Scientific and Industrial Research Organisation, Probing Biosystems
Future Science Platform, Royal Brisbane
and Women’s Hospital, Brisbane, Queensland 4029, Australia
| | - Christopher B. Howard
- Centre
for Advanced Imaging, The University of
Queensland, St Lucia, Queensland 4072, Australia
- Australian
Institute for Bioengineering and Nanotechnology, The University of Queensland, St Lucia, Queensland 4072, Australia
- ARC
Centre of Excellence in Convergent BioNano Science and Technology, The University of Queensland, St Lucia, Queensland 4072, Australia
- ARC Training
Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, St Lucia, Queensland 4072, Australia
- ARC Training Centre for Biopharmaceutical
Innovation The University
of Queensland, St Lucia, Queensland 4072, Australia
| | - Nicholas L. Fletcher
- Centre
for Advanced Imaging, The University of
Queensland, St Lucia, Queensland 4072, Australia
- Australian
Institute for Bioengineering and Nanotechnology, The University of Queensland, St Lucia, Queensland 4072, Australia
- ARC
Centre of Excellence in Convergent BioNano Science and Technology, The University of Queensland, St Lucia, Queensland 4072, Australia
| | - Adrian V. Fuchs
- Centre
for Advanced Imaging, The University of
Queensland, St Lucia, Queensland 4072, Australia
- Australian
Institute for Bioengineering and Nanotechnology, The University of Queensland, St Lucia, Queensland 4072, Australia
- ARC
Centre of Excellence in Convergent BioNano Science and Technology, The University of Queensland, St Lucia, Queensland 4072, Australia
| | - Jiwei Cui
- Department
of Chemical Engineering, The University
of Melbourne, Parkville, Victoria 3010, Australia
- ARC
Centre of Excellence in Convergent BioNano Science and Technology, The University of Queensland, St Lucia, Queensland 4072, Australia
- Key
Laboratory of Colloid and Interface Chemistry of the Ministry of Education,
School of Chemistry and Chemical Engineering, Shandong University, Jinan, Shandong 250100, China
| | - Yi Ju
- Department
of Chemical Engineering, The University
of Melbourne, Parkville, Victoria 3010, Australia
- ARC
Centre of Excellence in Convergent BioNano Science and Technology, The University of Queensland, St Lucia, Queensland 4072, Australia
| | - Gary Cowin
- Centre
for Advanced Imaging, The University of
Queensland, St Lucia, Queensland 4072, Australia
| | - Xin Song
- Centre
for Advanced Imaging, The University of
Queensland, St Lucia, Queensland 4072, Australia
| | - Andrew W. Boyd
- Leukaemia
Foundation Laboratory, QIMR-Berghofer Medical Research Institute, Herston, Queensland 4006, Australia
- Department
of Medicine, The University of Queensland, St Lucia, Queensland 4072, Australia
| | - Stephen M. Mahler
- Australian
Institute for Bioengineering and Nanotechnology, The University of Queensland, St Lucia, Queensland 4072, Australia
- ARC Training Centre for Biopharmaceutical
Innovation The University
of Queensland, St Lucia, Queensland 4072, Australia
| | - Linda J. Richards
- Queensland
Brain Institute, The University of Queensland, St Lucia, Queensland 4072, Australia
- The
School of Biomedical Sciences, The University
of Queensland, St Lucia, Queensland 4072, Australia
| | - Frank Caruso
- Department
of Chemical Engineering, The University
of Melbourne, Parkville, Victoria 3010, Australia
- ARC
Centre of Excellence in Convergent BioNano Science and Technology, The University of Queensland, St Lucia, Queensland 4072, Australia
| | - Kristofer J. Thurecht
- Centre
for Advanced Imaging, The University of
Queensland, St Lucia, Queensland 4072, Australia
- Australian
Institute for Bioengineering and Nanotechnology, The University of Queensland, St Lucia, Queensland 4072, Australia
- ARC
Centre of Excellence in Convergent BioNano Science and Technology, The University of Queensland, St Lucia, Queensland 4072, Australia
- ARC Training
Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, St Lucia, Queensland 4072, Australia
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179
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Brenner AJ, Peters KB, Vredenburgh J, Bokstein F, Blumenthal DT, Yust-Katz S, Peretz I, Oberman B, Freedman LS, Ellingson BM, Cloughesy TF, Sher N, Cohen YC, Lowenton-Spier N, Rachmilewitz Minei T, Yakov N, Mendel I, Breitbart E, Wen PY. Safety and efficacy of VB-111, an anticancer gene therapy, in patients with recurrent glioblastoma: results of a phase I/II study. Neuro Oncol 2020; 22:694-704. [PMID: 31844886 PMCID: PMC7229257 DOI: 10.1093/neuonc/noz231] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND VB-111 is a non-replicating adenovirus carrying a Fas-chimera transgene, leading to targeted apoptosis of tumor vascular endothelium and induction of a tumor-specific immune response. This phase I/II study evaluated the safety, tolerability, and efficacy of VB-111 with and without bevacizumab in recurrent glioblastoma (rGBM). METHODS Patients with rGBM (n = 72) received VB-111 in 4 treatment groups: subtherapeutic (VB-111 dose escalation), limited exposure (LE; VB-111 monotherapy until progression), primed combination (VB-111 monotherapy continued upon progression with combination of bevacizumab), and unprimed combination (upfront combination of VB-111 and bevacizumab). The primary endpoint was median overall survival (OS). Secondary endpoints were safety, overall response rate, and progression-free survival (PFS). RESULTS VB-111 was well tolerated. The most common adverse event was transient mild-moderate fever. Median OS time was significantly longer in the primed combination group compared with both LE (414 vs 223 days; hazard ratio [HR], 0.48; P = 0.043) and unprimed combination (414 vs 141.5 days; HR, 0.24; P = 0.0056). Patients in the combination phase of the primed combination group had a median PFS time of 90 days compared with 60 in the LE group (HR, 0.36; P = 0.032), and 63 in the unprimed combination group (P = 0.72). Radiographic responders to VB-111 exhibited characteristic, expansive areas of necrosis in the areas of initial enhancing disease. CONCLUSIONS Patients with rGBM who were primed with VB-111 monotherapy that continued after progression with the addition of bevacizumab showed significant survival and PFS advantage, as well as specific imaging characteristics related to VB-111 mechanism of action. These results warrant further assessment in a randomized controlled study.
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Affiliation(s)
- Andrew J Brenner
- University of Texas Health San Antonio Mays Cancer Center, San Antonio, Texas, USA
| | - Katherine B Peters
- Preston Robert Tisch Brain Tumor Center, Duke University Medical Center, Durham, North Carolina, USA
| | - James Vredenburgh
- Saint Francis Hospital and Medical Center, Hartford, Connecticut, USA
| | - Felix Bokstein
- Tel Aviv Sourasky Medical Center and Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Deborah T Blumenthal
- Tel Aviv Sourasky Medical Center and Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Shlomit Yust-Katz
- Neuro-Oncology Unit, Davidoff Cancer Center at Rabin Medical Center, Petach Tikvah, Israel and Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Idit Peretz
- Neuro-Oncology Unit, Davidoff Cancer Center at Rabin Medical Center, Petach Tikvah, Israel and Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Bernice Oberman
- Biostatistics and Biomathematics Unit, Gertner Institute for Epidemiology and Health Policy Research, Chaim Sheba Medical Center, Tel Hashomer, Israel
| | - Laurence S Freedman
- Biostatistics and Biomathematics Unit, Gertner Institute for Epidemiology and Health Policy Research, Chaim Sheba Medical Center, Tel Hashomer, Israel
| | - Benjamin M Ellingson
- UCLA Brain Tumor Imaging Laboratory, Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Timothy F Cloughesy
- Department of Neurology, Ronald Reagan UCLA Medical Center, University of California Los Angeles, Los Angeles, California, USA
| | | | | | | | | | | | | | | | - Patrick Y Wen
- Center for Neuro-Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
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180
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Kurz C, Buizza G, Landry G, Kamp F, Rabe M, Paganelli C, Baroni G, Reiner M, Keall PJ, van den Berg CAT, Riboldi M. Medical physics challenges in clinical MR-guided radiotherapy. Radiat Oncol 2020; 15:93. [PMID: 32370788 PMCID: PMC7201982 DOI: 10.1186/s13014-020-01524-4] [Citation(s) in RCA: 79] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Accepted: 03/24/2020] [Indexed: 12/18/2022] Open
Abstract
The integration of magnetic resonance imaging (MRI) for guidance in external beam radiotherapy has faced significant research and development efforts in recent years. The current availability of linear accelerators with an embedded MRI unit, providing volumetric imaging at excellent soft tissue contrast, is expected to provide novel possibilities in the implementation of image-guided adaptive radiotherapy (IGART) protocols. This study reviews open medical physics issues in MR-guided radiotherapy (MRgRT) implementation, with a focus on current approaches and on the potential for innovation in IGART.Daily imaging in MRgRT provides the ability to visualize the static anatomy, to capture internal tumor motion and to extract quantitative image features for treatment verification and monitoring. Those capabilities enable the use of treatment adaptation, with potential benefits in terms of personalized medicine. The use of online MRI requires dedicated efforts to perform accurate dose measurements and calculations, due to the presence of magnetic fields. Likewise, MRgRT requires dedicated quality assurance (QA) protocols for safe clinical implementation.Reaction to anatomical changes in MRgRT, as visualized on daily images, demands for treatment adaptation concepts, with stringent requirements in terms of fast and accurate validation before the treatment fraction can be delivered. This entails specific challenges in terms of treatment workflow optimization, QA, and verification of the expected delivered dose while the patient is in treatment position. Those challenges require specialized medical physics developments towards the aim of fully exploiting MRI capabilities. Conversely, the use of MRgRT allows for higher confidence in tumor targeting and organs-at-risk (OAR) sparing.The systematic use of MRgRT brings the possibility of leveraging IGART methods for the optimization of tumor targeting and quantitative treatment verification. Although several challenges exist, the intrinsic benefits of MRgRT will provide a deeper understanding of dose delivery effects on an individual basis, with the potential for further treatment personalization.
