A Deep Learning Approach for Rapid Mutational Screening in Melanoma
Kim, R.; Nomikou, S.; Coudray, N.; Jour, G.; Dawood, Z.; Hong, R.; Esteva, E.; Sakellaropoulos, T.; Donnelly, D.; Moran, U.; Hatzimemos, A.; Weber, J. S.; Razavian, N.; Aifantis, I.; Fenyo, D.; Snuderl, M.; Shapiro, R.; Berman, R. S.; Osman, I.; Tsirigos, A.
Show abstract
Image-based analysis as a rapid method for mutation detection can be advantageous in research or clinical settings when tumor tissue is limited or unavailable for direct testing. Here, we applied a deep convolutional neural network (CNN) to whole slide images of melanomas from 256 patients and developed a fully automated model that first selects for tumor-rich areas (Area Under the Curve AUC=0.96) then predicts for the presence of mutated BRAF in our test set (AUC=0.72) Model performance was cross-validated on melanoma images from The Cancer Genome Atlas (AUC=0.75). We confirm that the mutated BRAF genotype is linked to phenotypic alterations at the level of the nucleus through saliency mapping and pathomics analysis, which reveal that cells with mutated BRAF exhibit larger and rounder nuclei. Not only do these findings provide additional insights on how BRAF mutations affects tumor structural characteristics, deep learning-based analysis of histopathology images have the potential to be integrated into higher order models for understanding tumor biology, developing biomarkers, and predicting clinical outcomes.
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