基于 DNA 超分子水凝胶的保护性 NK 细胞储库用于增强三阴性乳腺癌治疗
Protective NK Cell Reservoir Based on DNA Supramolecular Hydrogel for Enhanced Triple-Negative Breast Cancer Therapy.
CELL INTELLIGENCE · 肿瘤细胞治疗研究
肿瘤细胞治疗研究
多助手方法改善乳腺癌间质 TIL（肿瘤浸润淋巴细胞）（sTILs）评估：多机构环形研究结果
英文原题：Multi-assistant methods improve stromal tumor-infiltrating lymphocytes (sTILs) assessment in breast cancer: results of multi-institutional ring studies.
查看英文原题
Multi-assistant methods improve stromal tumor-infiltrating lymphocytes (sTILs) assessment in breast cancer: results of multi-institutional ring studies.
分数与星级只用于站内排序 —— 不代表疗效、安全性或个人适用性。
研究概要
RC 和 AI 辅助技术可以降低由异质性分布导致的解读不确定性。
研究思路结论见上方概要
背景
间质TIL（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评估难点，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。
方法
开展了三轮循环研究（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AI算法，分别用于创建RC和计算全切片图像判读。将AI方法识别出的间质区域及相关的sTIL评分作为参考提供给病理学家。RS1中使用50例手术切除病例进行观察者间一致性分析。RS2中评估了200个带有挑战因素的FOV，以检验RC辅助和AI辅助方法的准确性，而RS3中使用167例病例验证其临床性能。
结果
在RC的协助下，RS1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]。ICC的最大提升见于异质性，从中等（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，异质性和间质因子FOV的分级均有显著改善。AI辅助方法对异质性和间质因子分析的ICC达到了与RC协助下高级组相当的水平。AI辅助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，优于视觉评估的AUC 0.775。
展开英文摘要原文
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.
论文信息
- 作者
- Zhao M、Dong P、Li Z、Li J、Wu S、Xing H、Zhang P、Zhang J
- 第一作者单位
- Department of Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei.China
- 通讯作者单位
- Department of Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei. Electronic address: liuyp@hebmu.edu.cn.China
- 文献类型
- 多中心研究
- 期刊
- ESMO open2025 May
- 原文标识
- PubMed 40373351 · DOI 10.1016/j.esmoop.2025.105095
在 PubMed 查看 → 出版商原文（DOI） 全文 PDF（PMC）· 可下载 治疗专题与资料阅读指南 资料来源与翻译说明 报告译文或资料问题 →