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Almalki YE, Basha MAA, Metwally MI, Zeed NA, Nada MG, Alduraibi SK, Morsy AA, Balata R, Al Attar AZ, Amer MM, Farag MAEAM, Aly SA, Basha AMA, Hamed EM. Validating Brain Tumor Reporting and Data System (BT-RADS) as a Diagnostic Tool for Glioma Follow-Up after Surgery. Biomedicines 2024; 12:887. [PMID: 38672241 PMCID: PMC11048183 DOI: 10.3390/biomedicines12040887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2024] [Revised: 04/03/2024] [Accepted: 04/09/2024] [Indexed: 04/28/2024] Open
Abstract
Gliomas are a type of brain tumor that requires accurate monitoring for progression following surgery. The Brain Tumor Reporting and Data System (BT-RADS) has emerged as a potential tool for improving diagnostic accuracy and reducing the need for repeated operations. This prospective multicenter study aimed to evaluate the diagnostic accuracy and reliability of BT-RADS in predicting tumor progression (TP) in postoperative glioma patients and evaluate its acceptance in clinical practice. The study enrolled patients with a history of partial or complete resection of high-grade glioma. All patients underwent two consecutive follow-up brain MRI examinations. Five neuroradiologists independently evaluated the MRI examinations using the BT-RADS. The diagnostic accuracy of the BT-RADS for predicting TP was calculated using histopathology after reoperation and clinical and imaging follow-up as reference standards. Reliability based on inter-reader agreement (IRA) was assessed using kappa statistics. Reader acceptance was evaluated using a short survey. The final analysis included 73 patients (male, 67.1%; female, 32.9%; mean age, 43.2 ± 12.9 years; age range, 31-67 years); 47.9% showed TP, and 52.1% showed no TP. According to readers, TP was observed in 25-41.7% of BT-3a, 61.5-88.9% of BT-3b, 75-90.9% of BT-3c, and 91.7-100% of BT-RADS-4. Considering >BT-RADS-3a as a cutoff value for TP, the sensitivity, specificity, and accuracy of the BT-RADS were 68.6-85.7%, 84.2-92.1%, and 78.1-86.3%, respectively, according to the reader. The overall IRA was good (κ = 0.75) for the final BT-RADS classification and very good for detecting new lesions (κ = 0.89). The readers completely agreed with the statement "the application of the BT-RADS should be encouraged" (score = 25). The BT-RADS has good diagnostic accuracy and reliability for predicting TP in postoperative glioma patients. However, BT-RADS 3 needs further improvements to increase its diagnostic accuracy.
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Affiliation(s)
- Yassir Edrees Almalki
- Division of Radiology, Department of Internal Medicine, Medical College, Najran University, Najran 61441, Saudi Arabia;
| | - Mohammad Abd Alkhalik Basha
- Department of Diagnostic Radiology, Faculty of Human Medicine, Zagazig University, Zagazig 44519, Egypt; (M.I.M.); (N.A.Z.); (M.G.N.); (E.M.H.)
| | - Maha Ibrahim Metwally
- Department of Diagnostic Radiology, Faculty of Human Medicine, Zagazig University, Zagazig 44519, Egypt; (M.I.M.); (N.A.Z.); (M.G.N.); (E.M.H.)
| | - Nesma Adel Zeed
- Department of Diagnostic Radiology, Faculty of Human Medicine, Zagazig University, Zagazig 44519, Egypt; (M.I.M.); (N.A.Z.); (M.G.N.); (E.M.H.)
| | - Mohamad Gamal Nada
- Department of Diagnostic Radiology, Faculty of Human Medicine, Zagazig University, Zagazig 44519, Egypt; (M.I.M.); (N.A.Z.); (M.G.N.); (E.M.H.)
| | | | - Ahmed A. Morsy
- Department of Neurosurgery, Faculty of Human Medicine, Zagazig University, Zagazig 44519, Egypt;
| | - Rawda Balata
- Department of Clinical Oncology and Nuclear Medicine, Faculty of Human Medicine, Zagazig University, Zagazig 44519, Egypt; (R.B.); (A.Z.A.A.)
| | - Ahmed Z. Al Attar
- Department of Clinical Oncology and Nuclear Medicine, Faculty of Human Medicine, Zagazig University, Zagazig 44519, Egypt; (R.B.); (A.Z.A.A.)
| | - Mona M. Amer
- Department of Neurology, Faculty of Human Medicine, Zagazig University, Zagazig 44519, Egypt;
| | | | - Sameh Abdelaziz Aly
- Department of Diagnostic Radiology, Faculty of Human Medicine, Benha University, Benha 13511, Egypt;
| | | | - Enas Mahmoud Hamed
- Department of Diagnostic Radiology, Faculty of Human Medicine, Zagazig University, Zagazig 44519, Egypt; (M.I.M.); (N.A.Z.); (M.G.N.); (E.M.H.)
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2
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Ramos A, Hilario A. Standardized brain tumor reporting in the multidisciplinary spotlight: pros of the BT-RADS. Eur Radiol 2024:10.1007/s00330-024-10716-5. [PMID: 38583126 DOI: 10.1007/s00330-024-10716-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 02/27/2024] [Accepted: 03/07/2024] [Indexed: 04/08/2024]
Affiliation(s)
- Ana Ramos
- Radiology Department, Neuroradiology Section, University Hospital, 12 de Octubre, Madrid, Spain.
| | - Amaya Hilario
- Radiology Department, Neuroradiology Section, University Hospital, 12 de Octubre, Madrid, Spain
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3
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Bhattacharya K, Rastogi S, Mahajan A. Post-treatment imaging of gliomas: challenging the existing dogmas. Clin Radiol 2024; 79:e376-e392. [PMID: 38123395 DOI: 10.1016/j.crad.2023.11.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 10/23/2023] [Accepted: 11/21/2023] [Indexed: 12/23/2023]
Abstract
Gliomas are the commonest malignant central nervous system tumours in adults and imaging is the cornerstone of diagnosis, treatment, and post-treatment follow-up of these patients. With the ever-evolving treatment strategies post-treatment imaging and interpretation in glioma remains challenging, more so with the advent of anti-angiogenic drugs and immunotherapy, which can significantly alter the appearance in this setting, thus making interpretation of routine imaging findings such as contrast enhancement, oedema, and mass effect difficult to interpret. This review details the various methods of management of glioma including the upcoming novel therapies and their impact on imaging findings, with a comprehensive description of the imaging findings in conventional and advanced imaging techniques. A systematic appraisal for the existing and emerging techniques of imaging in these settings and their clinical application including various response assessment guidelines and artificial intelligence based response assessment will also be discussed.
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Affiliation(s)
- K Bhattacharya
- Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, Maharashtra, India
| | - S Rastogi
- Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, Maharashtra, India
| | - A Mahajan
- Department of imaging, The Clatterbridge Cancer Centre, NHS Foundation Trust, Pembroke Place, Liverpool L7 8YA, UK; University of Liverpool, Liverpool L69 3BX, UK.
