• Jun Liao, Xiao-Bing Feng, Yu-Hong Wang, and Ling-Chuan Guo.
• School of Science, China Pharmaceutical University, Nanjing 211198, China.
• Sichuan Da Xue Xue Bao Yi Xue Ban. 2021 Jul 1; 52 (4): 686-692.

ObjectiveTo establish an artificial intelligence-assisted diagnosis system for molecular subtyping of colorectal cancer (CRC).Methods812 whole-slide images (WSIs) of 422 patients were selected from the database of The Cancer Genome Atlas (TCGA) and were put into the training set (75%) and the test set (25%). The slides were stored in the www.paiwsit.com database. We preprocessed and segmented the slides based on the labelling results of experienced pathologists to generate a training set of more than 4 million labeled samples. Finally, deep learning models were adopted for training.ResultsAfter training with several convolutional neural network models, we tested the performance of the trained deep learning model on the test set of 203 WSIs from 110 patients, and our model achieved an accuracy of 53.04% at patch-level and 51.72% at slide-level, while the accuracy of CMS2 (one of a consensus of four subtypes for CRC) at slide-level was as high as 75.00%.ConclusionThis study is of great significance to the promotion of colorectal cancer screening and precision treatment.Copyright© by Editorial Board of Journal of Sichuan University (Medical Sciences).

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