可解释的多式放射病理学模型预测了食道状细胞癌中新辅助化学免疫治疗的病理完整反应
Baojia Qi1,2, Zhaoyu Jiang1,3, Haixia Shen1,2
1Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China.
Journal for immunotherapy of cancer
|December 21, 2025
概括
这项研究开发了一个可解释的机器学习模型,集成CT放射学和病理学,以预测新辅助化疗免疫治疗 (nCIT) 后食道状细胞癌 (ESCC) 患者的病理完整反应 (pCR). 该模型准确预测治疗结果,有助于个性化患者管理.
科学领域:
- 在瘤学瘤学.
- 放射学 放射学是一门学科.
- 病理学 病理学 病理学
- 人工智能的人工智能
背景情况:
- 准确预测病理完整反应 (pCR) 对于定制新辅助化疗免疫疗法 (nCIT) 在食道状细胞癌 (ESCC) 中至关重要.
- 目前的预测方法缺乏整合多式联运数据以提高准确性.
研究的目的:
- 开发和外部验证可解释的多模式机器学习框架,用于预测ESCC患者的pCR.
- 整合CT放射学和H&E染色全幻灯片图像病理学,以提高预测性能.
主要方法:
- 一项回顾性研究涉及335名ESCC患者接受nCIT治疗.
- 开发单模放射学,病态学和多模聚变模型 (中间和晚期聚变).
- 外部验证使用来自多个机构的数据,通过AUC,准确性,敏感性,特异性和F1得分来评估性能.
主要成果:
- 中间融合模型 (MIFM) 在所有队列中表现出卓越的性能,实现了高AUC和精度.
- 这种MIFM的表现优于单模式模型和晚期融合模型 (MLFM).
- 探索性分析显示,基于预测的PCR状态,对总生存期的预后分层.
结论:
- 一个可解释的放射病理学聚变框架已成功开发和验证,用于在nCIT后的ESCC中预测pCR.
- 这种人工智能驱动的工具,利用标准护理数据,显示出在ESCC管理中引导个性化治疗决策的潜力.
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