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Liu W, Cai L, Li Y. Application of natural language processing to post-structuring of rectal cancer MRI reports. Clin Radiol 2024; 79:e204-e210. [PMID: 38042740 DOI: 10.1016/j.crad.2023.10.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 10/20/2023] [Accepted: 10/26/2023] [Indexed: 12/04/2023]
Abstract
AIM To evaluate a natural language processing (NLP) system for extracting structured information from the free-form text of rectal cancer magnetic resonance imaging (MRI) reports written in Chinese. MATERIALS AND METHODS A rule-based NLP model that could extract 11 key image features of rectal cancer was constructed using 358 MRI reports of rectal cancer written between 2015 and 2021. Fifty reports written before 2015 and 50 written after 2021 were used as test datasets, and the reference standard was determined by manual extraction of information by two radiologists. The length and reporting rate of image features in pre-2015 and post-2021 datasets, as well as the accuracy, precision, recall, and F1 score of feature extraction by the NLP system, were compared. The time required for the NLP to extract data was compared with that required by the radiologists. RESULTS Reports written after 2021 had longer diagnostic impression sections than reports written before 2015. The reporting rate of key imaging features of rectal cancer was 36.55% before 2015 and 79.82% after 2021. The accuracy, precision, recall, and F1 score of NLP for correct extraction of values from reports were 93.82%, 95.63%, 87.06%, and 91.15%, respectively, for pre-2015 reports, and 92.55%, 98.53%, 94.15%, and 96.29%, respectively, for post-2021 reports. NLP generated all the structured information in <1 second. CONCLUSIONS The NLP system with rule-based pattern matching achieved rapid and accurate structured processing of rectal cancer MRI reports. MRI reports with structured templates are more suitable for NLP-based extraction of information.
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Affiliation(s)
- W Liu
- Department of Radiology, Aerospace Center Hospital, Beijing, 100049, China; Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China
| | - L Cai
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Y Li
- Department of General Surgery, Aerospace Center Hospital, Beijing, 100049, China.
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5
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Ramesh KK, Xu KM, Trivedi AG, Huang V, Sharghi VK, Kleinberg LR, Mellon EA, Shu HKG, Shim H, Weinberg BD. A Fully Automated Post-Surgical Brain Tumor Segmentation Model for Radiation Treatment Planning and Longitudinal Tracking. Cancers (Basel) 2023; 15:3956. [PMID: 37568773 PMCID: PMC10417353 DOI: 10.3390/cancers15153956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 07/26/2023] [Accepted: 08/02/2023] [Indexed: 08/13/2023] Open
Abstract
Glioblastoma (GBM) has a poor survival rate even with aggressive surgery, concomitant radiation therapy (RT), and adjuvant chemotherapy. Standard-of-care RT involves irradiating a lower dose to the hyperintense lesion in T2-weighted fluid-attenuated inversion recovery MRI (T2w/FLAIR) and a higher dose to the enhancing tumor on contrast-enhanced, T1-weighted MRI (CE-T1w). While there have been several attempts to segment pre-surgical brain tumors, there have been minimal efforts to segment post-surgical tumors, which are complicated by a resection cavity and postoperative blood products, and tools are needed to assist physicians in generating treatment contours and assessing treated patients on follow up. This report is one of the first to train and test multiple deep learning models for the purpose of post-surgical brain tumor segmentation for RT planning and longitudinal tracking. Post-surgical FLAIR and CE-T1w MRIs, as well as their corresponding RT targets (GTV1 and GTV2, respectively) from 225 GBM patients treated with standard RT were trained on multiple deep learning models including: Unet, ResUnet, Swin-Unet, 3D Unet, and Swin-UNETR. These models were tested on an independent dataset of 30 GBM patients with the Dice metric used to evaluate segmentation accuracy. Finally, the best-performing segmentation model was integrated into our longitudinal tracking web application to assign automated structured reporting scores using change in percent cutoffs of lesion volume. The 3D Unet was our best-performing model with mean Dice scores of 0.72 for GTV1 and 0.73 for GTV2 with a standard deviation of 0.17 for both in the test dataset. We have successfully developed a lightweight post-surgical segmentation model for RT planning and longitudinal tracking.
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Affiliation(s)
- Karthik K. Ramesh
- Department of Radiation Oncology, Emory University School of Medicine, Atlanta, GA 30322, USA; (K.K.R.); (A.G.T.); (V.H.)
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Karen M. Xu
- Department of Radiation Oncology, Emory University School of Medicine, Atlanta, GA 30322, USA; (K.K.R.); (A.G.T.); (V.H.)
| | - Anuradha G. Trivedi
- Department of Radiation Oncology, Emory University School of Medicine, Atlanta, GA 30322, USA; (K.K.R.); (A.G.T.); (V.H.)
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Vicki Huang
- Department of Radiation Oncology, Emory University School of Medicine, Atlanta, GA 30322, USA; (K.K.R.); (A.G.T.); (V.H.)
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA 30332, USA
| | | | - Lawrence R. Kleinberg
- Department of Radiation Oncology, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Eric A. Mellon
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL 45056, USA
| | - Hui-Kuo G. Shu
- Department of Radiation Oncology, Emory University School of Medicine, Atlanta, GA 30322, USA; (K.K.R.); (A.G.T.); (V.H.)
- Winship Cancer Institute, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Hyunsuk Shim
- Department of Radiation Oncology, Emory University School of Medicine, Atlanta, GA 30322, USA; (K.K.R.); (A.G.T.); (V.H.)
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA 30332, USA
- Winship Cancer Institute, Emory University School of Medicine, Atlanta, GA 30322, USA
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Brent D. Weinberg
- Winship Cancer Institute, Emory University School of Medicine, Atlanta, GA 30322, USA
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA 30322, USA
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6
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Abidi SA, Hoch MJ, Hu R, Sadigh G, Voloschin A, Olson JJ, Shu HKG, Neill SG, Weinberg BD. Using Brain Tumor MRI Structured Reporting to Quantify the Impact of Imaging on Brain Tumor Boards. Tomography 2023; 9:859-870. [PMID: 37104141 PMCID: PMC10146901 DOI: 10.3390/tomography9020070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 04/06/2023] [Accepted: 04/13/2023] [Indexed: 04/28/2023] Open
Abstract
Multidisciplinary tumor boards (TB) are an essential part of brain tumor care, but quantifying the impact of imaging on patient management is challenging due to treatment complexity and a lack of quantitative outcome measures. This work uses a structured reporting system for classifying brain tumor MRIs, the brain tumor reporting and data system (BT-RADS), in a TB setting to prospectively assess the impact of imaging review on patient management. Published criteria were used to prospectively assign three separate BT-RADS scores (an initial radiology report, secondary TB presenter review, and TB consensus) to brain MRIs reviewed at an adult brain TB. Clinical recommendations at TB were noted and management changes within 90 days after TB were determined by chart review. In total, 212 MRIs in 130 patients (median age = 57 years) were reviewed. Agreement was 82.2% between report and presenter, 79.0% between report and consensus, and 90.1% between presenter and consensus. Rates of management change increased with increasing BT-RADS scores (0-3.1%, 1a-0%, 1b-66.7%, 2-8.3%, 3a-38.5%, 3b-55.9, 3c-92.0%, and 4-95.6%). Of 184 (86.8%) cases with clinical follow-up within 90 days after the tumor board, 155 (84.2%) of the recommendations were implemented. Structured scoring of MRIs provides a quantitative way to assess rates of agreement interpretation alongside how often management changes are recommended and implemented in a TB setting.
