Multimodal MRI features predict isocitrate dehydrogenase genotype in high-grade gliomas

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.

Epidermal Growth Factor Receptor Extracellular Domain Mutations in Glioblastoma Present Opportunities for Clinical Imaging and Therapeutic Development

“…There is increasing evidence demonstrating the validity of advanced computational analysis of QIP features that has shown promise in predicting clinical outcome and molecular characteristics (Aerts, 2016; Bakas et al, 2017a, 2017c; Ellingson et al, 2017; Gevaert et al, 2017; Gill et al, 2014; Gutman et al, 2013; Itakura et al, 2015; Macyszyn et al, 2016; Zhang et al, 2017). In this study, examination of QIP features associated with the EGFR missense mutations helped to generate hypotheses for the negative survival impact of EGFR A289D/T/V .…”

Radiomics Strategy for Molecular Subtype Stratification of Lower‐Grade Glioma: Detecting IDH and TP53 Mutations Based on Multimodal MRI

“…As listed in online Table E1, a total of 65 texture and histogram features were then extracted from the VOI of each sequence: 12 histogram‐based textures, 9 Gray‐Level Co‐occurrence Matrix (GLCM) features, 13 Gray‐Level Gradient Co‐occurrence Matrix (GLGCM) features, 13 Gray‐Level Run‐Length Matrix (GLRLM) features, 13 Gray‐Level Size Zone Matrix (GLSZM) features, and five Neighborhood Gray‐Tone Difference Matrix (NGTDM) features. Details of the definitions and calculations of these features have previously been given …”

Textural features of dynamic contrast‐enhanced MRI derived model‐free and model‐based parameter maps in glioma grading

“…Among these radiomic features, textural features are one of the most commonly used, which could quantify spatial variation of gray‐level intensity and characterize the underlying heterogeneity of the image under evaluation . Until recently, textural features, extracted from routine MR images, have successfully been associated with glioma grade, overall survival, and isocitrate dehydrogenase (IDH) genotype . However, dynamic contrast‐enhanced MRI (DCE‐MRI), which makes the heterogeneity of vascular physiology and structure quantifiable data and reveals dramatic imaging heterogeneity, was seldom analyzed by texture analysis.…”