Deep Learning and Pathomics Analyses Reveal Cell Nuclei as Important Features for Mutation Prediction of BRAF-Mutated Melanomas.

Image-based analysis as a method for mutation detection can be advantageous in settings when tumor tissue is limited or unavailable for direct testing. In this study, we utilize two distinct and complementary machine-learning methods of analyzing whole-slide images for predicting mutated BRAF. In the first method, whole-slide images of melanomas from 256 patients were used to train a deep convolutional neural network to develop a fully automated model that first selects for tumor-rich areas (area under the curve = 0.96) and then predicts for mutated BRAF (area under the curve = 0.71). Saliency mapping was performed and revealed that pixels corresponding to nuclei were the most relevant to network learning. In the second method, whole-slide images were analyzed using a pathomics pipeline that first annotates nuclei and then quantifies nuclear features, showing that mutated BRAF nuclei were significantly larger and rounder than BRAF‒wild-type nuclei. Finally, we developed a model that combines clinical information, deep learning, and pathomics that improves the predictive performance for mutated BRAF to an area under the curve of 0.89. Not only does this provide additional insights on how BRAF mutations affect tumor structural characteristics, but machine learning‒based analysis of whole-slide images also has the potential to be integrated into higher-order models for understanding tumor biology.

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