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Affiliation(s)
- Syed A Abidi
- Mallinckrodt Institute of Radiology, Washington University, St. Louis, MO 63110, USA
| | - Michael J Hoch
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ranliang Hu
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA 30322, USA
| | - Gelareh Sadigh
- Department of Radiology, University of California-Irvine, Irvine, CA 92868, USA
| | - Alfredo Voloschin
- Department of Neuro-Oncology, Orlando Health Cancer Institute, Orlando, FL 32806, USA
| | - Jeffrey J Olson
- Department of Neurosurgery, Emory University, Atlanta, GA 30322, USA
| | - Hui-Kuo G Shu
- Department of Radiation Oncology, Emory University, Atlanta, GA 30322, USA
| | - Stewart G Neill
- Department of Pathology, Emory University, Atlanta, GA 30322, USA
| | - Brent D Weinberg
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA 30322, USA
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7
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Stember JN, Young RJ, Shalu H. Direct Evaluation of Treatment Response in Brain Metastatic Disease with Deep Neuroevolution. J Digit Imaging 2023; 36:536-546. [PMID: 36396839 PMCID: PMC10039135 DOI: 10.1007/s10278-022-00725-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 09/29/2022] [Accepted: 10/25/2022] [Indexed: 11/18/2022] Open
Abstract
Cancer centers have an urgent and unmet clinical and research need for AI that can guide patient management. A core component of advancing cancer treatment research is assessing response to therapy. Doing so by hand, for example, as per RECIST or RANO criteria, is tedious and time-consuming, and can miss important tumor response information. Most notably, the prevalent response criteria often exclude lesions, the non-target lesions, altogether. We wish to assess change in a holistic fashion that includes all lesions, obtaining simple, informative, and automated assessments of tumor progression or regression. Because genetic sub-types of cancer can be fairly specific and patient enrollment in therapy trials is often limited in number and accrual rate, we wish to make response assessments with small training sets. Deep neuroevolution (DNE) is a novel radiology artificial intelligence (AI) optimization approach that performs well on small training sets. Here, we use a DNE parameter search to optimize a convolutional neural network (CNN) that predicts progression versus regression of metastatic brain disease. We analyzed 50 pairs of MRI contrast-enhanced images as our training set. Half of these pairs, separated in time, qualified as disease progression, while the other 25 image pairs constituted regression. We trained the parameters of a CNN via "mutations" that consisted of random CNN weight adjustments and evaluated mutation "fitness" as summed training set accuracy. We then incorporated the best mutations into the next generation's CNN, repeating this process for approximately 50,000 generations. We applied the CNNs to our training set, as well as a separate testing set with the same class balance of 25 progression and 25 regression cases. DNE achieved monotonic convergence to 100% training set accuracy. DNE also converged monotonically to 100% testing set accuracy. We have thus shown that DNE can accurately classify brain metastatic disease progression versus regression. Future work will extend the input from 2D image slices to full 3D volumes, and include the category of "no change." We believe that an approach such as ours can ultimately provide a useful and informative complement to RANO/RECIST assessment and volumetric AI analysis.
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Affiliation(s)
- Joseph N Stember
- Memorial Sloan Kettering Cancer Center, 1275 York Avenue, NY, NY, 10065, USA.
| | - Robert J Young
- Memorial Sloan Kettering Cancer Center, 1275 York Avenue, NY, NY, 10065, USA
| | - Hrithwik Shalu
- Indian Institute of Technology Madras, Madras, Chennai, 600036, India
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Huang V, Rejimon A, Reddy K, Trivedi AG, Ramesh KK, Giuffrida AS, Muiruri R, Shim H, Eaton BR. Spectroscopic MRI-Guided Proton Therapy in Non-Enhancing Pediatric High-Grade Glioma. Tomography 2023; 9:633-646. [PMID: 36961010 PMCID: PMC10037577 DOI: 10.3390/tomography9020051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 02/24/2023] [Accepted: 03/06/2023] [Indexed: 03/12/2023] Open
Abstract
Radiation therapy (RT) is a critical part of definitive therapy for pediatric high-grade glioma (pHGG). RT is designed to treat residual tumor defined on conventional MRI (cMRI), though pHGG lesions may be ill-characterized on standard imaging. Spectroscopic MRI (sMRI) measures endogenous metabolite concentrations in the brain, and Choline (Cho)/N-acetylaspartate (NAA) ratio is a highly sensitive biomarker for metabolically active tumor. We provide a preliminary report of our study introducing a novel treatment approach of whole brain sMRI-guided proton therapy for pHGG. An observational cohort (c1 = 10 patients) receives standard of care RT; a therapeutic cohort (c2 = 15 patients) receives sMRI-guided proton RT. All patients undergo cMRI and sMRI, a high-resolution 3D whole-brain echo-planar spectroscopic imaging (EPSI) sequence (interpolated resolution of 12 µL) prior to RT and at several follow-up timepoints integrated into diagnostic scans. Treatment volumes are defined by cMRI for c1 and by cMRI and Cho/NAA ≥ 2x for c2. A longitudinal imaging database is used to quantify changes in lesion and metabolite volumes. Four subjects have been enrolled (c1 = 1/c2 = 3) with sMRI imaging follow-up of 4-18 months. Preliminary data suggest sMRI improves identification of pHGG infiltration based on abnormal metabolic activity, and using proton therapy to target sMRI-defined high-risk regions is safe and feasible.
