小柯机器人

英国剑桥大学Florian Markowetz和Rebecca C. Fitzgerald小组合作的一项最新研究，揭示了深度学习barrett食管分诊诊断方法在食管腺癌早期诊断中的应用。2021年4月15日出版的《自然-医学》发表了这项成果。

研究人员研发了一个深度学习框架，用于分析Cytosponge-TFF3测试（内窥镜微创替代方法）样本，以检测Barrett食道，其是食道腺癌的主要前体。研究人员对来自两项临床试验的数据进行了检测，并对其进行了独立验证，共分析了2,331名患者的4,662份病理切片。

该方法利用胃肠病理学家的决策模式来进行八种不同优先级的分类，以进行人工专家审查。通过在低优先级组中将手动检查替换为自动检查，该方法可减少病理医生57％的工作量，同时可与经验丰富病理医生的诊断相匹配。

研究人员表示，深度学习方法在诊断应用中已显示出优势，但是如何将其与专家常识和现有的临床决策方法最佳地结合仍具有挑战性。这个问题对于癌症的早期发现尤其重要，因为在这种情况下，大量工作流程可能会从（半）自动分析中受益。

附：英文原文

Title: Triage-driven diagnosis of Barrett’s esophagus for early detection of esophageal adenocarcinoma using deep learning

Author: Marcel Gehrung, Mireia Crispin-Ortuzar, Adam G. Berman, Maria ODonovan, Rebecca C. Fitzgerald, Florian Markowetz

Issue&Volume: 2021-04-15

Abstract: Deep learning methods have been shown to achieve excellent performance on diagnostic tasks, but how to optimally combine them with expert knowledge and existing clinical decision pathways is still an open challenge. This question is particularly important for the early detection of cancer, where high-volume workflows may benefit from (semi-)automated analysis. Here we present a deep learning framework to analyze samples of the Cytosponge-TFF3 test, a minimally invasive alternative to endoscopy, for detecting Barrett’s esophagus, which is the main precursor of esophageal adenocarcinoma. We trained and independently validated the framework on data from two clinical trials, analyzing a combined total of 4,662 pathology slides from 2,331 patients. Our approach exploits decision patterns of gastrointestinal pathologists to define eight triage classes of varying priority for manual expert review. By substituting manual review with automated review in low-priority classes, we can reduce pathologist workload by 57% while matching the diagnostic performance of experienced pathologists.

DOI: 10.1038/s41591-021-01287-9

Source: https://www.nature.com/articles/s41591-021-01287-9

Nature Medicine：《自然—医学》，创刊于1995年。隶属于施普林格·自然出版集团，最新IF：87.241
官方网址：https://www.nature.com/nm/
投稿链接：https://mts-nmed.nature.com/cgi-bin/main.plex