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A Deep Learning Approach for Rapid Mutational Screening in Melanoma

By Randie H. Kim, Sofia Nomikou, Zarmeena Dawood, George Jour, Douglas Donnelly, Una Moran, Jeffrey S Weber, Narges Razavian, Matija Snuderl, Richard Shapiro, Russell S Berman, Nicolas Coudray, Iman Osman, Aristotelis Tsirigos

Posted 16 Apr 2019
bioRxiv DOI: 10.1101/610311

DNA-based molecular assays for determining mutational status in melanomas are time-consuming and costly. As an alternative, we applied a deep convolutional neural network (CNN) to histopathology images of tumors from 257 melanoma patients and developed a fully automated model that first selects for tumor-rich areas (Area under the curve AUC=0.98), and second, predicts for the presence of mutated BRAF or NRAS. Network performance was enhanced on BRAF-mutated melanomas <=1.0 mm (AUC=0.83) and on non-ulcerated NRAS-mutated melanomas (AUC=0.92). Applying our models to histological images of primary melanomas from The Cancer Genome Atlas database also demonstrated improved performances on thinner BRAF-mutated melanomas and non-ulcerated NRAS-mutated melanomas. We propose that deep learning-based analysis of histological images has the potential to become integrated into clinical decision making for the rapid detection of mutations of interest in melanoma.

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