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Affiliation(s)
- Christopher Kurz
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany
- Department of Medical Physics, Ludwig-Maximilians-Universität München, Am Coulombwall 1, 85748, Garching, Germany
| | - Giulia Buizza
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, P.za Leonardo da Vinci 32, 20133, Milano, Italy
| | - Guillaume Landry
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany
- Department of Medical Physics, Ludwig-Maximilians-Universität München, Am Coulombwall 1, 85748, Garching, Germany
- German Cancer Consortium (DKTK), 81377, Munich, Germany
| | - Florian Kamp
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany
| | - Moritz Rabe
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany
| | - Chiara Paganelli
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, P.za Leonardo da Vinci 32, 20133, Milano, Italy
| | - Guido Baroni
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, P.za Leonardo da Vinci 32, 20133, Milano, Italy
- Bioengineering Unit, National Center of Oncological Hadrontherapy (CNAO), Strada Privata Campeggi 53, 27100, Pavia, Italy
| | - Michael Reiner
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany
| | - Paul J Keall
- ACRF Image X Institute, University of Sydney, Sydney, NSW, 2006, Australia
| | - Cornelis A T van den Berg
- Department of Radiotherapy, University Medical Centre Utrecht, PO box 85500, 3508 GA, Utrecht, The Netherlands
| | - Marco Riboldi
- Department of Medical Physics, Ludwig-Maximilians-Universität München, Am Coulombwall 1, 85748, Garching, Germany.
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181
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Huang RY, Bi WL, Weller M, Kaley T, Blakeley J, Dunn I, Galanis E, Preusser M, McDermott M, Rogers L, Raizer J, Schiff D, Soffietti R, Tonn JC, Vogelbaum M, Weber D, Reardon DA, Wen PY. Proposed response assessment and endpoints for meningioma clinical trials: report from the Response Assessment in Neuro-Oncology Working Group. Neuro Oncol 2020; 21:26-36. [PMID: 30137421 DOI: 10.1093/neuonc/noy137] [Citation(s) in RCA: 116] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Abstract
No standard criteria exist for assessing response and progression in clinical trials involving patients with meningioma, and there is no consensus on the optimal endpoints for trials currently under way. As a result, there is substantial variation in the design and response criteria of meningioma trials, making comparison between trials difficult. In addition, future trials should be designed with accepted standardized endpoints. The Response Assessment in Neuro-Oncology Meningioma Working Group is an international effort to develop standardized radiologic criteria for treatment response for meningioma clinical trials. In this proposal, we present the recommendations for response criteria and endpoints for clinical trials involving patients with meningiomas.
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Affiliation(s)
- Raymond Y Huang
- Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Wenya Linda Bi
- Center for Skull Base and Pituitary Surgery, Department of Neurosurgery, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Michael Weller
- Department of Neurology, University Hospital Zurich, Zürich, Switzerland
| | - Thomas Kaley
- Neuro-Oncology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | | | - Ian Dunn
- Center for Skull Base and Pituitary Surgery, Department of Neurosurgery, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Evanthia Galanis
- Division of Medical Oncology, Department of Oncology, Mayo Clinic, Rochester, Minnesota, USA
| | - Matthias Preusser
- Clinical Division of Oncology, Department of Medicine I, Comprehensive Cancer Centre, Medical University Vienna-General Hospital, Vienna, Austria
| | - Michael McDermott
- Department of Neurological Surgery, University of California San Francisco, San Francisco, California, USA
| | - Leland Rogers
- Radiation Oncology, Barrow Neurological Institute, Phoenix, Arizona, USA
| | - Jeffrey Raizer
- Medical Neuro-Oncology, Northwestern Medicine, Chicago, Illinois, USA
| | - David Schiff
- Departments of Neurology, Neurological Surgery, and Medicine, University of Virginia, Charlottesville, Virginia, USA
| | - Riccardo Soffietti
- Department of Neuro-Oncology, University of Turin and City of Health and Science University Hospital, Torino, Italy
| | | | - Michael Vogelbaum
- Rose Ella Burkhardt Brain Tumor and Neuro Oncology Center, Cleveland Clinic, Cleveland, Ohio, USA
| | | | - David A Reardon
- Center of Neuro-Oncology, Dana-Farber/Brigham and Women's Cancer Center, Boston, Massachusetts, USA
| | - Patrick Y Wen
- Center of Neuro-Oncology, Dana-Farber/Brigham and Women's Cancer Center, Boston, Massachusetts, USA
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Park JE, Kim HS. [Current Applications and Future Perspectives of Brain Tumor Imaging]. TAEHAN YONGSANG UIHAKHOE CHI 2020; 81:467-487. [PMID: 36238631 PMCID: PMC9431910 DOI: 10.3348/jksr.2020.81.3.467] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 05/04/2020] [Accepted: 05/07/2020] [Indexed: 11/29/2022]
Abstract
뇌종양의 진단 및 치료 반응 평가의 기본이 되는 영상기법은 해부학적 영상이다. 현재 임상에서 사용 가능한 영상기법들 중 확산 강조 영상 및 관류 영상이 추가적인 정보를 제공하고 있다. 최근에는 종양의 유전체 변이와 이질성 평가가 중요해지면서 라디오믹스와 딥러닝을 이용한 영상분석기법의 임상 응용이 기대되고 있다. 본 종설에서는 뇌종양 영상 임상 적용에서 여전히 중요한 해부학적 영상을 중심으로 한 자기공명영상 촬영 권고안, 최신 영상기법 중 확산 강조 영상 및 관류 영상의 기본 원리, 병태생리학적 배경 및 임상응용, 마지막으로 최근 컴퓨터 기술의 발전으로 많이 연구되고 있는 라디오믹스와 딥러닝의 뇌종양에서의 향후 활용가치에 대해 기술하고자 한다.
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183
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Cho N, Wang C, Raymond C, Kaprealian T, Ji M, Salamon N, Pope WB, Nghiemphu PL, Lai A, Cloughesy TF, Ellingson BM. Diffusion MRI changes in the anterior subventricular zone following chemoradiation in glioblastoma with posterior ventricular involvement. J Neurooncol 2020; 147:643-652. [PMID: 32239430 DOI: 10.1007/s11060-020-03460-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Accepted: 03/14/2020] [Indexed: 12/18/2022]
Abstract
INTRODUCTION There is growing evidence that the subventricular zone (SVZ) plays a key role in glioblastoma (GBM) tumorigenesis. However, little is known regarding how the SVZ, which is a harbor for adult neural stem cells, may be influenced by chemoradiation. The current diffusion-weighted imaging (DWI) study explored ipsilateral and contralateral alterations in the anterior SVZ in GBM patients with posterior enhancing lesions following chemoradiation. METHODS Forty GBM patients with tumor involvement in the posterior SVZ (mean age = 57 ± 10; left-hemisphere N = 25; right-hemisphere N = 15) were evaluated using DWI before and after chemoradiation. Regions-of-interest were drawn on the ipsilesional and contralesional anterior SVZ on apparent diffusion coefficient (ADC) maps for both timepoints. ADC histogram analysis was performed by modeling a bimodal, double Gaussian distribution to obtain ADCL, defined as the mean of the lower Gaussian distribution. RESULTS The ipsilesional SVZ had lower ADCL values compared to the contralesional SVZ before treatment (mean difference = 0.025 μm2/ms; P = 0.007). Following chemoradiation, these changes were no longer observed (mean difference = 0.0025 μm2/ms; P > 0.5), as ADCL values of the ipsilesional SVZ increased (mean difference = 0.026 μm2/ms; P = 0.037). An increase in ipsilesional ADCL was associated with shorter progression-free (P = 0.0119) and overall survival (P = 0.0265). CONCLUSIONS These preliminary observations suggest baseline asymmetry as well as asymmetric changes in the SVZ proximal (ipsilesional) to the tumor with respect to contralesional SVZ regions may be present in GBM, potentially implicating this region in tumorigenesis and/or treatment resistance.
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Affiliation(s)
- Nicholas Cho
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, 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
- Medical Scientist Training Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Chencai Wang
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, 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
| | - Catalina Raymond
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, 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
| | - Tania Kaprealian
- Department of Radiation Oncology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Matthew Ji
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Noriko Salamon
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Whitney B Pope
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Phioanh L Nghiemphu
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- UCLA Neuro-Oncology Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Albert Lai
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- UCLA Neuro-Oncology Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Timothy F Cloughesy
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- UCLA Neuro-Oncology Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Benjamin M Ellingson
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, 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.
