Accurate diagnosis of colorectal cancer based on histopathology images using artificial intelligence

作者：Kai Wang, Gang Yu, C. Xu, Xiang‐He Meng, Jing Zhou, Cheng Zheng, Zihao Deng, Li Shang, Rongren Liu, Shilin Su, Xiao Zhou, Qingsong Li, Jieyi Li, J. Wang, Kebo Ma, Ji Qi, Zhe-Yu Hu, Peng Tang, Jun Deng, Xiaohui Qiu, Bo Li, Wen‐Di Shen, Ruping Quan, Jun Yang, Lifan Huang, Yin Xiao, Zhen Yang, Zhengze Li, Ssu‐Yuan Wang, Haoran Ren, Chia-Wei Liang, Wei Guo, Y. Li, Haifan Xiao, H. Xiao, Y. Gu, J. P. Yun, Dan Huang, Zhigang Song, Xiangshan Fan, L. Chen, Xijing Yan, Z. Li, J. Huang, J. Huang, Joy Luttrell, Chunyu Zhang, Wu Zhou, K. Zhang, Chaozhong Wu, C. Wu, Hanshu Shen, Yipeng Wang, H. M. Xiao, Huangqing Xiao, Hong‐Wen Deng · 发表于：BMC Medicine · 年份：2021 · DOI：10.1186/s12916-021-01942-5 · 被引用次数：179 · 研究领域：AI in cancer detection、Digital Imaging for Blood Diseases、Colorectal Cancer Screening and Detection

BACKGROUND: Accurate and robust pathological image analysis for colorectal cancer (CRC) diagnosis is time-consuming and knowledge-intensive, but is essential for CRC patients' treatment. The current heavy workload of pathologists in clinics/hospitals may easily lead to unconscious misdiagnosis of CRC based on daily image analyses. METHODS: Based on a state-of-the-art transfer-learned deep convolutional neural network in artificial intelligence (AI), we proposed a novel patch aggregation strategy for clinic CRC diagnosis using weakly labeled pathological whole-slide image (WSI) patches. This approach was trained and validated using an unprecedented and enormously large number of 170,099 patches, > 14,680 WSIs, from > 9631 subjects that covered diverse and representative clinical cases from multi-independent-sources across China, the USA, and Germany. RESULTS: Our innovative AI tool consistently and nearly perfectly agreed with (average Kappa statistic 0.896) and even often better than most of the experienced expert pathologists when tested in diagnosing CRC WSIs from multicenters. The average area under the receiver operating characteristics curve (AUC) of AI was greater than that of the pathologists (0.988 vs 0.970) and achieved the best performance among the application of other AI methods to CRC diagnosis. Our AI-generated heatmap highlights the image regions of cancer tissue/cells. CONCLUSIONS: This first-ever generalizable AI system can handle large amounts of WSIs consis...