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Affiliation(s)
- Vicki Huang
- Department of Radiation Oncology, Emory University School of Medicine, Atlanta, GA 30322, USA
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Abinand Rejimon
- Department of Radiation Oncology, Emory University School of Medicine, Atlanta, GA 30322, USA
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Kartik Reddy
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA 30322, USA
- Department of Radiology, Children’s Healthcare of Atlanta, Atlanta, GA 30342, USA
| | - Anuradha G. Trivedi
- Department of Radiation Oncology, Emory University School of Medicine, Atlanta, GA 30322, USA
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Karthik K. Ramesh
- Department of Radiation Oncology, Emory University School of Medicine, Atlanta, GA 30322, USA
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Alexander S. Giuffrida
- Department of Radiation Oncology, Emory University School of Medicine, Atlanta, GA 30322, USA
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Robert Muiruri
- Winship Cancer Institute, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Hyunsuk Shim
- Department of Radiation Oncology, Emory University School of Medicine, Atlanta, GA 30322, USA
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA 30332, USA
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA 30322, USA
- Winship Cancer Institute, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Bree R. Eaton
- Department of Radiation Oncology, Emory University School of Medicine, Atlanta, GA 30322, USA
- Department of Radiology, Children’s Healthcare of Atlanta, Atlanta, GA 30342, USA
- Winship Cancer Institute, Emory University School of Medicine, Atlanta, GA 30322, USA
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9
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Ramesh KK, Huang V, Rosenthal J, Mellon EA, Goryawala M, Barker PB, Gurbani SS, Trivedi AG, Giuffrida AS, Schreibmann E, Han H, de le Fuente M, Dunbar EM, Holdhoff M, Kleinberg LR, Shu HKG, Shim H, Weinberg BD. A Novel Approach to Determining Tumor Progression Using a Three-Site Pilot Clinical Trial of Spectroscopic MRI-Guided Radiation Dose Escalation in Glioblastoma. Tomography 2023; 9:362-374. [PMID: 36828381 PMCID: PMC9964256 DOI: 10.3390/tomography9010029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Revised: 02/01/2023] [Accepted: 02/02/2023] [Indexed: 02/09/2023] Open
Abstract
Glioblastoma (GBM) is a fatal disease, with poor prognosis exacerbated by difficulty in assessing tumor extent with imaging. Spectroscopic MRI (sMRI) is a non-contrast imaging technique measuring endogenous metabolite levels of the brain that can serve as biomarkers for tumor extension. We completed a three-site study to assess survival benefits of GBM patients when treated with escalated radiation dose guided by metabolic abnormalities in sMRI. Escalated radiation led to complex post-treatment imaging, requiring unique approaches to discern tumor progression from radiation-related treatment effect through our quantitative imaging platform. The purpose of this study is to determine true tumor recurrence timepoints for patients in our dose-escalation multisite study using novel methodology and to report on median progression-free survival (PFS). Follow-up imaging for all 30 trial patients were collected, lesion volumes segmented and graphed, and imaging uploaded to our platform for visual interpretation. Eighteen months post-enrollment, the median PFS was 16.6 months with a median time to follow-up of 20.3 months. With this new treatment paradigm, incidence rate of tumor recurrence one year from treatment is 30% compared to 60-70% failure under standard care. Based on the delayed tumor progression and improved survival, a randomized phase II trial is under development (EAF211).
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Affiliation(s)
- Karthik K. Ramesh
- Department of Radiation Oncology, Emory University School of Medicine, Atlanta, GA 30322, USA
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30322, USA
| | - Vicki Huang
- Department of Radiation Oncology, Emory University School of Medicine, Atlanta, GA 30322, USA
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30322, USA
| | - Jeffrey Rosenthal
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Eric A. Mellon
- Department of Radiation Oncology, University of Miami, Miami, FL 45056, USA
| | | | - Peter B. Barker
- Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Saumya S. Gurbani
- Department of Radiation Oncology, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Anuradha G. Trivedi
- Department of Radiation Oncology, Emory University School of Medicine, Atlanta, GA 30322, USA
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30322, USA
| | - Alexander S. Giuffrida
- Department of Radiation Oncology, Emory University School of Medicine, Atlanta, GA 30322, USA
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30322, USA
| | - Eduard Schreibmann
- Department of Radiation Oncology, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Hui Han
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | | | - Erin M. Dunbar
- Department of Neuro-Oncology and Neurosurgery, Piedmont Atlanta Hospital, Atlanta, GA 30309, USA
| | - Matthias Holdhoff
- Department of Oncology, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Lawrence R. Kleinberg
- Department of Radiation Oncology, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Hui-Kuo G. Shu
- Department of Radiation Oncology, Emory University School of Medicine, Atlanta, GA 30322, USA
- Winship Cancer Institute, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Hyunsuk Shim
- Department of Radiation Oncology, Emory University School of Medicine, Atlanta, GA 30322, USA
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30322, USA
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA 30322, USA
- Winship Cancer Institute, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Brent D. Weinberg
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA 30322, USA
- Winship Cancer Institute, Emory University School of Medicine, Atlanta, GA 30322, USA
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Ramakrishnan D, von Reppert M, Krycia M, Sala M, Mueller S, Aneja S, Nabavizadeh A, Galldiks N, Lohmann P, Raji C, Ikuta I, Memon F, Weinberg BD, Aboian MS. Evolution and implementation of radiographic response criteria in neuro-oncology. Neurooncol Adv 2023; 5:vdad118. [PMID: 37860269 PMCID: PMC10584081 DOI: 10.1093/noajnl/vdad118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2023] Open
Abstract
Radiographic response assessment in neuro-oncology is critical in clinical practice and trials. Conventional criteria, such as the MacDonald and response assessment in neuro-oncology (RANO) criteria, rely on bidimensional (2D) measurements of a single tumor cross-section. Although RANO criteria are established for response assessment in clinical trials, there is a critical need to address the complexity of brain tumor treatment response with multiple new approaches being proposed. These include volumetric analysis of tumor compartments, structured MRI reporting systems like the Brain Tumor Reporting and Data System, and standardized approaches to advanced imaging techniques to distinguish tumor response from treatment effects. In this review, we discuss the strengths and limitations of different neuro-oncology response criteria and summarize current research findings on the role of novel response methods in neuro-oncology clinical trials and practice.