- UCLA Neuro-Oncology Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
- Departments of Psychiatry, David Geffen School of Medicine, University of California Los Angeles, 924 Westwood Blvd., Suite 615, Los Angeles, CA, 90024, USA.
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184
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Magnetic resonance imaging for brain stereotactic radiotherapy : A review of requirements and pitfalls. Strahlenther Onkol 2020; 196:444-456. [PMID: 32206842 PMCID: PMC7182639 DOI: 10.1007/s00066-020-01604-0] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Accepted: 03/03/2020] [Indexed: 12/29/2022]
Abstract
Due to its superior soft tissue contrast, magnetic resonance imaging (MRI) is essential for many radiotherapy treatment indications. This is especially true for treatment planning in intracranial tumors, where MRI has a long-standing history for target delineation in clinical practice. Despite its routine use, care has to be taken when selecting and acquiring MRI studies for the purpose of radiotherapy treatment planning. Requirements on MRI are particularly demanding for intracranial stereotactic radiotherapy, where accurate imaging has a critical role in treatment success. However, MR images acquired for routine radiological assessment are frequently unsuitable for high-precision stereotactic radiotherapy as the requirements for imaging are significantly different for radiotherapy planning and diagnostic radiology. To assure that optimal imaging is used for treatment planning, the radiation oncologist needs proper knowledge of the most important requirements concerning the use of MRI in brain stereotactic radiotherapy. In the present review, we summarize and discuss the most relevant issues when using MR images for target volume delineation in intracranial stereotactic radiotherapy.
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185
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Lesueur P, Kao W, Leconte A, Geffrelot J, Lequesne J, Lacroix J, Brachet PE, Hrab I, Royer P, Clarisse B, Stefan D. Stereotactic radiotherapy on brain metastases with recent hemorrhagic signal: STEREO-HBM, a two-step phase 2 trial. BMC Cancer 2020; 20:147. [PMID: 32087691 PMCID: PMC7036220 DOI: 10.1186/s12885-020-6569-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2019] [Accepted: 01/21/2020] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND Brain metastases often occur in cancer evolution. They are not only responsible for death but also for disorders affecting the quality of life and the cognitive functions. Management of brain metastases usually consists in multi-modality treatments, including neurosurgery, whole brain radiotherapy (WBRT), and more recently radiosurgery (SRS) or fractionated stereotactic radiotherapy (FSRT), systemic treatment (chemotherapy or targeted therapy), combined or not with corticosteroids. Almost 20% of brain metastases can present recent (within 15 days) bleeding signs on neuro-imagery. In these conditions, WBRT is the usual treatment. Yet, patients may benefit from a more aggressive strategy with SRT or FSRT. However, these options were suspected to possibly major the risk of brain haemorrhage, although no scientifically proven. Radiation oncologists therefore usually remain reluctant to deliver SRS/FSRT for bleeding brain metastases. It is therefore challenging to establish a standard of care for the treatment of bleeding brain metastases. We propose a phase II trial to simultaneously assess safety and efficacy of FSRT to manage brain metastases with hemorrhagic signal. METHODS The STEREO-HBM study is a multicenter two-step non-randomised phase II trial addressing patients with at least one bleeding brain metastasis out of a maximum of 3 brain metastases. Each brain metastasis will be treated with 30 Gy in 3 fractions for 1 week. The main endpoint is based on both safety and efficacy endpoints as proposed by Bryant and Day's design. Safety endpoint is defined as the rate of bleeding complications 4 months post-FSRT while efficacy endpoint is defined as the 6-month local control rate. Multi-modal MRI will be used to assess intra-tumoral hemorrhagic events before and after treatment. Patients' quality of life will also be assessed. DISCUSSION Management of bleeding brain metastases is still debated and poorly explored in clinical trials. There is sparse and weak data on the signification of pretreatment intra-tumour haemorrhagic signs or on the risk of brain bleeding complications after FSRT. We expect this first prospective phase 2 trial in this particular setting will allow to clarify the place of FSRT to optimally manage bleeding brain metastases. TRIAL REGISTRATION NCT03696680, registered October, 4, 2018. PROTOCOL VERSION Version 2.1 dated from 2018/11/09.
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Affiliation(s)
- Paul Lesueur
- Radiation Oncology Department, Centre François Baclesse, F-14000 Caen, France
- Normandy University, F-14000 Caen, France
| | - William Kao
- Radiation Oncology Department, Centre François Baclesse, F-14000 Caen, France
| | - Alexandra Leconte
- Clinical Research Department, Centre François Baclesse, F-14000 Caen, France
| | - Julien Geffrelot
- Radiation Oncology Department, Centre François Baclesse, F-14000 Caen, France
| | - Justine Lequesne
- Clinical Research Department, Centre François Baclesse, F-14000 Caen, France
| | - Joëlle Lacroix
- Radiology Department, Centre François Baclesse, F-14000 Caen, France
| | - Pierre-Emmanuel Brachet
- Clinical Research Department, Centre François Baclesse, F-14000 Caen, France
- Medical Oncology Department, Centre François Baclesse, F-14000 Caen, France
| | - Ioana Hrab
- Medical Oncology Department, Centre François Baclesse, F-14000 Caen, France
| | - Philippe Royer
- Radiation Oncology Department, Institut de Cancérologie de Lorraine, F-54000 Vandœuvre-lès-Nancy, France
| | - Bénédicte Clarisse
- Clinical Research Department, Centre François Baclesse, F-14000 Caen, France
| | - Dinu Stefan
- Radiation Oncology Department, Centre François Baclesse, F-14000 Caen, France
- Radiation Oncology Department, Centre François Baclesse, 3 Avenue du Général Harris, F-14076 Caen Cedex 05, France
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186
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Duan S, Zhu Y, Liu F, Xin SX. Numerical Experiments on the Contrast Capability of Magnetic Resonance Electrical Property Tomography. Magn Reson Med Sci 2020; 19:77-85. [PMID: 31019159 PMCID: PMC7067912 DOI: 10.2463/mrms.mp.2018-0167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
Purpose: Magnetic resonance electrical property tomography (MR EPT) is a technique for non-invasively obtaining the electric property (EP) distribution of biological tissues, with a promising potential for application in the early detection of tumors. However, the contrast capability (CC) of this technique has not been fully studied. This work aims to theoretically explore the CC for detecting the variation of EP values and the size of the imaging region. Methods: A simulation scheme was specifically designed to evaluate the CC of MR EPT. The simulation study has the advantage that the magnetic field can be accurately obtained. EP maps of the designed phantom embedded with target regions of designated various sizes and EPs were reconstructed using the homogeneous Helmholtz equation based on B1+ with different signal-to-noise ratios (SNRs). The CC was estimated by determining the smallest detectable EP contrast when the target region size was as large as the Laplacian kernel and the smallest detectable target region size when the EP contrast was the same as the difference between healthy and malignant tissues in the brain, based on the reconstructed EP maps. Results: Using noise free B1+, the smallest detectable contrastσ and contrastεr were 1% and 3%, respectively, and the smallest detectable target region size was 1 mesh unit (the base unit size used in the simulation) for conductivity and relative permittivity. The smallest detectable EP contrast and target region size were decreased as the B1+ SNR increased. Conclusion: The CC of MR EPT was related with the SNR of the magnetic field. A small EP contrast and size were necessary for detection at a high-SNR magnetic field. Obtaining a high-SNR magnetic field is important for improving the CC of MR EPT.
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Affiliation(s)
- Song Duan
- Department of Radiation Oncology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University
| | - Yurong Zhu
- Department of Biomedical Engineering, Southern Medical University
| | - Feng Liu
- School of Information Technology and Electrical Engineering, University of Queensland
| | - Sherman Xuegang Xin
- School of Medicine, South China University of Technology, Guangzhou Higher Education Mega Centre
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187
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Ellingson BM. On the promise of artificial intelligence for standardizing radiographic response assessment in gliomas. Neuro Oncol 2020; 21:1346-1347. [PMID: 31504809 DOI: 10.1093/neuonc/noz162] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Affiliation(s)
- Benjamin M Ellingson
- UCLA Brain Tumor Imaging Laboratory, Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California
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188
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Stringfield O, Arrington JA, Johnston SK, Rognin NG, Peeri NC, Balagurunathan Y, Jackson PR, Clark-Swanson KR, Swanson KR, Egan KM, Gatenby RA, Raghunand N. Multiparameter MRI Predictors of Long-Term Survival in Glioblastoma Multiforme. ACTA ACUST UNITED AC 2020; 5:135-144. [PMID: 30854451 PMCID: PMC6403044 DOI: 10.18383/j.tom.2018.00052] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Standard-of-care multiparameter magnetic resonance imaging (MRI) scans of the brain were used to objectively subdivide glioblastoma multiforme (GBM) tumors into regions that correspond to variations in blood flow, interstitial edema, and cellular density. We hypothesized that the distribution of these distinct tumor ecological "habitats" at the time of presentation will impact the course of the disease. We retrospectively analyzed initial MRI scans in 2 groups of patients diagnosed with GBM, a long-term survival group comprising subjects who survived >36 month postdiagnosis, and a short-term survival group comprising subjects who survived ≤19 month postdiagnosis. The single-institution discovery cohort contained 22 subjects in each group, while the multi-institution validation cohort contained 15 subjects per group. MRI voxel intensities were calibrated, and tumor voxels clustered on contrast-enhanced T1-weighted and fluid-attenuated inversion-recovery (FLAIR) images into 6 distinct "habitats" based on low- to medium- to high-contrast enhancement and low-high signal on FLAIR scans. Habitat 6 (high signal on calibrated contrast-enhanced T1-weighted and FLAIR sequences) comprised a significantly higher volume fraction of tumors in the long-term survival group (discovery cohort, 35% ± 6.5%; validation cohort, 34% ± 4.8%) compared with tumors in the short-term survival group (discovery cohort, 17% ± 4.5%, P < .03; validation cohort, 16 ± 4.0%, P < .007). Of the 6 distinct MRI-defined habitats, the fractional tumor volume of habitat 6 at diagnosis was significantly predictive of long- or short-term survival. We discuss a possible mechanistic basis for this association and implications for habitat-driven adaptive therapy of GBM.
