对于接受终极放射治疗的宫癌患者的预后预测的多模式深度学习模型:一项多中心研究
Weiping Wang1, Guang Yang2, Yulin Liu1
1Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
NPJ digital medicine
|August 5, 2025
概括
一个新的深度学习模型,CerviPro,准确地预测了局部晚期宫癌 (LACC) 患者的无疾病生存期 (DFS). 这种多模式的方法集成成像,放射学和临床数据,以指导个性化治疗策略.
科学领域:
- 在瘤学瘤学.
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 精确的生存预测对于局部晚期宫癌 (LACC) 个性化治疗至关重要.
- 目前的预测模型可能无法充分利用多式联运数据来提高准确性.
研究的目的:
- 开发和验证基于深度学习的多式预后模型CerviPro,用于预测LACC患者的无病生存期 (DFS).
- 用集成的临床,成像和放射性特征来评估CerviPro的性能.
主要方法:
- 开发一个深度学习模型 (CerviPro),使用来自1018名接受终极放射治疗的LACC患者的数据.
- 整合治疗前后的CT成像,手工制作的放射性特征和临床变量.
- 在内部和外部队列中验证模型的预测性能.
主要成果:
- CerviPro实现了强大的预测性能,C指数为0.81 (内部) 和0.70/0.66 (外部验证).
- 该模型有效地将患者分为高风险和低风险的DFS组.
- 多模式特征融合显著优于使用单一数据源的模型,证明了协同作用的价值.
结论:
- CerviPro是LACC临床上有价值的预后工具,集成多种数据来准确预测DFS和复发风险.
- 该模型对风险分层的能力支持了指导LACC个性化治疗策略的潜力.
- 多模式数据集成提供了一种强大的方法,可以提高瘤应用中的预后准确性.
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