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With growing emphasis on personalized cancer-therapies,radiogenomics hasshown promise in identifying target tumor mutational status on routine imaging(i.e. MRI) scans. These approaches fall into 2 categories: (1)deep-learning/radiomics (context-based), using image features from the entiretumor to identify the gene mutation status, or (2) atlas (spatial)-based toobtain likelihood of gene mutation status based on population statistics. Whilemany genes (i.e. EGFR, MGMT) are spatially variant, a significant challenge inreliable assessment of gene mutation status on imaging has been the lack ofavailable co-localized ground truth for training the models. We presentSpatial-And-Context aware (SpACe) "virtual biopsy" maps that incorporatecontext-features from co-localized biopsy site along with spatial-priors frompopulation atlases, within a Least Absolute Shrinkage and Selection Operator(LASSO) regression model, to obtain a per-voxel probability of the presence ofa mutation status (M+ vs M-). We then use probabilistic pair-wise Markov modelto improve the voxel-wise prediction probability. We evaluate the efficacy ofSpACe maps on MRI scans with co-localized ground truth obtained fromcorresponding biopsy, to predict the mutation status of 2 driver genes inGlioblastoma: (1) EGFR (n=91), and (2) MGMT (n=81). When compared againstdeep-learning (DL) and radiomic models, SpACe maps obtained training andtesting accuracies of 90% (n=71) and 90.48% (n=21) in identifying EGFRamplification status,compared to 80% and 71.4% via radiomics, and 74.28% and65.5% via DL. For MGMT status, training and testing accuracies using SpACe were88.3% (n=61) and 71.5% (n=20), compared to 52.4% and 66.7% using radiomics,and79.3% and 68.4% using DL. Following validation,SpACe maps could providesurgical navigation to improve localization of sampling sites for targeting ofspecific driver genes in cancer.

Spatial-And-Context aware (SpACe) "virtual biopsy" radiogenomic maps to target tumor mutational status on structural MRI