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Affiliation(s)
| | - John A Arrington
- Departments of Diagnostic & Interventional Radiology.,Department of Oncologic Sciences, University of S Florida, Tampa, FL
| | - Sandra K Johnston
- Mathematical NeuroOncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, AZ.,Department of Radiology, University of Washington, Seattle, WA; and
| | | | - Noah C Peeri
- Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL
| | | | - Pamela R Jackson
- Mathematical NeuroOncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, AZ
| | - Kamala R Clark-Swanson
- Mathematical NeuroOncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, AZ
| | - Kristin R Swanson
- Mathematical NeuroOncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic, Phoenix, AZ
| | - Kathleen M Egan
- Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL.,Department of Oncologic Sciences, University of S Florida, Tampa, FL
| | - Robert A Gatenby
- Departments of Diagnostic & Interventional Radiology.,Department of Oncologic Sciences, University of S Florida, Tampa, FL
| | - Natarajan Raghunand
- Cancer Physiology, and.,Department of Oncologic Sciences, University of S Florida, Tampa, FL
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189
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Korshoej AR, Lukacova S, Lassen-Ramshad Y, Rahbek C, Severinsen KE, Guldberg TL, Mikic N, Jensen MH, Cortnum SOS, von Oettingen G, Sørensen JCH. OptimalTTF-1: Enhancing tumor treating fields therapy with skull remodeling surgery. A clinical phase I trial in adult recurrent glioblastoma. Neurooncol Adv 2020; 2:vdaa121. [PMID: 33215088 PMCID: PMC7660275 DOI: 10.1093/noajnl/vdaa121] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND Preclinical studies suggest that skull remodeling surgery (SR-surgery) increases the dose of tumor treating fields (TTFields) in glioblastoma (GBM) and prevents wasteful current shunting through the skin. SR-surgery introduces minor skull defects to focus the cancer-inhibiting currents toward the tumor and increase the treatment dose. This study aimed to test the safety and feasibility of this concept in a phase I setting. METHODS Fifteen adult patients with the first recurrence of GBM were treated with personalized SR-surgery, TTFields, and physician's choice oncological therapy. The primary endpoint was toxicity and secondary endpoints included standard efficacy outcomes. RESULTS SR-surgery resulted in a mean skull defect area of 10.6 cm2 producing a median TTFields enhancement of 32% (range 25-59%). The median TTFields treatment duration was 6.8 months and the median compliance rate 90%. Patients received either bevacizumab, bevacizumab/irinotecan, or temozolomide rechallenge. We observed 71 adverse events (AEs) of grades 1 (52%), 2 (35%), and 3 (13%). There were no grade 4 or 5 AEs or intervention-related serious AEs. Six patients experienced minor TTFields-induced skin rash. The median progression-free survival (PFS) was 4.6 months and the PFS rate at 6 months was 36%. The median overall survival (OS) was 15.5 months and the OS rate at 12 months was 55%. CONCLUSIONS TTFields therapy combined with SR-surgery and medical oncological treatment is safe and nontoxic and holds the potential to improve the outcome for GBM patients through focal dose enhancement in the tumor.
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Affiliation(s)
- Anders Rosendal Korshoej
- Department of Neurosurgery, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Slavka Lukacova
- Department of Oncology, Aarhus University Hospital, Aarhus, Denmark
| | | | - Christian Rahbek
- Department of Neuroradiology, Aarhus University Hospital, Aarhus Denmark
| | | | | | - Nikola Mikic
- Department of Neurosurgery, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
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190
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Penas-Prado M, Wu J, Cahill DP, Brat DJ, Costello JF, Kluetz PG, Cairncross JG, van den Bent M, Verhaak RGW, Aboud O, Burger P, Chang SM, Cordova C, Huang RY, Rowe LS, Taphoorn MJB, Gilbert MR, Armstrong TS. Proceedings of the Comprehensive Oncology Network Evaluating Rare CNS Tumors (NCI-CONNECT) Oligodendroglioma Workshop. Neurooncol Adv 2019; 2:vdz048. [PMID: 33289010 DOI: 10.1093/noajnl/vdz048] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Background Oligodendroglioma is a rare primary central nervous system (CNS) tumor with highly variable outcome and for which therapy is usually not curative. At present, little is known regarding the pathways involved with progression of oligodendrogliomas or optimal biomarkers for stratifying risk. Developing new therapies for this rare cancer is especially challenging. To overcome these challenges, the neuro-oncology community must be particularly innovative, seeking multi-institutional and international collaborations, and establishing partnerships with patients and advocacy groups thereby ensuring that each patient enrolled in a study is as informative as possible. Methods The mission of the National Cancer Institute's NCI-CONNECT program is to address the challenges and unmet needs in rare CNS cancer research and treatment by connecting patients, health care providers, researchers, and advocacy organizations to work in partnership. On November 19, 2018, the program convened a workshop on oligodendroglioma, one of the 12 rare CNS cancers included in its initial portfolio. The purpose of this workshop was to discuss scientific progress and regulatory challenges in oligodendroglioma research and develop a call to action to advance research and treatment for this cancer. Results The recommendations of the workshop include a multifaceted and interrelated approach covering: biology and preclinical models, data sharing and advanced molecular diagnosis and imaging; clinical trial design; and patient outreach and engagement. Conclusions The NCI-CONNECT program is well positioned to address challenges in oligodendroglioma care and research in collaboration with other stakeholders and is developing a list of action items for future initiatives.
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Affiliation(s)
| | - Jing Wu
- Neuro-Oncology Branch/National Cancer Institute, Bethesda, Maryland
| | - Daniel P Cahill
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Daniel J Brat
- Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Joseph F Costello
- Department of Neurological Surgery, University of California, San Francisco
| | - Paul G Kluetz
- Oncology Center of Excellence, U.S. Food and Drug Administration, Washington DC
| | | | | | - Roel G W Verhaak
- Jackson Laboratory for Genomic Medicine, Farmington, Connecticut
| | - Orwa Aboud
- Neuro-Oncology Branch/National Cancer Institute, Bethesda, Maryland.,Brain Tumor Program, The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Baltimore, Maryland
| | - Peter Burger
- Neuropathology Division, Johns Hopkins, Baltimore, Maryland
| | - Susan M Chang
- Department of Neurological Surgery, University of California, San Francisco
| | - Christine Cordova
- Neuro-Oncology Branch/National Cancer Institute, Bethesda, Maryland.,NYU School of Medicine, Laura and Isaac Perlmutter Cancer Center, New York, NY
| | - Raymond Y Huang
- Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts
| | - Lindsay S Rowe
- Radiation Oncology Branch/National Cancer Institute, Bethesda, Maryland
| | - Martin J B Taphoorn
- Leiden University Medical Center and Haaglanden Medical Center, The Hague, The Netherlands
| | - Mark R Gilbert
- Neuro-Oncology Branch/National Cancer Institute, Bethesda, Maryland
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Thomas RP, Nagpal S, Iv M, Soltys SG, Bertrand S, Pelpola JS, Ball R, Yang J, Sundaram V, Lavezo J, Born D, Vogel H, Brown JM, Recht LD. Macrophage Exclusion after Radiation Therapy (MERT): A First in Human Phase I/II Trial using a CXCR4 Inhibitor in Glioblastoma. Clin Cancer Res 2019; 25:6948-6957. [PMID: 31537527 PMCID: PMC6891194 DOI: 10.1158/1078-0432.ccr-19-1421] [Citation(s) in RCA: 60] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Revised: 07/17/2019] [Accepted: 09/11/2019] [Indexed: 01/18/2023]
Abstract
PURPOSE Preclinical studies have demonstrated that postirradiation tumor revascularization is dependent on a stromal cell-derived factor-1 (SDF-1)/C-X-C chemokine receptor type 4 (CXCR4)-driven process in which myeloid cells are recruited from bone marrow. Blocking this axis results in survival improvement in preclinical models of solid tumors, including glioblastoma (GBM). We conducted a phase I/II study to determine the safety and efficacy of Macrophage Exclusion after Radiation Therapy (MERT) using the reversible CXCR4 inhibitor plerixafor in patients with newly diagnosed glioblastoma. PATIENTS AND METHODS We enrolled nine patients in the phase I study and an additional 20 patients in phase II using a modified toxicity probability interval (mTPI) design. Plerixafor was continuously infused intravenously via a peripherally inserted central catheter (PICC) line for 4 consecutive weeks beginning at day 35 of conventional treatment with concurrent chemoradiation. Blood serum samples were obtained for pharmacokinetic analysis. Additional studies included relative cerebral blood volume (rCBV) analysis using MRI and histopathology analysis of recurrent tumors. RESULTS Plerixafor was well tolerated with no drug-attributable grade 3 toxicities observed. At the maximum dose of 400 μg/kg/day, biomarker analysis found suprathreshold plerixafor serum levels and an increase in plasma SDF-1 levels. Median overall survival was 21.3 months [95% confidence interval (CI), 15.9-NA] with a progression-free survival of 14.5 months (95% CI, 11.9-NA). MRI and histopathology support the mechanism of action to inhibit postirradiation tumor revascularization. CONCLUSIONS Infusion of the CXCR4 inhibitor plerixafor was well tolerated as an adjunct to standard chemoirradiation in patients with newly diagnosed GBM and improves local control of tumor recurrences.
