COMP-07. MACHINE LEARNING PREDICTS PROGRESSION AND SURVIVAL IN GLIOMA USING RADIOHISTOGENOMIC FEATURES

Abstract

Abstract BACKGROUND Random forest classifiers can predict survival and progression status with radiomic features. Here, we compare the predictions of radiomic, histomic, and genomic features on progression-free survival (PFS) and overall survival (OS) in patients with low-grade glioma (LGG). METHODS We identified patients from our institution and The Cancer Genome Atlas (TCGA) with LGG and analyzed histopathology, mRNA expression, and MRI sequences. Differential gene expression analysis was performed to obtain top gene expression values. Discriminative feature-oriented dictionary learning (DFDL)1 was used to extract histomic features from histopathology slides. Forty-four computer-derived texture features were extracted from segmented tumors for each MRI sequence. A random forest classifier with cross-validation was then used to evaluate binary predictions of PFS and OS using radiomic, genomic, and histomic features from institutional and TCGA data. RESULTS The out-of-bag (OOB) error rates for PFS and OS predictions from combined institutional and TCGA radiomic features (n = 104) was 0.22 and 0.15, respectively. Similarly, OOB error rates of PFS and OS predictions for institutional histomic features (n = 66) was 0.24 and 0.29, respectively. OOB error rates for predictions of PFS and OS from combined institutional and TCGA genomic features (n = 283) were 0.14 and 0.24, respectively. A combined model incorporating radiogenomic features from institutional and TCGA data performed slightly worse than either modality alone (PFS OOB error = 0.28, OS OOB error = 0.21), possibly due to fewer cases (n = 67). CONCLUSION Our findings indicate combining radiomic and genomic features can predict PFS and OS in patients with LGG. The utility of this methodology may help to predict malignant transformation in LGG. REFERENCES: Vu TH, Mousavi HS, Monga V, Rao G, Rao UA. Histopathological image classification using discriminative feature-oriented dictionary learning. IEEE transactions on medical imaging. 2015;35(3):738–51.