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BACKGROUND Radiomics and connectome analysis are distinct and non-invasive methods of deriving biologic information from MRI. Radiomics analyzes features intrinsic to the tumor, and connectomics incorporates data regarding the tumor and surrounding neural circuitry. In this study we used both techniques to predict glioma survival. METHODS We retrospectively identified 305 adult patients with histopathologically confirmed WHO grade II–IV gliomas who had presurgical, 3D, T1-weighted brain MRI. Available clinical variables included tumor lobe, hemisphere, multifocal nature grade, histology extent of surgical resection, patient age gender. For connectomics, we calculated nodal efficiencies, network size and degree for all pairs of 33 voxel cubes spanning the entire gray matter volume using similarity-based extraction and graph theory. Radiomic features were extracted using Pyradiomics and subjected to patient-level and population-level clustering (N=172). These clusters were then used to construct a multi-regional spatial interaction matrix for model building. Cox proportional hazards models were fit for clinical variables alone, connectomics alone, radiomics alone, connectomics+clinical and radiomics+clinical. We implemented 10-folds cross-validation and examined the mean area under the curve (AUC) across validation loops. RESULTS Median survival time was 134.2 months. The mean AUC for the clinical model was 0.79 +/- 0.01, the connectome model was 0.88 +/- 0.01, the combined connectome + clinical model was 0.93 +/- 0.01, the radiomic model was 0.64 +/- 0.05 and the radiomics+clinical model was 0.89+/-0.03. Radiomic analysis of the entire dataset as well as comparisons of radiomic+connectomics +/- clinical models are pending. CONCLUSIONS The combination of clinical variables and connectome analysis provided a more robust predictive model than other models. This suggests that connectome analysis incorporates valuable clinically-predictive information which can augment our capacity for prognostication of patients with diffuse glioma. These methods warrant further evaluation in larger prospective study of patients with diffuse glioma.

neuro-oncology

NIMG-32. THE PREDICTIVE CAPACITY OF PRE-OPERATIVE IMAGING ANALYSIS IN DIFFUSE GLIOMA: A COMPARISON OF CONNECTOMICS, RADIOMICS, AND CLINICAL PREDICTIVE MODELS