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Affiliation(s)
- Divya Ramakrishnan
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Marc von Reppert
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Mark Krycia
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Matthew Sala
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
- Tulane University School of Medicine, New Orleans, Louisiana, USA
| | - Sabine Mueller
- Department of Neurology, Neurosurgery, and Pediatrics, University of California San Francisco, San Francisco, California, USA
| | - Sanjay Aneja
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Ali Nabavizadeh
- Department of Radiology, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA
| | - Norbert Galldiks
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Institute of Neuroscience and Medicine (INM-3), Research Center Juelich, Juelich, Germany
- Center for Integrated Oncology (CIO), Universities of Aachen, Bonn, Cologne, and Duesseldorf, Cologne, Germany
| | - Philipp Lohmann
- Institute of Neuroscience and Medicine (INM-4), Research Center Juelich, Juelich, Germany
| | - Cyrus Raji
- Department of Radiology, Washington University in St. Louis School of Medicine, St. Louis, Missouri, USA
| | - Ichiro Ikuta
- Department of Radiology, Mayo Clinic, Phoenix, Arizona, USA
| | - Fatima Memon
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
| | - Brent D Weinberg
- Department of Radiology, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Mariam S Aboian
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA
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11
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Kim S, Hoch MJ, Peng L, Somasundaram A, Chen Z, Weinberg BD. A brain tumor reporting and data system to optimize imaging surveillance and prognostication in high-grade gliomas. J Neuroimaging 2022; 32:1185-1192. [PMID: 36045502 DOI: 10.1111/jon.13044] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 08/11/2022] [Accepted: 08/17/2022] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND AND PURPOSE High-grade glioma (HGG), including glioblastoma, is the most common primary brain neoplasm and has a dismal prognosis. After initial treatment, follow-up decisions are guided by longitudinal MRI performed at routine intervals. The Brain Tumor Reporting and Data System (BT-RADS) is a proposed structured reporting system for posttreatment brain MRIs. The purpose of this study is to determine the relationship between BT-RADS scores and overall survival in HGG patients. METHODS Chart review of grade 4 glioma patients who had an MRI at a single institution from November 2018 to November 2019 was performed. BT-RADS scores, tumor characteristics, and overall survival were recorded. Likelihood of improvement, stability, or worsening on the subsequent study was calculated for each score. Survival analysis was performed using Kaplan-Meier method, log-rank test, and a time-dependent cox model. Significance level of .05 was used. RESULTS The study identified 91 HGG patients who underwent a total of 538 MRIs. Mean age of patients was 57 years old. Score with the highest likelihood for worsening on the next follow-up was 3b. The risk of death was 53% higher with each incremental increase in BT-RADS scores (hazard ratio, 1.53; 95% confidence interval [CI], 1.07-2.19; p = .019). The risk of death was 167% higher in O-6-methylguanine-DNA-methyltransferase unmethylated tumors (hazard ratio, 2.67; 95% CI, 1.34-5.33; p = .005). CONCLUSIONS BT-RADS scores can be used as a reference guide to anticipate whether patients' subsequent MRI will be improved, stable, or worsened. The scoring system can also be used to predict clinical outcomes and prognosis.
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Affiliation(s)
- Sera Kim
- Department of Radiology, University of California, San Francisco, San Francisco, California, USA
| | - Michael J Hoch
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Lingyi Peng
- Department of Statistics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Aravind Somasundaram
- Department of Radiology and Imaging Sciences, Emory University Hospital, Atlanta, Georgia, USA
| | - Zhengjia Chen
- Division of Epidemiology and Biostatistics, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Brent D Weinberg
- Department of Radiology and Imaging Sciences, Emory University Hospital, Atlanta, Georgia, USA
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12
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MRT-Befundung hirneigener Tumoren. DIE RADIOLOGIE 2022; 62:683-691. [PMID: 35913575 PMCID: PMC9343312 DOI: 10.1007/s00117-022-01014-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Accepted: 05/02/2022] [Indexed: 11/26/2022]
Abstract
Hintergrund und Ziel Eine strukturierte MRT-Befundung unter Verwendung konsensbasierter inhaltlicher Kategorien hat das Potenzial, die interdisziplinäre Kommunikation in der Neuroonkologie zu verbessern. Ziel dieser Studie war es daher, mittels einer bundesweiten Befragung von Mitgliedern medizinischer Fachgesellschaften mit neuroonkologischem Bezug die wesentlichen Befundungskategorien der Bildgebung hirneigener Tumoren aus klinischer Perspektive zu ermitteln. Material und Methoden Auf der Basis eines interdisziplinär entwickelten Katalogs von MRT-Befundungselementen wurde ein Online-Fragebogen erstellt. Im Anschluss wurden fachärztliche Mitglieder der Deutschen Gesellschaften für Neurochirurgie, Radioonkologie, Hämatologie und Medizinische Onkologie, Neurologie und Neuropathologie dazu eingeladen, die Items hinsichtlich ihrer klinischen Relevanz zu bewerten. Ergebnisse An der Umfrage nahmen insgesamt 171 Fachärzte aus dem Bundesgebiet teil (81 Neurochirurgen, 66 Strahlentherapeuten und 24 andere neuroonkologische Experten). Anzahl und anatomische Ausdehnung der Tumoren in einer kontrastmittelverstärkten T1- und 2‑D-T2-Sequenz (98,8 % vs. 97,1 %) sowie neu diagnostizierte Läsionen bei Folgeuntersuchungen (T1 + Kontrast 98,2 %; T2 94,7 %) wurden am häufigsten als essenziell betrachtet. Darüber hinaus beurteilten die Experten insbesondere die Beschreibung einer ependymalen und/oder leptomeningealen Tumordissemination (93,6 %) sowie Zeichen der Raumforderung inklusive Verschlusshydrozephalus und parenchymale Massenverschiebungen (jeweils > 75,0 %) als wesentlich. Eine standardmäßige Erwähnung von intratumoralen Verkalkungen, Hämorrhagien, Tumorgefäßarchitektur oder erweiterter Bildgebungsmethoden wie MR-Perfusion, Diffusion, Traktographie und Protonenspektroskopie bewertete lediglich eine Minderheit der Umfrageteilnehmer als praxisrelevant. Schlussfolgerung Ein zuweiserorientierter inhaltlicher Mindeststandard der magnetresonanztomographischen Hirntumordiagnostik sollte als klinisch relevante Kernelemente die exakte anatomische Ausbreitung der Raumforderung(en) inklusive ependymaler und meningealer Beteiligung sowie die einschlägigen Raumforderungszeichen enthalten.
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13
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Li AY, Iv M. Conventional and Advanced Imaging Techniques in Post-treatment Glioma Imaging. FRONTIERS IN RADIOLOGY 2022; 2:883293. [PMID: 37492665 PMCID: PMC10365131 DOI: 10.3389/fradi.2022.883293] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 06/06/2022] [Indexed: 07/27/2023]
Abstract
Despite decades of advancement in the diagnosis and therapy of gliomas, the most malignant primary brain tumors, the overall survival rate is still dismal, and their post-treatment imaging appearance remains very challenging to interpret. Since the limitations of conventional magnetic resonance imaging (MRI) in the distinction between recurrence and treatment effect have been recognized, a variety of advanced MR and functional imaging techniques including diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), perfusion-weighted imaging (PWI), MR spectroscopy (MRS), as well as a variety of radiotracers for single photon emission computed tomography (SPECT) and positron emission tomography (PET) have been investigated for this indication along with voxel-based and more quantitative analytical methods in recent years. Machine learning and radiomics approaches in recent years have shown promise in distinguishing between recurrence and treatment effect as well as improving prognostication in a malignancy with a very short life expectancy. This review provides a comprehensive overview of the conventional and advanced imaging techniques with the potential to differentiate recurrence from treatment effect and includes updates in the state-of-the-art in advanced imaging with a brief overview of emerging experimental techniques. A series of representative cases are provided to illustrate the synthesis of conventional and advanced imaging with the clinical context which informs the radiologic evaluation of gliomas in the post-treatment setting.
