基于深度学习的结直肠癌全视野数字病理切片分子分型识别研究

廖俊; 冯小兵; 王玉红; 郭凌川

doi:10.12182/20210760501

基于深度学习的结直肠癌全视野数字病理切片分子分型识别研究

Identifying Molecular Subtypes of Whole-Slide Image in Colorectal Cancer via Deep Learning

摘要

摘要:
目的建立人工智能辅助结直肠癌病理切片分子分型诊断系统。
方法在癌症基因组图谱（the cancer genome Atlas, TCGA）数据库中筛选出422例结直肠癌患者的812张病理切片，分为训练集（75%）和测试集（25%）；存入www.paiwsit.com数据库中，根据资深的病理医生标注的数据进行处理及分割，得到超过400万张带有标签的训练集，最后利用深度学习模型进行训练。
结果在经过多种卷积神经网络模型训练后，在110例203张切片的测试集上测试，子图级别达到53.04%的准确率，切片级别准确率达到51.72%，其中结直肠癌共识分子亚型之一的经典型（CMS2）切片级准确率达到75.00%。
结论本研究对促进结直肠癌筛查和精准治疗具有重要意义。

Abstract:
Objective To establish an artificial intelligence-assisted diagnosis system for molecular subtyping of colorectal cancer (CRC).
Methods 812 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.
Results After 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%.
Conclusion This study is of great significance to the promotion of colorectal cancer screening and precision treatment.

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