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Affiliation(s)
- Reena P Thomas
- Department of Neurology, Division of Neuro Oncology, Stanford, California.
| | - Seema Nagpal
- Department of Neurology, Division of Neuro Oncology, Stanford, California
| | - Michael Iv
- Department of Radiology, Division of Neuro Radiology, Stanford, California
| | | | - Sophie Bertrand
- Department of Neurology, Division of Neuro Oncology, Stanford, California
| | - Judith S Pelpola
- Department of Neurology, Division of Neuro Oncology, Stanford, California
| | - Robyn Ball
- Department of Medicine, Quantitative Sciences Unit, Stanford, California
| | - Jaden Yang
- Department of Medicine, Quantitative Sciences Unit, Stanford, California
| | - Vandana Sundaram
- Department of Medicine, Quantitative Sciences Unit, Stanford, California
| | - Jonathan Lavezo
- Department of Pathology, Division of Neuro Pathology, Stanford University, Stanford, California
| | - Donald Born
- Department of Pathology, Division of Neuro Pathology, Stanford University, Stanford, California
| | - Hannes Vogel
- Department of Pathology, Division of Neuro Pathology, Stanford University, Stanford, California
| | - J Martin Brown
- Department of Neurology, Division of Neuro Oncology, Stanford, California
| | - Lawrence D Recht
- Department of Neurology, Division of Neuro Oncology, Stanford, California
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192
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Zeiner PS, Kinzig M, Divé I, Maurer GD, Filipski K, Harter PN, Senft C, Bähr O, Hattingen E, Steinbach JP, Sörgel F, Voss M, Steidl E, Ronellenfitsch MW. Regorafenib CSF Penetration, Efficacy, and MRI Patterns in Recurrent Malignant Glioma Patients. J Clin Med 2019; 8:jcm8122031. [PMID: 31766326 PMCID: PMC6947028 DOI: 10.3390/jcm8122031] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Revised: 10/28/2019] [Accepted: 11/18/2019] [Indexed: 12/13/2022] Open
Abstract
(1) Background: The phase 2 Regorafenib in Relapsed Glioblastoma (REGOMA) trial indicated a survival benefit for patients with first recurrence of a glioblastoma when treated with the multikinase inhibitor regorafenib (REG) instead of lomustine. The aim of this retrospective study was to investigate REG penetration to cerebrospinal fluid (CSF), treatment efficacy, and effects on magnetic resonance imaging (MRI) in patients with recurrent high-grade gliomas. (2) Methods: Patients were characterized by histology, adverse events, steroid treatment, overall survival (OS), and MRI growth pattern. REG and its two active metabolites were quantified by liquid chromatography/tandem mass spectrometry in patients’ serum and CSF. (3) Results: 21 patients mainly with IDH-wildtype glioblastomas who had been treated with REG were retrospectively identified. Thirteen CFS samples collected from 3 patients of the cohort were available for pharmacokinetic testing. CSF levels of REG and its metabolites were significantly lower than in serum. Follow-up MRI was available in 19 patients and showed progressive disease (PD) in all but 2 patients. Two distinct MRI patterns were identified: 7 patients showed classic PD with progression of contrast enhancing lesions, whereas 11 patients showed a T2-dominant MRI pattern characterized by a marked reduction of contrast enhancement. Median OS was significantly better in patients with a T2-dominant growth pattern (10 vs. 27 weeks respectively, p = 0.003). Diffusion restrictions were observed in 13 patients. (4) Conclusion: REG and its metabolites were detectable in CSF. A distinct MRI pattern that might be associated with an improved OS was observed in half of the patient cohort. Treatment response in the total cohort was poor.
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Affiliation(s)
- Pia S. Zeiner
- Dr. Senckenberg Institute of Neurooncology, University Hospital Frankfurt, Goethe University, 60528 Frankfurt am Main, Germany; (P.S.Z.); (I.D.); (G.D.M.); (O.B.); (J.P.S.); (M.V.)
- University Cancer Center (UCT) Frankfurt, University Hospital Frankfurt, Goethe University, 60590 Frankfurt am Main, Germany; (K.F.); (P.N.H.); (E.H.); (E.S.)
- German Cancer Consortium (DKTK), 60590 Frankfurt am Main, Germany
- Frankfurt Cancer Institute (FCI), University Hospital Frankfurt, Goethe University, 60590 Frankfurt am Main, Germany
| | - Martina Kinzig
- IBMP—Institute for Biomedical and Pharmaceutical Research, 90562 Nürnberg-Heroldsberg, Germany; (M.K.); (F.S.)
| | - Iris Divé
- Dr. Senckenberg Institute of Neurooncology, University Hospital Frankfurt, Goethe University, 60528 Frankfurt am Main, Germany; (P.S.Z.); (I.D.); (G.D.M.); (O.B.); (J.P.S.); (M.V.)
- University Cancer Center (UCT) Frankfurt, University Hospital Frankfurt, Goethe University, 60590 Frankfurt am Main, Germany; (K.F.); (P.N.H.); (E.H.); (E.S.)
- German Cancer Consortium (DKTK), 60590 Frankfurt am Main, Germany
- Frankfurt Cancer Institute (FCI), University Hospital Frankfurt, Goethe University, 60590 Frankfurt am Main, Germany
| | - Gabriele D. Maurer
- Dr. Senckenberg Institute of Neurooncology, University Hospital Frankfurt, Goethe University, 60528 Frankfurt am Main, Germany; (P.S.Z.); (I.D.); (G.D.M.); (O.B.); (J.P.S.); (M.V.)
- University Cancer Center (UCT) Frankfurt, University Hospital Frankfurt, Goethe University, 60590 Frankfurt am Main, Germany; (K.F.); (P.N.H.); (E.H.); (E.S.)
| | - Katharina Filipski
- University Cancer Center (UCT) Frankfurt, University Hospital Frankfurt, Goethe University, 60590 Frankfurt am Main, Germany; (K.F.); (P.N.H.); (E.H.); (E.S.)
- German Cancer Consortium (DKTK), 60590 Frankfurt am Main, Germany
- Institute of Neurology (Edinger-Institute), University Hospital Frankfurt, Goethe University, 60528 Frankfurt am Main, Germany
| | - Patrick N. Harter
- University Cancer Center (UCT) Frankfurt, University Hospital Frankfurt, Goethe University, 60590 Frankfurt am Main, Germany; (K.F.); (P.N.H.); (E.H.); (E.S.)
- German Cancer Consortium (DKTK), 60590 Frankfurt am Main, Germany
- Frankfurt Cancer Institute (FCI), University Hospital Frankfurt, Goethe University, 60590 Frankfurt am Main, Germany
- Institute of Neurology (Edinger-Institute), University Hospital Frankfurt, Goethe University, 60528 Frankfurt am Main, Germany
| | - Christian Senft
- Department of Neurosurgery, University Hospital Frankfurt, Goethe University, 60528 Frankfurt am Main, Germany;
| | - Oliver Bähr
- Dr. Senckenberg Institute of Neurooncology, University Hospital Frankfurt, Goethe University, 60528 Frankfurt am Main, Germany; (P.S.Z.); (I.D.); (G.D.M.); (O.B.); (J.P.S.); (M.V.)
- Department of Neurology, Klinikum Aschaffenburg-Alzenau, 63739 Aschaffenburg, Germany
| | - Elke Hattingen
- University Cancer Center (UCT) Frankfurt, University Hospital Frankfurt, Goethe University, 60590 Frankfurt am Main, Germany; (K.F.); (P.N.H.); (E.H.); (E.S.)
- German Cancer Consortium (DKTK), 60590 Frankfurt am Main, Germany
- Department of Neuroradiology, University Hospital Frankfurt, Goethe University, 60528 Frankfurt am Main, Germany
| | - Joachim P. Steinbach
- Dr. Senckenberg Institute of Neurooncology, University Hospital Frankfurt, Goethe University, 60528 Frankfurt am Main, Germany; (P.S.Z.); (I.D.); (G.D.M.); (O.B.); (J.P.S.); (M.V.)