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Affiliation(s)
- Anna Y. Li
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, United States
| | - Michael Iv
- Division of Neuroimaging and Neurointervention, Department of Radiology, Stanford University School of Medicine, Stanford, CA, United States
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14
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Clinical Routine and Necessary Advances in Soft Tissue Tumor Imaging Based on the ESSR Guideline: Initial Findings. Tomography 2022; 8:1586-1594. [PMID: 35736879 PMCID: PMC9228892 DOI: 10.3390/tomography8030131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Revised: 06/06/2022] [Accepted: 06/13/2022] [Indexed: 11/16/2022] Open
Abstract
Soft tissue sarcomas are malignant diseases with a complex classification and various histological subtypes, mostly clinically inconspicuous appearance, and a rare occurrence. To ensure safe patient care, the European Society of Musculoskeletal Radiology (ESSR) issued a guideline for diagnostic imaging of soft tissue tumors in adults in 2015. In this study, we investigated whether implementation of these guidelines resulted in improved MRI protocol and report quality in patients with soft tissue sarcomas in our cancer center. All cases of histologically confirmed soft tissue sarcomas that were treated at our study center from 2006 to 2018 were evaluated retrospectively. The radiological reports were examined for their compliance with the recommendations of the ESSR. Patients were divided into two groups, before and after the introduction of the 2015 ESSR guidelines. In total, 103 cases of histologically confirmed sarcomas were studied. The distribution of, age, gender, number of subjects, performing radiology, and MRI indication on both groups did not show any significant differences. Only using the required MRI sequences showed a significant improvement after the introduction of the guidelines (p = 0.048). All other criteria, especially the requirements for the report of findings, showed no improvement. The guidelines of the European Society for Musculoskeletal Radiology are not regularly followed, and their establishment did not consistently improve MRI quality in our study group. This poses a risk for incorrect or delayed diagnosis and, ultimately, therapy of soft tissue tumors. However, this study is the first of its kind and involves a limited collective. A European-wide multicenter study would be appreciated to confirm these results.
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15
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Tadavarthi Y, Makeeva V, Wagstaff W, Zhan H, Podlasek A, Bhatia N, Heilbrun M, Krupinski E, Safdar N, Banerjee I, Gichoya J, Trivedi H. Overview of Noninterpretive Artificial Intelligence Models for Safety, Quality, Workflow, and Education Applications in Radiology Practice. Radiol Artif Intell 2022; 4:e210114. [PMID: 35391770 DOI: 10.1148/ryai.210114] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 12/17/2021] [Accepted: 01/11/2022] [Indexed: 12/17/2022]
Abstract
Artificial intelligence has become a ubiquitous term in radiology over the past several years, and much attention has been given to applications that aid radiologists in the detection of abnormalities and diagnosis of diseases. However, there are many potential applications related to radiologic image quality, safety, and workflow improvements that present equal, if not greater, value propositions to radiology practices, insurance companies, and hospital systems. This review focuses on six major categories for artificial intelligence applications: study selection and protocoling, image acquisition, worklist prioritization, study reporting, business applications, and resident education. All of these categories can substantially affect different aspects of radiology practices and workflows. Each of these categories has different value propositions in terms of whether they could be used to increase efficiency, improve patient safety, increase revenue, or save costs. Each application is covered in depth in the context of both current and future areas of work. Keywords: Use of AI in Education, Application Domain, Supervised Learning, Safety © RSNA, 2022.
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Affiliation(s)
- Yasasvi Tadavarthi
- Department of Medicine, Medical College of Georgia, Augusta, Ga (Y.T.); Department of Radiology and Imaging Sciences (V.M., W.W., H.Z., M.H., E.K., N.S., J.G., H.T.), School of Medicine (N.B.), and Department of Biomedical Informatics (I.B.), Emory University, 1364 E Clifton Rd NE, Atlanta, GA 30322; and Southend University Hospital NHS Foundation Trust, Westcliff-on-Sea, UK (A.P.)
| | - Valeria Makeeva
- Department of Medicine, Medical College of Georgia, Augusta, Ga (Y.T.); Department of Radiology and Imaging Sciences (V.M., W.W., H.Z., M.H., E.K., N.S., J.G., H.T.), School of Medicine (N.B.), and Department of Biomedical Informatics (I.B.), Emory University, 1364 E Clifton Rd NE, Atlanta, GA 30322; and Southend University Hospital NHS Foundation Trust, Westcliff-on-Sea, UK (A.P.)
| | - William Wagstaff
- Department of Medicine, Medical College of Georgia, Augusta, Ga (Y.T.); Department of Radiology and Imaging Sciences (V.M., W.W., H.Z., M.H., E.K., N.S., J.G., H.T.), School of Medicine (N.B.), and Department of Biomedical Informatics (I.B.), Emory University, 1364 E Clifton Rd NE, Atlanta, GA 30322; and Southend University Hospital NHS Foundation Trust, Westcliff-on-Sea, UK (A.P.)
| | - Henry Zhan
- Department of Medicine, Medical College of Georgia, Augusta, Ga (Y.T.); Department of Radiology and Imaging Sciences (V.M., W.W., H.Z., M.H., E.K., N.S., J.G., H.T.), School of Medicine (N.B.), and Department of Biomedical Informatics (I.B.), Emory University, 1364 E Clifton Rd NE, Atlanta, GA 30322; and Southend University Hospital NHS Foundation Trust, Westcliff-on-Sea, UK (A.P.)
| | - Anna Podlasek
- Department of Medicine, Medical College of Georgia, Augusta, Ga (Y.T.); Department of Radiology and Imaging Sciences (V.M., W.W., H.Z., M.H., E.K., N.S., J.G., H.T.), School of Medicine (N.B.), and Department of Biomedical Informatics (I.B.), Emory University, 1364 E Clifton Rd NE, Atlanta, GA 30322; and Southend University Hospital NHS Foundation Trust, Westcliff-on-Sea, UK (A.P.)
| | - Neil Bhatia
- Department of Medicine, Medical College of Georgia, Augusta, Ga (Y.T.); Department of Radiology and Imaging Sciences (V.M., W.W., H.Z., M.H., E.K., N.S., J.G., H.T.), School of Medicine (N.B.), and Department of Biomedical Informatics (I.B.), Emory University, 1364 E Clifton Rd NE, Atlanta, GA 30322; and Southend University Hospital NHS Foundation Trust, Westcliff-on-Sea, UK (A.P.)
| | - Marta Heilbrun
- Department of Medicine, Medical College of Georgia, Augusta, Ga (Y.T.); Department of Radiology and Imaging Sciences (V.M., W.W., H.Z., M.H., E.K., N.S., J.G., H.T.), School of Medicine (N.B.), and Department of Biomedical Informatics (I.B.), Emory University, 1364 E Clifton Rd NE, Atlanta, GA 30322; and Southend University Hospital NHS Foundation Trust, Westcliff-on-Sea, UK (A.P.)
