Zhao M, Dong P, Li Z, Li J, Wu S, Xing H, Zhang P, Zhang J, Shen H, Yang H, Yang W, Han X, Liu Y
Department of Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei.
AI Lab, Tencent, Shenzhen, Guangdong, China.
ESMO Open. 2025 May;10(5):105095. doi: 10.1016/j.esmoop.2025.105095. Epub 2025 May 14.
BACKGROUND: Stromal tumor-infiltrating lymphocytes (sTILs) have significant prognostic value for breast cancer patients, but its accurate assessment can be very challenging. We comprehensively studied the pitfalls faced by pathologists with different levels of professional experience, and explored clinical applicability of reference cards (RCs)- and artificial intelligence (AI)-assisted methods in assessing sTILs. MATERIALS AND METHODS: Three rounds of ring studies (RSs) involving 12 pathologists from four hospitals were conducted. AI algorithms based on the field of view (FOV) and whole section were proposed to create RCs and to compute whole-slide image interpretations, respectively. Stromal regions identified and the associated sTIL scores by the AI method were provided to the pathologists as references. Fifty cases of surgical resections were used for interobserver concordance analysis in RS1. A total of 200 FOVs with challenge factors were assessed in RS2 for accuracy of the RC-assisted and AI-assisted methods, while 167 cases were used to validate their clinical performance in RS3. RESULTS: With the assistance of RCs, the intraclass correlation coefficient (ICC) in RS1 increased significantly to 0.834 [95% confidence interval (CI) 0.772-0.889]. The largest enhancement in ICC, from moderate (ICC: 0.592; 95% CI 0.499-0.677) to good (ICC: 0.808; 95% CI 0.746-0.857) was observed for heterogeneity. Accuracy evaluation showed significant grade improvement for heterogeneity and stromal factor FOVs among senior, intermediate, and junior groups. The ICC of heterogeneity and stromal factor analysis by the AI-assisted method achieved a level comparable to that of the senior group with RC assistance. The area under the receiver operating characteristic (ROC) curve, denoted as AUC, for AI-assisted sTIL scores in predicting pathological complete response after neoadjuvant therapy was 0.937, which was superior to visual assessment with an AUC of 0.775. CONCLUSION: RC- and AI-assisted technology can reduce the uncertainty of interpretation caused by heterogeneous distribution.
背景：基质肿瘤浸润淋巴细胞（sTILs）对乳腺癌患者具有重要的预后价值，但其准确评估极具挑战性。我们全面研究了不同专业经验水平的病理学家所面临的陷阱，并探讨了参考卡片（RCs）和人工智能（AI）辅助方法在评估sTILs中的临床适用性。 材料与方法：开展了三轮环式研究（RSs），涉及来自四家医院的12名病理学家。提出了基于视野（FOV）和全切片的人工智能算法，分别用于创建RCs和计算全切片图像解读结果。通过人工智能方法识别的基质区域及相关的sTIL评分被提供给病理学家作为参考。在RS1中，使用50例手术切除病例进行观察者间一致性分析。在RS2中，共评估了200个具有挑战因素的FOV，以评估RC辅助和AI辅助方法的准确性，而在RS3中，使用167例病例验证其临床性能。 结果：在RCs的辅助下，RS1中的组内相关系数（ICC）显著提高至0.834 [95%置信区间（CI）0.772 - 0.889]。对于异质性，ICC的最大提升最为明显，从中度（ICC：0.592；95% CI 0.499 - 0.677）提升至良好（ICC：0.808；95% CI 0.746 - 0.857）。准确性评估显示，在高级、中级和初级组中，异质性和基质因子FOV的分级有显著改善。通过AI辅助方法进行的异质性和基质因子分析的ICC达到了与RC辅助下高级组相当的水平。在预测新辅助治疗后病理完全缓解方面，AI辅助sTIL评分的受试者操作特征（ROC）曲线下面积（AUC）为0.937，优于视觉评估的AUC（0.775）。 结论：RC和AI辅助技术可降低由异质性分布导致的解读不确定性。
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