图尔塞格:对瘤透淋巴细胞的自动整体幻灯片级空间评分揭示了三阴性乳腺癌的预后模式
medRxiv : the preprint server for health sciences
|February 6, 2026
概括
在三阴性乳腺癌 (TNBC) 中,用于分析 stromal 瘤透淋巴细胞 (sTILs) 的 AI 工具提供可复制和准确的预后. 这种数字病理学方法改善了患者风险分层,以获得更好的治疗结果.
科学领域:
- 计算病理学计算病理学
- 发现生物标志物的发现.
- 乳腺癌研究 乳腺癌研究
背景情况:
- 流体瘤透淋巴细胞 (sTIL) 是预测三阴性乳腺癌 (TNBC) 治疗反应和生存的关键生物标志物.
- 目前由病理学家进行的手动sTIL评估由于读者之间的变异性而缺乏可重复性.
- 在TNBC中需要客观和标准化的STIL评估方法.
研究的目的:
- 开发和验证人工智能 (AI) 驱动的管道,用于在TNBC的整个幻灯片图像中自动细分和评分STIL.
- 与手动评分相比,评估AI衍生的全球和空间解析的sTIL分数的预后价值.
- 研究sTIL与上皮区域相对的空间分布对患者结果的影响.
主要方法:
- 开发一个AI管道 (TILseg) 处理H&E染色的整片图像来量化STILs.
- 在两个独立的TNBC患者队列 (n=57和n=43) 上验证管道,并提供临床结果数据.
- 以人工智能获得的全球和空间sTIL分数与专家手动评分和患者预后 (对新辅助化疗的反应,无复发生存率) 的比较.
主要成果:
- 由人工智能驱动的全球TILseg评分与专家病理学家有很强的一致性 (斯皮尔曼R = 0.84-0.89).
- 与手动评分相比,全球TILseg分数提供了显著改善的患者分层 (p=0.0191与p=0.0663).
- 空间分辨的TILseg,特别是50微米内上皮区域的TIL,显示出无复发生存和病理完整反应的预后意义高于全局或手动得分.
结论:
- 一个自动化,空间解析的AI工具用于sTILs评分,增强了患者风险分层.
- 这种人工智能方法改善了基于TNBC治疗反应和无复发生存率的预后.
- 开发的AI工具确立了stILs作为一个强大的诊断和预后生物标志物,特别是对于TNBC患者.
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