多序MRI深度学习和息地放射学用于预测不匹配修复状态和结直肠肝转移的预后:一个多中心研究
Zhuofu Li1,2,3,4, Jianing Zhang1,2,3,4, Chao Sun5
1Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China.
La Radiologia medica
|November 13, 2025
概括
使用多序MRI的深度学习模型有效预测结直肠肝转移 (CRLM) 中的不匹配修复 (MMR) 状态. 深度学习模型还有助于预后分层,以实现个性化的患者治疗.
科学领域:
- 放射学 放射学是一门学科.
- 在瘤学瘤学.
- 医学成像分析 医学成像分析
背景情况:
- 不匹配修复 (MMR) 缺陷在结直肠肝转移 (CRLM) 治疗中至关重要.
- 准确的MMR状态和预后的术前预测对于患者管理至关重要.
研究的目的:
- 评估息地放射学和深度学习 (DL) 模型,使用多序MRI进行手术前预测CRLM中MMR状态.
- 评估这些模型的预后分层能力.
主要方法:
- 一项对178名CRLM患者的回顾性研究,分为培训,内部和外部验证队列.
- 使用T2WI,DWI和对比增强MRI构建经典放射学,息地放射学和DL模型.
- 卡普兰-梅尔生存分析用于预后分层.
主要成果:
- 在内部和外部验证中,DL模型在预测MMR状态方面表现优于放射学模型.
- DL模型实现了0.817 (内部) 和0.710 (外部) 的AUC.
- 在DL输出子组之间观察到预后的显著差异,表明预后分层潜力.
结论:
- 基于多序MRI的息地放射学和DL模型可以有效地预测CRLM MMR状态.
- DL模型显示出卓越的性能,有助于对CRLM患者的预后分层.
- 这些模型可以帮助个性化临床治疗和预测CRLM的预后.
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