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• W2887086576 abstract "Abstract To characterize a tumor and suggest appropriate treatments, pathologists rely on the visual assessment of histopathology slides as well as molecular profiling of the tumors. This is especially true in lung adenocarcinoma cases where targeted therapies exist for tumors with EGFR mutations. In this work, we studied the use of deep learning approaches to classify lung cancer histopathology images and predict the mutational status of frequently mutated genes. To achieve these tasks, more than 1600 scanned histopathology slides, obtained from TCGA, were separated into independent training, validation and test sets. They were then tiled into 512x512 pixel windows, and the training set was used to feed the inception v3 convolutional neural network developed by Google. The resulting model was evaluated on the test set, and the generated per-tile probabilities were aggregated to generate a per-slide classification score. We based our study on whole-slide images of adenocarcinoma, squamous cells carcinoma and normal lung tissues and the neural network was trained on several tasks: first, the goal was to identify normal from tumor regions, and then, once the tumor regions were identified, it was trained to identify adenocarcinoma versus squamous cell carcinoma. Finally, the neural network was trained predict the mutational status of the most frequently mutated genes in lung adenocarcinoma. After verifying that the trained network properly identifies tumor slides compared to normal tissue (AUC=0.99) and distinguishes adenocarcinomas from squamous cell carcinomas (AUC=0.95), the network was trained to simultaneously identify different mutations. It achieved AUC scores of 0.82 and 0.86 for STK11 and EGFR respectively, showing that mutations in these two genes may confer specific macroscopic features, visible in histopathology images, and that a deep convolutional neural network can be trained to recognize those features. Our results demonstrate that, given the availability of sufficient data, convolutional neural networks learn to perform complex diagnoses and thus assist pathologists in their work. Citation Format: Nicolas Coudray, Andre L. Moreira, Theodore Sakellaropoulos, David Fenyö, Narges Razavian, Aristotelis Tsirigos. Determining EGFR and STK11 mutational status in lung adenocarcinoma histopathology images using deep learning [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5309." @default.
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• W2887086576 date "2018-07-01" @default.
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• W2887086576 title "Abstract 5309: Determining EGFR and STK11 mutational status in lung adenocarcinoma histopathology images using deep learning" @default.
• W2887086576 doi "https://doi.org/10.1158/1538-7445.am2018-5309" @default.
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