- University Cancer Center (UCT) Frankfurt, University Hospital Frankfurt, Goethe University, 60590 Frankfurt am Main, Germany; (K.F.); (P.N.H.); (E.H.); (E.S.)
- German Cancer Consortium (DKTK), 60590 Frankfurt am Main, Germany
- Frankfurt Cancer Institute (FCI), University Hospital Frankfurt, Goethe University, 60590 Frankfurt am Main, Germany
| | - Fritz Sörgel
- IBMP—Institute for Biomedical and Pharmaceutical Research, 90562 Nürnberg-Heroldsberg, Germany; (M.K.); (F.S.)
- Institute of Pharmacology, University Duisburg-Essen, 45141 Essen, Germany
| | - Martin Voss
- Dr. Senckenberg Institute of Neurooncology, University Hospital Frankfurt, Goethe University, 60528 Frankfurt am Main, Germany; (P.S.Z.); (I.D.); (G.D.M.); (O.B.); (J.P.S.); (M.V.)
- University Cancer Center (UCT) Frankfurt, University Hospital Frankfurt, Goethe University, 60590 Frankfurt am Main, Germany; (K.F.); (P.N.H.); (E.H.); (E.S.)
- German Cancer Consortium (DKTK), 60590 Frankfurt am Main, Germany
- Frankfurt Cancer Institute (FCI), University Hospital Frankfurt, Goethe University, 60590 Frankfurt am Main, Germany
| | - Eike Steidl
- University Cancer Center (UCT) Frankfurt, University Hospital Frankfurt, Goethe University, 60590 Frankfurt am Main, Germany; (K.F.); (P.N.H.); (E.H.); (E.S.)
- German Cancer Consortium (DKTK), 60590 Frankfurt am Main, Germany
- Department of Neuroradiology, University Hospital Frankfurt, Goethe University, 60528 Frankfurt am Main, Germany
| | - Michael W. Ronellenfitsch
- Dr. Senckenberg Institute of Neurooncology, University Hospital Frankfurt, Goethe University, 60528 Frankfurt am Main, Germany; (P.S.Z.); (I.D.); (G.D.M.); (O.B.); (J.P.S.); (M.V.)
- University Cancer Center (UCT) Frankfurt, University Hospital Frankfurt, Goethe University, 60590 Frankfurt am Main, Germany; (K.F.); (P.N.H.); (E.H.); (E.S.)
- German Cancer Consortium (DKTK), 60590 Frankfurt am Main, Germany
- Frankfurt Cancer Institute (FCI), University Hospital Frankfurt, Goethe University, 60590 Frankfurt am Main, Germany
- Correspondence: ; Tel.: +49-69-6301-87711; Fax: +49-69-6301-87713
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Rebsamen M, Knecht U, Reyes M, Wiest R, Meier R, McKinley R. Divide and Conquer: Stratifying Training Data by Tumor Grade Improves Deep Learning-Based Brain Tumor Segmentation. Front Neurosci 2019; 13:1182. [PMID: 31749678 PMCID: PMC6848279 DOI: 10.3389/fnins.2019.01182] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Accepted: 10/18/2019] [Indexed: 11/13/2022] Open
Abstract
It is a general assumption in deep learning that more training data leads to better performance, and that models will learn to generalize well across heterogeneous input data as long as that variety is represented in the training set. Segmentation of brain tumors is a well-investigated topic in medical image computing, owing primarily to the availability of a large publicly-available dataset arising from the long-running yearly Multimodal Brain Tumor Segmentation (BraTS) challenge. Research efforts and publications addressing this dataset focus predominantly on technical improvements of model architectures and less on properties of the underlying data. Using the dataset and the method ranked third in the BraTS 2018 challenge, we performed experiments to examine the impact of tumor type on segmentation performance. We propose to stratify the training dataset into high-grade glioma (HGG) and low-grade glioma (LGG) subjects and train two separate models. Although we observed only minor gains in overall mean dice scores by this stratification, examining case-wise rankings of individual subjects revealed statistically significant improvements. Compared to a baseline model trained on both HGG and LGG cases, two separately trained models led to better performance in 64.9% of cases (p < 0.0001) for the tumor core. An analysis of subjects which did not profit from stratified training revealed that cases were missegmented which had poor image quality, or which presented clinically particularly challenging cases (e.g., underrepresented subtypes such as IDH1-mutant tumors), underlining the importance of such latent variables in the context of tumor segmentation. In summary, we found that segmentation models trained on the BraTS 2018 dataset, stratified according to tumor type, lead to a significant increase in segmentation performance. Furthermore, we demonstrated that this gain in segmentation performance is evident in the case-wise ranking of individual subjects but not in summary statistics. We conclude that it may be useful to consider the segmentation of brain tumors of different types or grades as separate tasks, rather than developing one tool to segment them all. Consequently, making this information available for the test data should be considered, potentially leading to a more clinically relevant BraTS competition.
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Affiliation(s)
- Michael Rebsamen
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
- Graduate School for Cellular and Biomedical Sciences, University of Bern, Bern, Switzerland
| | - Urspeter Knecht
- Institute for Surgical Technology and Biomechanics, University of Bern, Bern, Switzerland
| | - Mauricio Reyes
- Healthcare Imaging A.I. Lab, Insel Data Science Center, Inselspital, Bern University Hospital, Bern, Switzerland
| | - Roland Wiest
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Raphael Meier
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Richard McKinley
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
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194
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Chang K, Beers AL, Bai HX, Brown JM, Ly KI, Li X, Senders JT, Kavouridis VK, Boaro A, Su C, Bi WL, Rapalino O, Liao W, Shen Q, Zhou H, Xiao B, Wang Y, Zhang PJ, Pinho MC, Wen PY, Batchelor TT, Boxerman JL, Arnaout O, Rosen BR, Gerstner ER, Yang L, Huang RY, Kalpathy-Cramer J. Automatic assessment of glioma burden: a deep learning algorithm for fully automated volumetric and bidimensional measurement. Neuro Oncol 2019; 21:1412-1422. [PMID: 31190077 PMCID: PMC6827825 DOI: 10.1093/neuonc/noz106] [Citation(s) in RCA: 101] [Impact Index Per Article: 20.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
BACKGROUND Longitudinal measurement of glioma burden with MRI is the basis for treatment response assessment. In this study, we developed a deep learning algorithm that automatically segments abnormal fluid attenuated inversion recovery (FLAIR) hyperintensity and contrast-enhancing tumor, quantitating tumor volumes as well as the product of maximum bidimensional diameters according to the Response Assessment in Neuro-Oncology (RANO) criteria (AutoRANO). METHODS Two cohorts of patients were used for this study. One consisted of 843 preoperative MRIs from 843 patients with low- or high-grade gliomas from 4 institutions and the second consisted of 713 longitudinal postoperative MRI visits from 54 patients with newly diagnosed glioblastomas (each with 2 pretreatment "baseline" MRIs) from 1 institution. RESULTS The automatically generated FLAIR hyperintensity volume, contrast-enhancing tumor volume, and AutoRANO were highly repeatable for the double-baseline visits, with an intraclass correlation coefficient (ICC) of 0.986, 0.991, and 0.977, respectively, on the cohort of postoperative GBM patients. Furthermore, there was high agreement between manually and automatically measured tumor volumes, with ICC values of 0.915, 0.924, and 0.965 for preoperative FLAIR hyperintensity, postoperative FLAIR hyperintensity, and postoperative contrast-enhancing tumor volumes, respectively. Lastly, the ICCs for comparing manually and automatically derived longitudinal changes in tumor burden were 0.917, 0.966, and 0.850 for FLAIR hyperintensity volume, contrast-enhancing tumor volume, and RANO measures, respectively. CONCLUSIONS Our automated algorithm demonstrates potential utility for evaluating tumor burden in complex posttreatment settings, although further validation in multicenter clinical trials will be needed prior to widespread implementation.
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Affiliation(s)
- Ken Chang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Andrew L Beers
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Harrison X Bai
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - James M Brown
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - K Ina Ly
- Stephen E. and Catherine Pappas Center for Neuro-Oncology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Xuejun Li
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Joeky T Senders
- Computational Neuroscience Outcomes Center, Department of Neurosurgery, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - Vasileios K Kavouridis
- Computational Neuroscience Outcomes Center, Department of Neurosurgery, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - Alessandro Boaro
- Computational Neuroscience Outcomes Center, Department of Neurosurgery, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - Chang Su
- Yale School of Medicine, New Haven, Connecticut, USA
| | - Wenya Linda Bi
- Center for Skull Base and Pituitary Surgery, Department of Neurosurgery, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - Otto Rapalino
- Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Weihua Liao
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Qin Shen
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Hao Zhou
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Bo Xiao
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Yinyan Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Paul J Zhang
- Department of Pathology and Laboratory Medicine, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Marco C Pinho
- Department of Radiology and Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Patrick Y Wen
- Center For Neuro-Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA
| | - Tracy T Batchelor
- Department of Neurology, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - Jerrold L Boxerman
- Department of Diagnostic Imaging, Rhode Island Hospital and Alpert Medical School of Brown University, Providence, Rhode Island, USA
| | - Omar Arnaout
- Computational Neuroscience Outcomes Center, Department of Neurosurgery, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - Bruce R Rosen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Elizabeth R Gerstner
- Stephen E. and Catherine Pappas Center for Neuro-Oncology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Li Yang
- Department of Neurology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Raymond Y Huang
- Department of Radiology, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - Jayashree Kalpathy-Cramer
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA
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195
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Cagney DN, Sul J, Huang RY, Ligon KL, Wen PY, Alexander BM. The FDA NIH Biomarkers, EndpointS, and other Tools (BEST) resource in neuro-oncology. Neuro Oncol 2019; 20:1162-1172. [PMID: 29294069 DOI: 10.1093/neuonc/nox242] [Citation(s) in RCA: 73] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
In early 2016, the FDA and the National Institutes of Health (NIH) published the first version of the glossary included in the Biomarkers, EndpointS, and other Tools (BEST) resource.1 The BEST glossary was constructed to harmonize and clarify terms used in translational science and medical product development and to provide a common language used for communication by those agencies. It is considered a "living" document that will be updated in the future. This review will discuss the main biomarker and clinical outcome categories contained in the BEST glossary as they apply to neuro-oncology, as well as the overlapping and hierarchical relationships among them.
