| 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 |
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| 引用本文： | 廖俊,冯小兵,王玉红,郭凌川. 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[J]. 四川大学学报(医学版), 2021, 52(4): 686-692. DOI: 10.12182/20210760501 |
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| 作者姓名： | 廖俊 冯小兵 王玉红 郭凌川 |
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| 作者单位： | 中国药科大学理学院 南京 211198;中国药科大学基础医学与临床药学学院 南京 211198;苏州大学附属第一医院 病理科 苏州 215000 |
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| 基金项目： | 国家自然科学基金（No. 81902969、No. 81874331）和双一流创新团队（No. CPU2018GY19）资助 |
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| 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,...
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| 关 键 词： | 结直肠癌 分子亚型 深度学习 全视野数字病理切片 辅助诊断 |
| 收稿时间： | 2020-11-26 |
Identifying Molecular Subtypes of Whole-Slide Image in Colorectal Cancer via Deep Learning |
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| LIAO Jun,FENG Xiao-bing,WANG Yu-hong,GUO Ling-chuan. Identifying Molecular Subtypes of Whole-Slide Image in Colorectal Cancer via Deep Learning[J]. Journal of Sichuan University. Medical science edition, 2021, 52(4): 686-692. DOI: 10.12182/20210760501 |
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| Authors: | LIAO Jun FENG Xiao-bing WANG Yu-hong GUO Ling-chuan |
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| Affiliation: | 1.School of Science, China Pharmaceutical University, Nanjing 211198, China |
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| 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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