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Glioblastoma, the most common primary malignant brain tumor, is genetically diverse and classified into four transcriptomic subtypes, i.e., classical, mesenchymal, proneural, neural. We sought a noninvasive robust quantitative imaging phenomic (QIP) signature associated with this transcriptomic classification of glioblastoma patients, derived from clinically-acquired imaging protocols and without the need for advanced genetic testing. This QIP signature was discovered and evaluated in a retrospective cohort of 112 pathology-proven de novo glioblastoma patients for whom basic preoperative multi-parametric MRI (mpMRI) data (T1, T1-Gd, T2, T2-FLAIR) were available, and compared with the tumor subtype as obtained through an RNA isoform-based classifier. Comprehensive and diverse QIP features capturing intensity distributions, volume, morphology, statistics, tumors’ anatomical location, and texture for each tumor sub-region, were multivariately integrated via support vector machines to construct our QIP signature. The performance/generalizability of the model was evaluated using 5-fold cross-validation. The overall accuracy of the proposed method was estimated equal to 71% for identifying the transcriptomic tumor subtype; 82.14% [AUC:0.82], 75.89% [AUC:0.78], 75.89% [AUC:0.81], and 88.39% [AUC:0.84] for predicting proneural, neural, mesenchymal and classical subtypes, respectively. The obtained QIP signature revealed a macroscopic biological insight of the complex tumor subtypes, including more pronounced presence of tissue with higher water content in neural subtype, larger enhancement component of the tumor in mesenchymal subtype, and overall smaller tumors in classical subtype. Our results indicate that quantitative analysis of imaging features extracted from clinically-acquired mpMRI yields prompt non-invasive biomarkers of the molecular profile of glioblastoma patients, important in influencing surgical decision-making, treatment planning, and assessment of inoperable tumors.
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