Multimodal MRI features predict isocitrate dehydrogenase genotype in high-grade gliomas. Issue 1 (26th June 2016)

• Record Type: Journal Article
• Authors: Zhang, Biqi; Chang, Ken; Ramkissoon, Shakti; Tanguturi, Shyam; Bi, Wenya Linda; Reardon, David A.; Ligon, Keith L.; Alexander, Brian M.; Wen, Patrick Y.; Huang, Raymond Y.
• Is Part Of: Neuro-oncology. Volume 19:Issue 1(2017)
• Page Start: 109
• Page End: 117
• Publication Date: 2016-06-26
• Subjects: high-grade glioma -- isocitrate dehydrogenase -- machine learning -- MRI -- prediction; Brain Neoplasms -- Periodicals; Brain -- Tumors -- Periodicals; Brain -- Cancer -- Periodicals; Nervous system -- Cancer -- Periodicals; 616.99481
• DOI: 10.1093/neuonc/now121
• Languages: English
• ISSNs: 1522-8517

Abstract

Abstract: Background: High-grade gliomas with mutations in the isocitrate dehydrogenase ( IDH ) gene family confer longer overall survival relative to their IDH -wild-type counterparts. Accurate determination of the IDH genotype preoperatively may have both prognostic and diagnostic value. The current study used a machine-learning algorithm to generate a model predictive of IDH genotype in high-grade gliomas based on clinical variables and multimodal features extracted from conventional MRI. Methods: Preoperative MRIs were obtained for 120 patients with primary grades III ( n = 35) and IV ( n = 85) glioma in this retrospective study. IDH genotype was confirmed for grade III (32/35, 91%) and IV (22/85, 26%) tumors by immunohistochemistry, spectrometry-based mutation genotyping (OncoMap), or multiplex exome sequencing (OncoPanel). IDH1 and IDH2 mutations were mutually exclusive, and all mutated tumors were collapsed into one IDH -mutated cohort. Cases were randomly assigned to either the training ( n = 90) or validation cohort ( n = 30). A total of 2970 imaging features were extracted from pre- and postcontrast T1-weighted, T2-weighted, and apparent diffusion coefficient map. Using a random forest algorithm, nonredundant features were integrated with clinical data to generate a model predictive of IDH genotype. Results: Our model achieved accuracies of 86% (area under the curve [AUC] = 0.8830) in the training cohort and 89% (AUC = 0.9231) in the validation cohort. Features with the highest predictive value included patient age as well as parametric intensity, texture, and shape features. Conclusion: Using a machine-learning algorithm, we achieved accurate prediction of IDH genotype in high-grade gliomas with preoperative clinical and MRI features.