Automated Gleason grading of prostate cancer tissue microarrays via deep learning
Automated Gleason grading of prostate cancer tissue microarrays via deep learning
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DOI：
10.1038/s41598-018-30535-1
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发表时间：
2018-08-13
影响因子：
4.6
通讯作者：
Claassen, Manfred
中科院分区：
文献类型：
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作者：
Arvaniti, Eirini;Fricker, Kim S.;Claassen, Manfred
自20世纪60年代以来，格里森分级系统仍然是前列腺癌患者最有效的预后预测指标。它的应用需要训练有素的病理学家，繁琐，但病理学家之间的重复性有限，特别是对于中间的Gleason评分7。自动注释程序是弥补这些限制的可行解决方案。在这项研究中，我们提出了一种深度学习方法，用于苏木精-伊红(H&E)染色的前列腺癌组织微阵列的自动Gleason分级。我们的系统在641名患者的发现队列上使用详细的Gleason注释进行培训，然后在由两名病理学家注释的245名患者的独立测试队列上进行评估。在测试队列中，模型与每个病理学家之间的注释者间一致性(通过Cohen的二次kappa统计量进行量化)分别为0.75和0.71，与病理学家间的一致性(kappa=0.71)相当。此外，该模型的Gleason评分分配实现了病理学专家级别的患者分层，根据测试队列可用的疾病特定生存数据，将患者分成预测不同的组。总体而言，我们的研究显示，关于基于深度学习的解决方案对更客观和可重复性的前列腺癌分级的适用性，特别是对于具有异质性Gleason模式的病例，我们的研究结果令人振奋。
The Gleason grading system remains the most powerful prognostic predictor for patients with prostate cancer since the 1960s. Its application requires highly-trained pathologists, is tedious and yet suffers from limited inter-pathologist reproducibility, especially for the intermediate Gleason score 7. Automated annotation procedures constitute a viable solution to remedy these limitations. In this study, we present a deep learning approach for automated Gleason grading of prostate cancer tissue microarrays with Hematoxylin and Eosin (H&E) staining. Our system was trained using detailed Gleason annotations on a discovery cohort of 641 patients and was then evaluated on an independent test cohort of 245 patients annotated by two pathologists. On the test cohort, the inter-annotator agreements between the model and each pathologist, quantified via Cohen's quadratic kappa statistic, were 0.75 and 0.71 respectively, comparable with the inter-pathologist agreement (kappa = 0.71). Furthermore, the model's Gleason score assignments achieved pathology expert-level stratification of patients into prognostically distinct groups, on the basis of disease-specific survival data available for the test cohort. Overall, our study shows promising results regarding the applicability of deep learning-based solutions towards more objective and reproducible prostate cancer grading, especially for cases with heterogeneous Gleason patterns.