  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 fall into 2 categories: (1)
deep-learning/radiomics (context-based), using 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 has been 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 site along with spatial-priors from
population atlases, within a Least Absolute Shrinkage and Selection Operator
(LASSO) regression model, to obtain a per-voxel probability of the presence of
a mutation status (M+ vs M-). We then use probabilistic pair-wise Markov model
to improve the voxel-wise prediction probability. We evaluate the efficacy of
SpACe maps on MRI scans with co-localized ground truth obtained from
corresponding biopsy, to predict the mutation status of 2 driver genes in
Glioblastoma: (1) EGFR (n=91), and (2) MGMT (n=81). When compared against
deep-learning (DL) and radiomic models, SpACe maps obtained training and
testing accuracies of 90% (n=71) 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 status, training and testing accuracies using SpACe were
88.3% (n=61) and 71.5% (n=20), 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.