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Affiliation(s)
- Daniel N Cagney
- Department of Radiation Oncology, Dana-Farber/Brigham and Women's Cancer Center, Harvard Medical School, Boston, Massachusetts
| | - Joohee Sul
- Office of Hematology and Oncology Products, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland
| | - Raymond Y Huang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Keith L Ligon
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Patrick Y Wen
- Center For Neuro-Oncology, Dana-Farber/Brigham and Women's Cancer Center, Harvard Medical School, Boston, Massachusetts
| | - Brian M Alexander
- Department of Radiation Oncology, Dana-Farber/Brigham and Women's Cancer Center, Harvard Medical School, Boston, Massachusetts
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Glioma through the looking GLASS: molecular evolution of diffuse gliomas and the Glioma Longitudinal Analysis Consortium. Neuro Oncol 2019; 20:873-884. [PMID: 29432615 PMCID: PMC6280138 DOI: 10.1093/neuonc/noy020] [Citation(s) in RCA: 111] [Impact Index Per Article: 22.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Adult diffuse gliomas are a diverse group of brain neoplasms that inflict a high emotional toll on patients and their families. The Cancer Genome Atlas and similar projects have provided a comprehensive understanding of the somatic alterations and molecular subtypes of glioma at diagnosis. However, gliomas undergo significant cellular and molecular evolution during disease progression. We review the current knowledge on the genomic and epigenetic abnormalities in primary tumors and after disease recurrence, highlight the gaps in the literature, and elaborate on the need for a new multi-institutional effort to bridge these knowledge gaps and how the Glioma Longitudinal Analysis Consortium (GLASS) aims to systemically catalog the longitudinal changes in gliomas. The GLASS initiative will provide essential insights into the evolution of glioma toward a lethal phenotype, with the potential to reveal targetable vulnerabilities and, ultimately, improved outcomes for a patient population in need.
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197
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Boxerman JL, Zhang Z, Safriel Y, Rogg JM, Wolf RL, Mohan S, Marques H, Sorensen AG, Gilbert MR, Barboriak DP. Prognostic value of contrast enhancement and FLAIR for survival in newly diagnosed glioblastoma treated with and without bevacizumab: results from ACRIN 6686. Neuro Oncol 2019; 20:1400-1410. [PMID: 29590461 DOI: 10.1093/neuonc/noy049] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Background ACRIN 6686/RTOG 0825 was a phase III trial of conventional chemoradiation plus adjuvant temozolomide with bevacizumab or without (placebo) in newly diagnosed glioblastoma. This study investigated whether changes in contrast-enhancing and fluid attenuated inversion recovery (FLAIR)-hyperintense tumor assessed by central reading prognosticate overall survival (OS). Methods Two hundred eighty-four patients (171 men; median age 57 y, range 19-79; 159 on bevacizumab) had MRI at post-op (baseline) and pre-cycle 4 of adjuvant temozolomide (22 wk post chemoradiation initiation). Four central readers measured bidimensional lesion enhancement (2D-T1) and FLAIR hyperintensity at both time points. Changes from baseline to pre-cycle 4 for both markers were dichotomized (increasing vs non-increasing). Cox proportional hazards model and Kaplan-Meier survival estimates were used for inference. Results Adjusting for treatment, increasing 2D-T1 (n = 262, hazard ratio [HR] = 2.07, 95% CI: 1.48-2.91, P < 0.0001) and FLAIR (n = 273, HR = 1.75, 95% CI: 1.26-2.41, P = 0.0008) significantly predicted worse OS. Median OS (days) was significantly shorter for patients with increasing versus non-increasing 2D-T1 for both bevacizumab (443 vs 535, P = 0.004) and placebo (526 vs 887, P = 0.001). Median OS was significantly shorter for patients with increasing versus non-increasing FLAIR for placebo (595 vs 872, P = 0.001), and trended similarly for bevacizumab (499 vs 535, P = 0.0935). Adjusting for 2D-T1 and treatment, increasing FLAIR represented significantly higher risk for death (HR = 1.59 [1.11-2.26], P = 0.01). Conclusion Increased 2D-T1 significantly predicts worse OS in both treatment groups, implying absence of a substantial proportion of pseudoprogression 22 weeks after initiation of standard therapy. FLAIR adds value beyond 2D-T1 in predicting OS, potentially addressing the pseudoresponse effect by substratifying bevacizumab-treated patients with non-increasing 2D-T1.
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Affiliation(s)
- Jerrold L Boxerman
- Department of Diagnostic Imaging, Rhode Island Hospital and Alpert Medical School of Brown University, Providence, Rhode Island
| | - Zheng Zhang
- Center for Statistical Sciences, Brown University, Providence, Rhode Island
| | - Yair Safriel
- Pharmascan Clinical Trials and Radiology Associates of Clearwater-University of South Florida, Clearwater, Florida
| | - Jeffrey M Rogg
- Department of Diagnostic Imaging, Rhode Island Hospital and Alpert Medical School of Brown University, Providence, Rhode Island
| | - Ronald L Wolf
- Department of Radiology, Neuroradiology Division, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania
| | - Suyash Mohan
- Department of Radiology, Neuroradiology Division, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania
| | - Helga Marques
- Center for Statistical Sciences, Brown University, Providence, Rhode Island
| | - A Gregory Sorensen
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts.,IMRIS, Deerfield Imaging, Inc, Minnetonka, Minnesota
| | - Mark R Gilbert
- Department of Neuro-oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.,Neuro-Oncology Branch of the National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Daniel P Barboriak
- Department of Radiology, Duke University Medical Center, Durham, North Carolina
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Gedye C, Sachchithananthan M, Leonard R, Jeffree RL, Buckland ME, Ziegler DS, Graeber MB, Day BW, McDonald KL, Lasocki A, Nowak AK. Driving innovation through collaboration: development of clinical annotation datasets for brain cancer biobanking. Neurooncol Pract 2019; 7:31-37. [PMID: 32257282 DOI: 10.1093/nop/npz036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Background A key component of cancer research is the availability of clinical samples with appropriately annotated clinical data. Biobanks facilitate research by collecting/storing various types of clinical samples for research. Brain Cancer Biobanking Australia (BCBA) was established to facilitate the networking of brain cancer biobanking operations Australia-wide. Maximizing biospecimen utility in a networked biobanking environment requires the standardization of procedures and data across different sites. The aim of this research was to scope and develop a recommended clinical annotation dataset both for pediatric and adult brain cancer biobanks. Methods A multidisciplinary working group consisting of members from the BCBA Consortium was established to develop clinical dataset recommendations for brain cancer biobanks. A literature search was undertaken to identify any published clinical dataset recommendations for brain cancer biobanks. An audit of data items collected and stored by BCBA member biobanks was also conducted to survey current clinical data collection practices across the BCBA network. Results BCBA has developed a clinical annotation dataset recommendation for pediatric and adult brain cancer biobanks. The clinical dataset recommendation has 5 clinical data categories: demographic, clinical and radiological diagnosis and surgery, neuropathological diagnosis, patient treatment, and patient follow-up. The data fields have been categorized into 1 of 3 tiers; essential, preferred, and comprehensive. This enables biobanks and researchers to prioritize appropriately where resources are limited. Conclusion This dataset can be used to guide the integration of data from multiple existing biobanks for research studies and for planning prospective brain cancer biobanking activities.