| | - Elizabeth Krupinski
- Department of Medicine, Medical College of Georgia, Augusta, Ga (Y.T.); Department of Radiology and Imaging Sciences (V.M., W.W., H.Z., M.H., E.K., N.S., J.G., H.T.), School of Medicine (N.B.), and Department of Biomedical Informatics (I.B.), Emory University, 1364 E Clifton Rd NE, Atlanta, GA 30322; and Southend University Hospital NHS Foundation Trust, Westcliff-on-Sea, UK (A.P.)
| | - Nabile Safdar
- Department of Medicine, Medical College of Georgia, Augusta, Ga (Y.T.); Department of Radiology and Imaging Sciences (V.M., W.W., H.Z., M.H., E.K., N.S., J.G., H.T.), School of Medicine (N.B.), and Department of Biomedical Informatics (I.B.), Emory University, 1364 E Clifton Rd NE, Atlanta, GA 30322; and Southend University Hospital NHS Foundation Trust, Westcliff-on-Sea, UK (A.P.)
| | - Imon Banerjee
- Department of Medicine, Medical College of Georgia, Augusta, Ga (Y.T.); Department of Radiology and Imaging Sciences (V.M., W.W., H.Z., M.H., E.K., N.S., J.G., H.T.), School of Medicine (N.B.), and Department of Biomedical Informatics (I.B.), Emory University, 1364 E Clifton Rd NE, Atlanta, GA 30322; and Southend University Hospital NHS Foundation Trust, Westcliff-on-Sea, UK (A.P.)
| | - Judy Gichoya
- Department of Medicine, Medical College of Georgia, Augusta, Ga (Y.T.); Department of Radiology and Imaging Sciences (V.M., W.W., H.Z., M.H., E.K., N.S., J.G., H.T.), School of Medicine (N.B.), and Department of Biomedical Informatics (I.B.), Emory University, 1364 E Clifton Rd NE, Atlanta, GA 30322; and Southend University Hospital NHS Foundation Trust, Westcliff-on-Sea, UK (A.P.)
| | - Hari Trivedi
- Department of Medicine, Medical College of Georgia, Augusta, Ga (Y.T.); Department of Radiology and Imaging Sciences (V.M., W.W., H.Z., M.H., E.K., N.S., J.G., H.T.), School of Medicine (N.B.), and Department of Biomedical Informatics (I.B.), Emory University, 1364 E Clifton Rd NE, Atlanta, GA 30322; and Southend University Hospital NHS Foundation Trust, Westcliff-on-Sea, UK (A.P.)
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16
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Puranik AD, Rangarajan V, Dev ID, Jain Y, Purandare NC, Sahu A, Choudhary A, Gupta T, Chatterjee A, Moiyadi A, Shetty P, Sridhar E, Sahay A, Patil VM, Shah S, Agrawal A. Brain FET PET tumor-to-white mater ratio to differentiate recurrence from post-treatment changes in high-grade gliomas. J Neuroimaging 2021; 31:1211-1218. [PMID: 34388273 DOI: 10.1111/jon.12914] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 07/17/2021] [Accepted: 07/17/2021] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND AND PURPOSE Highergrade glial neoplasms undergo standard treatment with surgery, radiotherapy, and alkylating agents. There is often a clinical/neuroimaging dilemma in the post-treatment setting to differentiate disease recurrence from treatment-related changes. FET (fluoro-ethyl-tyrosine) PET has emerged as a molecular imaging modality for cases where MR imaging is inconclusive. This study aims to develop a cutoff on FET PET for differentiating true recurrence from post-treatment changes. METHODS We retrospectively analyzed72 patientswith post-treatment grade 3 or 4 brain gliomas. Five to six mCi of 18 F-FET was injected and static imaging of the brain was performed at 20 min. A tumor-to-white matter (T/Wm) ratio was used as semiquantitative parameter. A T/Wm cutoff of 2.5 was used for image interpretation. Imaging findings were confirmed by either histopathologic diagnosis in a multidisciplinary joint clinic or based on follow-up of clinical and neuroimaging findings. RESULTS Forty-one of 72 patients (57%) showed recurrent disease on FET PET. Thirty-five of them were confirmed to have tumor recurrence; six patients showed post-treatment changes. Thirty-one of 72 patients (43%) showed post-treatment changes on FET PET; 27 were confirmed as post-treatment change and four patients had tumor recurrence on subsequent MR imaging. An optimum T/Wm cutoff of 2.65 was derived based on receiver operating characteristic analysis with a sensitivity of 80% and specificity of 87.5%. CONCLUSION Static FET PET can be used as problem-solving imaging modality with a T/Wm cutoff of 2.65 to differentiate late recurrence from post-treatment changes in grade 3 or 4 brain gliomas with equivocal MR features.
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Affiliation(s)
- Ameya D Puranik
- Department of Nuclear Medicine and Molecular Imaging, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, India
| | - Venkatesh Rangarajan
- Department of Nuclear Medicine and Molecular Imaging, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, India
| | - Indraja D Dev
- Department of Nuclear Medicine and Molecular Imaging, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, India
| | - Yash Jain
- Department of Nuclear Medicine and Molecular Imaging, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, India
| | - Nilendu C Purandare
- Department of Nuclear Medicine and Molecular Imaging, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, India
| | - Arpita Sahu
- Department of Radiodiagnosis, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, India
| | - Amitkumar Choudhary
- Department of Radiodiagnosis, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, India
| | - Tejpal Gupta
- Department of Radiation Oncology, ACTREC, Tata Memorial Centre, Homi Bhabha National Institute, Navi Mumbai, India
| | - Abhishek Chatterjee
- Department of Radiation Oncology, ACTREC, Tata Memorial Centre, Homi Bhabha National Institute, Navi Mumbai, India
| | - Aliasgar Moiyadi
- Department of Neuro-surgery, ACTREC, Tata Memorial Centre, Homi Bhabha National Institute, Navi Mumbai, India
| | - Prakash Shetty
- Department of Neuro-surgery, ACTREC, Tata Memorial Centre, Homi Bhabha National Institute, Navi Mumbai, India
| | - Epari Sridhar
- Department of Pathology, Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, India
| | - Ayushi Sahay
- Department of Pathology, Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, India
| | - Vijay M Patil
- Department of Medical Oncology, Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, India
| | - Sneha Shah
- Department of Nuclear Medicine and Molecular Imaging, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, India
| | - Archi Agrawal
- Department of Nuclear Medicine and Molecular Imaging, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, India
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17
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Ramesh K, Gurbani SS, Mellon EA, Huang V, Goryawala M, Barker PB, Kleinberg L, Shu HKG, Shim H, Weinberg BD. The Longitudinal Imaging Tracker (BrICS-LIT):A Cloud Platform for Monitoring Treatment Response in Glioblastoma Patients. ACTA ACUST UNITED AC 2021; 6:93-100. [PMID: 32548285 PMCID: PMC7289246 DOI: 10.18383/j.tom.2020.00001] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Glioblastoma is a common and aggressive form of brain cancer affecting up to 20,000 new patients in the US annually. Despite rigorous therapies, current median survival is only 15-20 months. Patients who complete initial treatment undergo follow-up imaging at routine intervals to assess for tumor recurrence. Imaging is a central part of brain tumor management, but MRI findings in patients with brain tumor can be challenging to interpret and are further confounded by interpretation variability. Disease-specific structured reporting attempts to reduce variability in imaging results by implementing well-defined imaging criteria and standardized language. The Brain Tumor Reporting and Data System (BT-RADS) is one such framework streamlined for clinical workflows and includes quantitative criteria for more objective evaluation of follow-up imaging. To facilitate accurate and objective monitoring of patients during the follow-up period, we developed a cloud platform, the Brain Imaging Collaborative Suite's Longitudinal Imaging Tracker (BrICS-LIT). BrICS-LIT uses semiautomated tumor segmentation algorithms of both T2-weighted FLAIR and contrast-enhanced T1-weighted MRI to assist clinicians in quantitative assessment of brain tumors. The LIT platform can ultimately guide clinical decision-making for patients with glioblastoma by providing quantitative metrics for BT-RADS scoring. Further, this platform has the potential to increase objectivity when measuring efficacy of novel therapies for patients with brain tumor during their follow-up. Therefore, LIT will be used to track patients in a dose-escalated clinical trial, where spectroscopic MRI has been used to guide radiation therapy (Clinicaltrials.gov NCT03137888), and compare patients to a control group that received standard of care.
