Radiomic Texture Analysis Mapping Predicts Areas of True Functional MRI Activity.

Hassan, Islam; Kotrotsou, Aikaterini; Bakhtiari, Ali Shojaee; Thomas, Ginu A; Weinberg, Jeffrey S; Kumar, Ashok J; Sawaya, Raymond; Luedi, Markus M; Zinn, Pascal O; Colen, Rivka R (2016). Radiomic Texture Analysis Mapping Predicts Areas of True Functional MRI Activity. Scientific Reports, 6(25295), p. 25295. Nature Publishing Group 10.1038/srep25295

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Individual analysis of functional Magnetic Resonance Imaging (fMRI) scans requires user-adjustment of the statistical threshold in order to maximize true functional activity and eliminate false positives. In this study, we propose a novel technique that uses radiomic texture analysis (TA) features associated with heterogeneity to predict areas of true functional activity. Scans of 15 right-handed healthy volunteers were analyzed using SPM8. The resulting functional maps were thresholded to optimize visualization of language areas, resulting in 116 regions of interests (ROIs). A board-certified neuroradiologist classified different ROIs into Expected (E) and Non-Expected (NE) based on their anatomical locations. TA was performed using the mean Echo-Planner Imaging (EPI) volume, and 20 rotation-invariant texture features were obtained for each ROI. Using forward stepwise logistic regression, we built a predictive model that discriminated between E and NE areas of functional activity, with a cross-validation AUC and success rate of 79.84% and 80.19% respectively (specificity/sensitivity of 78.34%/82.61%). This study found that radiomic TA of fMRI scans may allow for determination of areas of true functional activity, and thus eliminate clinician bias.

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