NIMG-40. NON-INVASIVE IN VIVO SIGNATURE OF IDH1 MUTATIONAL STATUS IN HIGH GRADE GLIOMA FROM CLINICALLY-ACQUIRED MULTI-PARAMETRIC MAGNETIC RESONANCE IMAGING USING MULTIVARIATE MACHINE LEARNING

摘要

PURPOSE: Mutational status of isocitrate dehydrogenase (IDH1) is a defining feature of the World Health Organization classification scheme for high grade gliomas (HGGs). IDH-mutant HGGs confer significantly improved prognoses when compared with IDH-wildtype, which typically describe the most common malignant primary HGGs in adults, namely glioblastoma. HGGs are densely cellular, pleomorphic tumors with high mitotic activity, with glioblastoma having either microvascular proliferation, or necrosis, or both. We hypothesize that integrative analysis of multi-parametric magnetic resonance imaging (mpMRI) via multivariate machine learning (ML), will enhance subtle yet important radiographic HGG characteristics, and reveal imaging signatures determinant of IDH1 mutational status.METHODS86 HGG patients were retrospectively identified with available pre-operative clinically-acquired mpMRI data (T1, T1-Gd, T2, T2-FLAIR, DTI, DSC-MRI). Each HGG was delineated into sub-regions of enhancement, non-enhancement, and peritumoral edema/invasion. 342 quantitative imaging phenomic (QIP) features extracted across sub-regions from all mpMRI, comprising descriptors of size, morphology, texture, intensity, and biophysical growth modeling. Cross-validated sequential feature selection determined the most discriminative QIP features for our integrative ML predictor of IDH1 status. The predicted classifications, following a 10-fold cross-validation, were compared with the IDH1 status obtained by next generation sequencing, or immunohistochemistry.