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Halasz LM, Attia A, Bradfield L, Brat DJ, Kirkpatrick JP, Laack NN, Lalani N, Lebow ES, Liu AK, Niemeier HM, Palmer JD, Peters KB, Sheehan J, Thomas RP, Vora SA, Wahl DR, Weiss SE, Yeboa DN, Zhong J, Shih HA. Radiation Therapy for IDH-Mutant Grade 2 and Grade 3 Diffuse Glioma: An ASTRO Clinical Practice Guideline. Pract Radiat Oncol 2022; 12:370-386. [PMID: 35902341 DOI: 10.1016/j.prro.2022.05.004] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 05/04/2022] [Accepted: 05/04/2022] [Indexed: 11/22/2022]
Abstract
PURPOSE This guideline provides evidence-based recommendations for adults with isocitrate dehydrogenase (IDH)-mutant grade 2 and grade 3 diffuse glioma, as classified in the 2021 World Health Organization (WHO) Classification of Tumours. It includes indications for radiation therapy (RT), advanced RT techniques, and clinical management of adverse effects. METHODS The American Society for Radiation Oncology convened a multidisciplinary task force to address 4 key questions focused on the RT management of patients with IDH-mutant grade 2 and grade 3 diffuse glioma. Recommendations were based on a systematic literature review and created using a predefined consensus-building methodology and system for grading evidence quality and recommendation strength. RESULTS A strong recommendation for close surveillance alone was made for patients with oligodendroglioma, IDH-mutant, 1p/19q codeleted, WHO grade 2 after gross total resection without high-risk features. For oligodendroglioma, WHO grade 2 with any high-risk features, adjuvant RT was conditionally recommended. However, adjuvant RT was strongly recommended for oligodendroglioma, WHO grade 3. A conditional recommendation for close surveillance alone was made for astrocytoma, IDH-mutant, WHO grade 2 after gross total resection without high-risk features. Adjuvant RT was conditionally recommended for astrocytoma, WHO grade 2, with any high-risk features and strongly recommended for astrocytoma, WHO grade 3. Dose recommendations varied based on histology and grade. Given known adverse long-term effects of RT, consideration for advanced techniques such as intensity modulated radiation therapy/volumetric modulated arc therapy or proton therapy were given as strong and conditional recommendations, respectively. Finally, based on expert opinion, the guideline recommends assessment, surveillance, and management for toxicity management. CONCLUSIONS Based on published data, the American Society for Radiation Oncology task force has proposed recommendations to inform the management of adults with IDH-mutant grade 2 and grade 3 diffuse glioma as defined by WHO 2021 classification, based on the highest quality published data, and best translated by our task force of subject matter experts.
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Affiliation(s)
- Lia M Halasz
- Department of Radiation Oncology, University of Washington, Seattle, Washington.
| | - Albert Attia
- Department of Radiation Oncology, Bon Secours Mercy Health, Greenville, South Carolina
| | - Lisa Bradfield
- American Society for Radiation Oncology, Arlington, Virginia
| | - Daniel J Brat
- Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - John P Kirkpatrick
- Department of Radiation Oncology and Neurosurgery, Duke University, Durham, North Carolina
| | - Nadia N Laack
- Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota
| | - Nafisha Lalani
- Department of Radiation Oncology, The University of Ottawa, Ottawa, Ontario
| | - Emily S Lebow
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Arthur K Liu
- Department of Radiation Oncology, UC Health, Fort Collins, Colorado
| | | | - Joshua D Palmer
- Department of Radiation Oncology, Arthur G. James Cancer Hospital and Richard J. Solove Research Institute, Ohio State University Comprehensive Cancer Center, Columbus, Ohio
| | - Katherine B Peters
- Departments of Neurology and Neurosurgery, Duke University, Durham, North Carolina
| | - Jason Sheehan
- Department of Neurosurgery, University of Virginia, Charlottesville, Virginia
| | - Reena P Thomas
- Department of Neurology, Stanford University, Palo Alto, California
| | - Sujay A Vora
- Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona
| | - Daniel R Wahl
- Department of Radiation Oncology, University of Michigan Medical Center, Ann Arbor, Michigan
| | - Stephanie E Weiss
- Department of Radiation Oncology, Fox Chase Cancer Center, Philadelphia, Pennsylvania
| | - D Nana Yeboa
- Department of Radiation Oncology, MD-Anderson Cancer Center, Houston, Texas
| | - Jim Zhong
- Department of Radiation Oncology, Emory University, Atlanta, Georgia
| | - Helen A Shih
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, Massachusetts
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Soltaninejad M, Yang G, Lambrou T, Allinson N, Jones TL, Barrick TR, Howe FA, Ye X. Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI. Int J Comput Assist Radiol Surg 2016; 12:183-203. [PMID: 27651330 PMCID: PMC5263212 DOI: 10.1007/s11548-016-1483-3] [Citation(s) in RCA: 171] [Impact Index Per Article: 21.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2016] [Accepted: 08/31/2016] [Indexed: 12/03/2022]
Abstract
Purpose We propose a fully automated method for detection and segmentation of the abnormal tissue associated with brain tumour (tumour core and oedema) from Fluid- Attenuated Inversion Recovery (FLAIR) Magnetic Resonance Imaging (MRI). Methods The method is based on superpixel technique and classification of each superpixel. A number of novel image features including intensity-based, Gabor textons, fractal analysis and curvatures are calculated from each superpixel within the entire brain area in FLAIR MRI to ensure a robust classification. Extremely randomized trees (ERT) classifier is compared with support vector machine (SVM) to classify each superpixel into tumour and non-tumour. Results The proposed method is evaluated on two datasets: (1) Our own clinical dataset: 19 MRI FLAIR images of patients with gliomas of grade II to IV, and (2) BRATS 2012 dataset: 30 FLAIR images with 10 low-grade and 20 high-grade gliomas. The experimental results demonstrate the high detection and segmentation performance of the proposed method using ERT classifier. For our own cohort, the average detection sensitivity, balanced error rate and the Dice overlap measure for the segmented tumour against the ground truth are 89.48 %, 6 % and 0.91, respectively, while, for the BRATS dataset, the corresponding evaluation results are 88.09 %, 6 % and 0.88, respectively. Conclusions This provides a close match to expert delineation across all grades of glioma, leading to a faster and more reproducible method of brain tumour detection and delineation to aid patient management.
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Affiliation(s)
- Mohammadreza Soltaninejad
- Laboratory of Vision Engineering, School of Computer Science, University of Lincoln, Lincoln, LN6 7TS, UK.
| | - Guang Yang
- Neurosciences Research Centre, Molecular and Clinical Sciences Institute, St. George's, University of London, London, SW17 0RE, UK.,National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
| | - Tryphon Lambrou
- Laboratory of Vision Engineering, School of Computer Science, University of Lincoln, Lincoln, LN6 7TS, UK
| | - Nigel Allinson
- Laboratory of Vision Engineering, School of Computer Science, University of Lincoln, Lincoln, LN6 7TS, UK
| | - Timothy L Jones
- Atkinson Morley Department of Neurosurgery, St George's Hospital London, London, SW17 0RE, UK
| | - Thomas R Barrick
- Neurosciences Research Centre, Molecular and Clinical Sciences Institute, St. George's, University of London, London, SW17 0RE, UK
| | - Franklyn A Howe
- Neurosciences Research Centre, Molecular and Clinical Sciences Institute, St. George's, University of London, London, SW17 0RE, UK
| | - Xujiong Ye
- Laboratory of Vision Engineering, School of Computer Science, University of Lincoln, Lincoln, LN6 7TS, UK
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