Bouzid Kenza, Sharma Harshita, Killcoyne Sarah, Castro Daniel C, Schwaighofer Anton, Ilse Max, Salvatelli Valentina, Oktay Ozan, Murthy Sumanth, Bordeaux Lucas, Moore Luiza, O'Donovan Maria, Thieme Anja, Nori Aditya, Gehrung Marcel, Alvarez-Valle Javier
Microsoft Health Futures, Cambridge, UK.
Cyted Ltd, Cambridge, UK.
Nat Commun. 2024 Mar 11;15(1):2026. doi: 10.1038/s41467-024-46174-2.
Timely detection of Barrett's esophagus, the pre-malignant condition of esophageal adenocarcinoma, can improve patient survival rates. The Cytosponge-TFF3 test, a non-endoscopic minimally invasive procedure, has been used for diagnosing intestinal metaplasia in Barrett's. However, it depends on pathologist's assessment of two slides stained with H&E and the immunohistochemical biomarker TFF3. This resource-intensive clinical workflow limits large-scale screening in the at-risk population. To improve screening capacity, we propose a deep learning approach for detecting Barrett's from routinely stained H&E slides. The approach solely relies on diagnostic labels, eliminating the need for expensive localized expert annotations. We train and independently validate our approach on two clinical trial datasets, totaling 1866 patients. We achieve 91.4% and 87.3% AUROCs on discovery and external test datasets for the H&E model, comparable to the TFF3 model. Our proposed semi-automated clinical workflow can reduce pathologists' workload to 48% without sacrificing diagnostic performance, enabling pathologists to prioritize high risk cases.
及时检测出食管腺癌的癌前病变——巴雷特食管，可提高患者生存率。Cytosponge-TFF3检测是一种非内镜微创检查方法，已用于诊断巴雷特食管中的肠化生。然而，它依赖于病理学家对两张苏木精-伊红（H&E）染色切片以及免疫组化生物标志物三叶因子3（TFF3）的评估。这种资源密集型的临床工作流程限制了对高危人群的大规模筛查。为提高筛查能力，我们提出一种从常规H&E染色切片中检测巴雷特食管的深度学习方法。该方法仅依赖诊断标签，无需昂贵的局部专家注释。我们在两个临床试验数据集（共1866例患者）上训练并独立验证了我们的方法。我们的H&E模型在发现数据集和外部测试数据集上分别达到了91.4%和87.3%的曲线下面积（AUROC），与TFF3模型相当。我们提出的半自动临床工作流程可将病理学家的工作量减少至48%，同时不牺牲诊断性能，使病理学家能够优先处理高风险病例。