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Affiliation(s)
- Karthik Ramesh
- Department of Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, GA.,Coulter Department of Biomedical Engineering, Georgia Institute of Technology & Emory University School of Medicine, Atlanta, GA
| | - Saumya S Gurbani
- Department of Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, GA.,Coulter Department of Biomedical Engineering, Georgia Institute of Technology & Emory University School of Medicine, Atlanta, GA
| | - Eric A Mellon
- Departments of Radiation Oncology, Sylvester Comprehensive Cancer Center; and
| | - Vicki Huang
- Department of Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, GA.,Coulter Department of Biomedical Engineering, Georgia Institute of Technology & Emory University School of Medicine, Atlanta, GA
| | | | | | | | - Hui-Kuo G Shu
- Department of Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, GA
| | - Hyunsuk Shim
- Department of Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, GA.,Coulter Department of Biomedical Engineering, Georgia Institute of Technology & Emory University School of Medicine, Atlanta, GA.,Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA
| | - Brent D Weinberg
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA
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18
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Neuroimaging in the Era of the Evolving WHO Classification of Brain Tumors, From the AJR Special Series on Cancer Staging. AJR Am J Roentgenol 2021; 217:3-15. [PMID: 33502214 DOI: 10.2214/ajr.20.25246] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The inclusion of molecular and genetic information with histopathologic information defines the framework for brain tumor classification and grading. This framework is reflected in the major restructuring of the WHO brain tumor classification system in 2016 and in numerous subsequent proposed updates reflecting ongoing developments in understanding the impact of tumor genotype on classification and grading. This incorporation of molecular and genetic features improves tumor diagnosis and prediction of tumor behavior and response to treatment. Neuroimaging is essential for the noninvasive assessment of pretreatment tumor grading and for identification and determination of therapeutic efficacy. Use of conventional neuroimaging and physiologic imaging techniques, such as diffusion- and perfusion-weighted MRI, can increase diagnostic confidence before and after treatment. Although the use of neuroimaging to consistently determine tumor genetics is not yet robust, promising developments are on the horizon. Given the complexity of the brain tumor microenvironment, the development and implementation of a standardized reporting system can aid in conveying to radiologists, referring providers, and patients important information about brain tumor response to treatment. The purpose of this article is to review the current state and role of neuroimaging in this continuously evolving field.
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Yang Y, Yang Y, Wu X, Pan Y, Zhou D, Zhang H, Chen Y, Zhao J, Mo Z, Huang B. Adding DSC PWI and DWI to BT-RADS can help identify postoperative recurrence in patients with high-grade gliomas. J Neurooncol 2020; 146:363-371. [PMID: 31902040 DOI: 10.1007/s11060-019-03387-6] [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: 11/12/2019] [Accepted: 12/27/2019] [Indexed: 12/22/2022]
Abstract
BACKGROUND The Brain Tumor Reporting and Data System (BT-RADS) category 3 is suitable for identifying cases with intermediate probability of tumor recurrence that do not meet the Response Assessment in Neuro-Oncology (RANO) criteria for progression. The aim of this study was to evaluate the added value of dynamic susceptibility contrast-enhanced perfusion-weighted imaging (DSC PWI) and diffusion-weighted imaging (DWI) to BT-RADS for differentiating tumor recurrence from non-recurrence in postoperative high-grade glioma (HGG) patients with category 3 lesions. METHODS Patients with BT-RADS category 3 lesions were included. The maximal relative cerebral blood volume (rCBVmax) and the mean apparent diffusion coefficient (ADCmean) values were measured. The added value of DSC PWI and DWI to BT-RADS was evaluated by receiver operating characteristic (ROC) curve analysis. RESULTS Fifty-one of 91 patients had tumor recurrence, and 40 patients did not. There were significant differences in rCBVmax and ADCmean between the tumor recurrence group and non-recurrence group. Compared to BT-RADS alone, the addition of DSC PWI to BT-RADS increased the area under curve (AUC) from 0.76 (95% confidence interval [CI] 0.66-0.84) to 0.90 (95% CI 0.81-0.95) for differentiating tumor recurrence from non-recurrence. The addition of DWI to BT-RADS increased the AUC from 0.76 (95% CI 0.66-0.84) to 0.88 (95% CI 0.80-0.94). The combination of BT-RADS, DSC PWI, and DWI exhibited the best diagnostic performance (AUC = 0.95; 95% CI 0.88-0.98) for differentiating tumor recurrence from non-recurrence. CONCLUSION Adding DSC PWI and DWI to BT-RADS can significantly improve the diagnostic performance for differentiating tumor recurrence from non-recurrence in BT-RADS category 3 lesions.
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Affiliation(s)
- Yuelong Yang
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan 2nd Road, Guangzhou, 510080, China
| | - Yunjun Yang
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan 2nd Road, Guangzhou, 510080, China
| | - Xiaoling Wu
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan 2nd Road, Guangzhou, 510080, China
| | - Yi Pan
- Department of Radiotherapy, Cancer Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Dong Zhou
- Department of Neurosurgery, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Hongdan Zhang
- Department of Radiotherapy, Cancer Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Yonglu Chen
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan 2nd Road, Guangzhou, 510080, China
| | - Jiayun Zhao
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan 2nd Road, Guangzhou, 510080, China
| | - Zihua Mo
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan 2nd Road, Guangzhou, 510080, China
| | - Biao Huang
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan 2nd Road, Guangzhou, 510080, China.
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