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Affiliation(s)
- Craig Gedye
- Brain Cancer Biobanking Australia, National Health and Medical Research Council Clinical Trials Centre, The University of Sydney, NSW, Australia.,Clinical Research Director, NSW Health Statewide Biobank, Camperdown NSW.,School of Medicine and Public Health, University of Newcastle, Callaghan, NSW, Australia.,Calvary Mater Newcastle, Waratah, NSW, Australia
| | - Mythily Sachchithananthan
- Brain Cancer Biobanking Australia, National Health and Medical Research Council Clinical Trials Centre, The University of Sydney, NSW, Australia
| | - Robyn Leonard
- Brain Cancer Biobanking Australia, National Health and Medical Research Council Clinical Trials Centre, The University of Sydney, NSW, Australia
| | - Rosalind L Jeffree
- Brain Cancer Biobanking Australia, National Health and Medical Research Council Clinical Trials Centre, The University of Sydney, NSW, Australia.,Royal Brisbane and Women's Hospital, University of Queensland, Australia
| | - Michael E Buckland
- Brain Cancer Biobanking Australia, National Health and Medical Research Council Clinical Trials Centre, The University of Sydney, NSW, Australia.,Department of Neuropathology, Royal Prince Alfred Hospital, Camperdown, NSW, Australia.,Discipline of Pathology, Brain & Mind Centre, University of Sydney, NSW, Australia
| | - David S Ziegler
- Brain Cancer Biobanking Australia, National Health and Medical Research Council Clinical Trials Centre, The University of Sydney, NSW, Australia.,Kids Cancer Centre, Sydney Children's Hospital, Randwick, NSW, Australia.,School of Women's and Children's Health, University of New South Wales, Sydney, Australia.,Children's Cancer Institute, University of New South Wales, Sydney, Australia
| | - Manuel B Graeber
- Brain Cancer Biobanking Australia, National Health and Medical Research Council Clinical Trials Centre, The University of Sydney, NSW, Australia.,Brain Tumor Research Laboratories, Brain and Mind Centre, The University of Sydney, NSW, Australia
| | - Bryan W Day
- Brain Cancer Biobanking Australia, National Health and Medical Research Council Clinical Trials Centre, The University of Sydney, NSW, Australia.,QIMR Berghofer Medical Research Institute, Herston, QLD, Australia
| | - Kerrie L McDonald
- Brain Cancer Biobanking Australia, National Health and Medical Research Council Clinical Trials Centre, The University of Sydney, NSW, Australia.,Cure Brain Cancer Neuro-oncology Laboratory, Prince of Wales Clinical School, University of New South Wales, Sydney, Australia.,Prince of Wales Clinical School, University of New South Wales, Sydney, Australia
| | - Arian Lasocki
- Brain Cancer Biobanking Australia, National Health and Medical Research Council Clinical Trials Centre, The University of Sydney, NSW, Australia.,Department of Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne, Victoria.,Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, Victoria, Australia
| | | | - Anna K Nowak
- Brain Cancer Biobanking Australia, National Health and Medical Research Council Clinical Trials Centre, The University of Sydney, NSW, Australia.,School of Medicine and Pharmacology, University of Western Australia, Crawley.,Department of Medical Oncology, Sir Charles Gairdner Hospital, Nedlands, Australia
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Kim D, Heo YJ, Jeong HW, Baek JW, Han JY, Lee JY, Jin SC, Baek HJ. Usefulness of the Delay Alternating with Nutation for Tailored Excitation Pulse with T1-Weighted Sampling Perfection with Application-Optimized Contrasts Using Different Flip Angle Evolution in the Detection of Cerebral Metastases: Comparison with MPRAGE Imaging. AJNR Am J Neuroradiol 2019; 40:1469-1475. [PMID: 31371358 DOI: 10.3174/ajnr.a6158] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Accepted: 06/27/2019] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE Contrast-enhanced T1-weighted sampling perfection with application-optimized contrasts by using different flip angle evolution (SPACE) with the delay alternating with nutation for tailored excitation (DANTE) pulse could suppress the blood flow signal and provide a higher contrast-to-noise ratio of enhancing lesion-to-brain parenchyma than the MPRAGE sequence. The purpose of our study was to evaluate the usefulness of SPACE with DANTE compared with MPRAGE for detecting brain metastases. MATERIALS AND METHODS Seventy-one patients who underwent contrast-enhanced SPACE with DANTE and MPRAGE sequences and who were suspected of having metastatic lesions were included. Two neuroradiologists determined the number of enhancing lesions, and diagnostic performance was evaluated using figure of merit, sensitivity, positive predictive value, interobserver agreement, and reading time. Contrast-to-noise ratiolesion/parenchyma and contrast-to-noise ratiowhite matter/gray matter were also assessed. RESULTS SPACE with DANTE (observer one, 328; observer two, 324) revealed significantly more small (<5 mm) enhancing lesions than MPRAGE (observer one, 175; observer two, 150) (P < 0.001 for observer 1, P ≤ .0001 for observer 2). Furthermore, SPACE with DANTE showed significantly higher figure of merit and sensitivity and shorter reading time than MPRAGE for both observers. The mean contrast-to-noise ratiolesion/parenchyma of SPACE with DANTE (52.3 ± 43.1) was significantly higher than that of MPRAGE (17.5 ± 19.3) (P ≤ .0001), but the mean contrast-to-noise ratiowhite matter/gray matter of SPACE with DANTE (-0.65 ± 1.39) was significantly lower than that of MPRAGE (3.08 ± 1.39) (P ≤ .0001). CONCLUSIONS Compared with MPRAGE, SPACE with DANTE significantly improves the detection of brain metastases.
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Affiliation(s)
- D Kim
- From the Departments of Radiology (D.K., Y.J.H., H.W.J., J.W.B., J.-Y.H.)
| | - Y J Heo
- From the Departments of Radiology (D.K., Y.J.H., H.W.J., J.W.B., J.-Y.H.)
| | - H W Jeong
- From the Departments of Radiology (D.K., Y.J.H., H.W.J., J.W.B., J.-Y.H.)
| | - J W Baek
- From the Departments of Radiology (D.K., Y.J.H., H.W.J., J.W.B., J.-Y.H.)
| | - J-Y Han
- From the Departments of Radiology (D.K., Y.J.H., H.W.J., J.W.B., J.-Y.H.)
| | - J Y Lee
- Internal Medicine (J.Y.L.), Inje University Busan Paik Hospital, Busan, Korea
| | - S-C Jin
- Department of Neurosurgery (S.-C.J.), Inje University Haeundae Paik Hospital, Busan, Republic of Korea
| | - H J Baek
- Department of Radiology (H.J.B.), Gyeongsang National University School of Medicine and Gyeongsang National University Changwon Hospital, Changwon, Republic of Korea
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200
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Steidl E, Müller M, Müller A, Herrlinger U, Hattingen E. Longitudinal, leakage corrected and uncorrected rCBV during the first-line treatment of glioblastoma: a prospective study. J Neurooncol 2019; 144:409-417. [PMID: 31321614 DOI: 10.1007/s11060-019-03244-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2019] [Accepted: 07/15/2019] [Indexed: 12/15/2022]
Abstract
PURPOSE Dynamic susceptibility contrast (DSC) MR-perfusion is becoming a standard of care for the monitoring of glioblastoma. Yet, technical standards are lacking and measurements without leakage correction are still common. Also, data on leakage corrected measurements during stable disease is scarce. In this study we hypothesized that basic leakage correction would significantly enhance data quality during stable disease and improve progress detection. We furthermore investigated whether longitudinal data could increase diagnostic performance. METHODS Patients with histologically proven glioblastoma undergoing first-line therapy were prospectively recruited. We conducted DSC perfusion measurements without prebolus administration in 6-week intervals from the end of radiotherapy until progression. Maximum relative cerebral volume values (rCBVmax) with and without leakage correction were calculated using Philips IntelliSpace®. RESULTS We recruited 16 patients and conducted 82 MRI scans with a mean follow up of 7.2 month. During stable disease, corrected rCBVmax was significantly more stable than uncorrected rCBVmax. Detection of progression with a rCBVmax cutoff was better for corrected (specificity 86%) than for uncorrected rCBVmax (specificity 41%). Interestingly, the increase of corrected rCBVmax upon progression also had a good diagnostic performance with a combination of both cutoffs delivering the best result (sensitivity/specificity 89%/93%). CONCLUSION Corrected rCBVmax supports the imaging finding of a stable disease and large increases during longitudinal observation support the diagnosis of tumor progression. rCBV values without prebolus or leakage correction are not reliable to monitor glioblastomas. Further studies to investigate the value of longitudinal rCBV dynamics for the differentiation of real tumor progression from pseudoprogression are warranted.
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Affiliation(s)
- Eike Steidl
- Institute of Neuroradiology, University Hospital Frankfurt, Schleusenweg 2-16, 60528, Frankfurt, Germany.
- Department of Radiology, Neuroradiology, University Hospital Bonn, Sigmund-Freud-Str. 25, 53127, Bonn, Germany.
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany.
| | - Mathias Müller
- Department of Radiology, Neuroradiology, University Hospital Bonn, Sigmund-Freud-Str. 25, 53127, Bonn, Germany
| | - Andreas Müller
- Department of Radiology, Neuroradiology, University Hospital Bonn, Sigmund-Freud-Str. 25, 53127, Bonn, Germany
| | - Ulrich Herrlinger
- Division of Clinical Neurooncology, Department of Neurology, University Hospital Bonn, Sigmund-Freud-Str. 25, 53127, Bonn, Germany
| | - Elke Hattingen
- Institute of Neuroradiology, University Hospital Frankfurt, Schleusenweg 2-16, 60528, Frankfurt, Germany
- Department of Radiology, Neuroradiology, University Hospital Bonn, Sigmund-Freud-Str. 25, 53127, Bonn, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany
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