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Multimodal MRI features predict isocitrate dehydrogenase genotype in high-grade gliomas
Biqi Zhang1, Ken Chang1, Shakti Ramkissoon1
1Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts (B.Z., K.C., R.Y.H.); Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts (S.R., K.L.L.); Department of Pathology, Harvard Medical School, Boston, Massachusetts (S.R., K.L.L.); Department of Pathology, Boston Children's Hospital, Boston, Massachusetts (S.R., K.L.L.); Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts(S.R., D.A.R., K.L.L., P.Y.W.); Harvard Radiation Oncology Program, Boston, Massachusetts (S.T.); Department of Neurosurgery, Brigham and Women's Hospital, Boston, Massachusetts (W.L.B.); Center of Neuro-Oncology, Dana-Farber/Brigham and Women's Cancer Center, Boston, Massachusetts (D.A.R., P.Y.W.); Department of Radiation Oncology, Dana-Farber/Brigham and Women's Cancer Center, Boston, Massachusetts (B.M.A.).
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.
