Spatial-And-Context Aware (SpACe) “Virtual Biopsy” Radiogenomic Maps to Target Tumor Mutational Status on Structural MRI

With growing emphasis on personalized cancer-therapies, radiogenomics has shown promise in identifying target tumor mutational status on routine imaging (i.e. MRI) scans. These approaches largely fall into two categories: (1) deep-learning/radiomics (context-based) that employ image features from the entire tumor to identify the gene mutation status, or (2) atlas (spatial)-based to obtain likelihood of gene mutation status based on population statistics. While many genes (i.e. EGFR, MGMT) are spatially variant, a significant challenge in reliable assessment of gene mutation status on imaging is the lack of available co-localized ground truth for training the models. We present Spatial-And-Context aware (SpACe) “virtual biopsy” maps that incorporate context-features from co-localized biopsy sites along with spatial-priors from population atlases, within a Least Absolute Shrinkage and Selection Operator (LASSO) regression model, to obtain a per-voxel likelihood of the presence of a mutation status. We then use probabilistic pair-wise Markov model to improve the voxel-wise likelihood. We evaluate the efficacy of SpACe maps on MRI scans with co-localized ground truth obtained from biopsy, to predict the mutation status of 2 driver genes in Glioblastoma (GBM): (1) versus, (n = 91), and (2) versus, (n = 81). When compared against state-of-the-art deep-learning (DL) and radiomic models, SpACe maps obtained training and testing accuracies of 90% (n = 70) and 90.48% (n = 21) in identifying EGFR amplification status, compared to 80% and 71.4% via radiomics, and 74.28% and 65.5% via DL. For MGMT methylation status, training and testing accuracies using SpACe were 88.3% (n = 60) and 71.5% (n = 21), compared to 52.4% and 66.7% using radiomics, and 79.3% and 68.4% using DL. Following validation, SpACe maps could provide surgical navigation to improve localization of sampling sites for targeting of specific driver genes in cancer.