Accurate diagnosis of colorectal cancer based on histopathology images using artificial intelligence.
Accurate diagnosis of colorectal cancer based on histopathology images using artificial intelligence.
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DOI：
10.1186/s12916-021-01942-5
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发表时间：
2021-03-23
期刊：
影响因子：
9.3
通讯作者：
Deng HW
中科院分区：
文献类型：
--
作者：
Wang KS;Yu G;Xu C;Meng XH;Zhou J;Zheng C;Deng Z;Shang L;Liu R;Su S;Zhou X;Li Q;Li J;Wang J;Ma K;Qi J;Hu Z;Tang P;Deng J;Qiu X;Li BY;Shen WD;Quan RP;Yang JT;Huang LY;Xiao Y;Yang ZC;Li Z;Wang SC;Ren H;Liang C;Guo W;Li Y;Xiao H;Gu Y;Yun JP;Huang D;Song Z;Fan X;Chen L;Yan X;Li Z;Huang ZC;Huang J;Luttrell J;Zhang CY;Zhou W;Zhang K;Yi C;Wu C;Shen H;Wang YP;Xiao HM;Deng HW
用于结直肠癌 (CRC) 诊断的准确、稳健的病理图像分析既耗时又需要大量知识，但对于 CRC 患者的治疗至关重要。当前诊所/医院病理学家的繁重工作量很容易导致基于日常图像分析的无意识误诊结直肠癌。基于人工智能（AI）中最先进的迁移学习深度卷积神经网络，我们提出了一种使用弱标记病理全切片图像（WSI）补丁进行临床CRC诊断的新型补丁聚合策略。该方法使用前所未有的大量 170,099 个补丁、>14,680 个 WSI 进行训练和验证，这些补丁来自超过 9631 名受试者，涵盖来自中国、美国和德国多个独立来源的不同且具有代表性的临床病例。在多中心诊断 CRC WSI 的测试中，我们的创新 AI 工具始终几乎完全符合（平均 Kappa 统计数据 0.896），甚至比大多数经验丰富的病理专家还要好。 AI的接受者操作特征曲线下面积（AUC）平均大于病理学家（0.988 vs 0.970），在其他AI方法应用于CRC诊断中取得了最好的性能。我们的人工智能生成的热图突出显示了癌症组织/细胞的图像区域。这个有史以来第一个通用的人工智能系统可以一致、稳健地处理大量 WSI，而不会因临床病理学家普遍经历的疲劳而产生潜在偏差。它将极大减轻日常病理诊断的繁重临床负担，改善CRC患者的治疗。该工具可推广到基于图像识别的其他癌症诊断。在线版本包含可在 10.1186/s12916-021-01942-5 获取的补充材料。
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. 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. 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. This first-ever generalizable AI system can handle large amounts of WSIs consistently and robustly without potential bias due to fatigue commonly experienced by clinical pathologists. It will drastically alleviate the heavy clinical burden of daily pathology diagnosis and improve the treatment for CRC patients. This tool is generalizable to other cancer diagnosis based on image recognition. The online version contains supplementary material available at 10.1186/s12916-021-01942-5.
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DOI：
10.1056/nejmp1607591
发表时间：
2016-09-22
期刊：
The New England journal of medicine
影响因子：
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作者：
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