Multi-assistant methods improve stromal tumor-infiltrating lymphocytes (sTILs) assessment in breast cancer: results of multi-institutional ring studies

IF 7.1 2区医学 Q1 ONCOLOGY

ESMO Open Pub Date : 2025-05-01 DOI:10.1016/j.esmoop.2025.105095

M. Zhao , P. Dong , Z. Li , J. Li , S. Wu , H. Xing , P. Zhang , J. Zhang , H. Shen , H. Yang , W. Yang , X. Han , Y. Liu

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引用次数: 0

Abstract

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.

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多辅助方法改善乳腺癌间质肿瘤浸润淋巴细胞（sTILs）评估：多机构环研究结果

背景基质肿瘤浸润淋巴细胞（stil）对乳腺癌患者的预后有重要价值，但其准确评估却非常具有挑战性。我们全面研究了不同专业经验水平的病理学家面临的陷阱，并探讨了参考卡（rc）和人工智能（AI）辅助方法在评估stil中的临床适用性。材料与方法对4家医院的12名病理医师进行三轮环形研究（RSs）。提出了基于视场（FOV）和整体切片的人工智能算法，分别用于创建rc和计算整张幻灯片的图像解译。人工智能方法鉴定的基质区域及相关的sTIL评分提供给病理学家作为参考。50例手术切除病例用于RS1的观察者间一致性分析。在RS2中，共评估了200个具有挑战因子的fov，以评估rc辅助和ai辅助方法的准确性，而在RS3中，167个病例用于验证其临床表现。结果在RCs的辅助下，RS1的类内相关系数（ICC）显著增加至0.834[95%可信区间（CI） 0.772 ~ 0.889]。ICC增强最大，从中度开始(ICC: 0.592；95% CI 0.499-0.677)至良好(ICC: 0.808；95% CI 0.746-0.857)存在异质性。准确性评估显示，在高、中、低年级组中，异质性和基质因子fov有显著的等级改善。人工智能辅助方法的异质性和基质因子分析的ICC达到了与RC辅助的老年组相当的水平。人工智能辅助的sTIL评分预测新辅助治疗后病理完全缓解的受试者工作特征（ROC）曲线下面积（AUC）为0.937，优于目测的AUC为0.775。结论人工智能和人工智能辅助技术可以减少由于非均匀分布导致的判读不确定性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

ESMO Open Medicine-Oncology

CiteScore

11.70

自引率

2.70%

发文量

255

审稿时长

10 weeks

期刊介绍： ESMO Open is the online-only, open access journal of the European Society for Medical Oncology (ESMO). It is a peer-reviewed publication dedicated to sharing high-quality medical research and educational materials from various fields of oncology. The journal specifically focuses on showcasing innovative clinical and translational cancer research. ESMO Open aims to publish a wide range of research articles covering all aspects of oncology, including experimental studies, translational research, diagnostic advancements, and therapeutic approaches. The content of the journal includes original research articles, insightful reviews, thought-provoking editorials, and correspondence. Moreover, the journal warmly welcomes the submission of phase I trials and meta-analyses. It also showcases reviews from significant ESMO conferences and meetings, as well as publishes important position statements on behalf of ESMO. Overall, ESMO Open offers a platform for scientists, clinicians, and researchers in the field of oncology to share their valuable insights and contribute to advancing the understanding and treatment of cancer. The journal serves as a source of up-to-date information and fosters collaboration within the oncology community.