基于瘤细胞和透性免疫细胞的监督学习人工智能多式模式平台用于瘤预后
Xin-Jia Cai1, Chao-Ran Peng2, Chuan-Yang Ding2
1Central Laboratory, Peking University School and Hospital of Stomatology & National Center for Stomatology & National Clinical Research Centre for Oral Diseases & National Engineering Research Centre of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & Research Centre of Engineering and Technology for Computerized Dentistry Ministry of Health & NMPA Key Laboratory for Dental Materials, Beijing, China.
NPJ precision oncology
|November 14, 2025
概括
人工智能 (AI) 平台预测口腔状细胞癌 (OSCC) 患者的生存率. 将AI与临床数据相结合,显著改善了OSCC的个性化生存评估.
科学领域:
- 数字病理学数字病理学
- 在瘤学瘤学.
- 人工智能的人工智能
背景情况:
- 准确预测口腔状细胞癌 (OSCC) 的生存率是一个重大的临床挑战.
- 现有的预后工具需要改进,以实现个性化的患者管理.
研究的目的:
- 开发和评估新的人工智能 (AI) 平台,用于评估OSCC患者的整体存活率.
- 将基于AI的生存预测与传统的临床特征进行比较.
主要方法:
- 利用来自多中心队伍的240个全幻灯片图像用于AI模型开发.
- 实施了带有注释的监督深度学习 (DL) (PathS模型) 和使用幻灯片级标签的弱监督DL.
- 采用梯度加权类激活映射用于特征识别.
主要成果:
- 监督DL (PathS模型) 的c指数达到0.809,超过弱监督DL (c指数=0.707) 和传统的临床特征 (CS模型,c指数=0.721).
- 人工智能确定了瘤细胞和瘤透免疫细胞作为关键的预后特征.
- 将PathS签名与CS结合的多式联络符号 (multimodal nomogram) 提高了生存预测的准确性 (c-index=0.817).
结论:
- 新型人工智能平台显示出在OSCC中改善整体生存评估的巨大潜力.
- 多式联络型诺米图为OSCC患者的个性化生存预测提供了实质性的进步.
- 对预后特征的AI驱动的洞察力提高了对OSCC进展的理解.
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