使用多任务深度学习和多模态核磁共振 (MRI) 预测局部高级直肠癌的复发情况
Zonglin Liu1,2, Runqi Meng3, Qiong Ma1,2
1Department of Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Rd, 270, Xuhui District, Shanghai, 200032, China.
Radiology. Imaging cancer
|May 30, 2025
概括
一个新的深度学习模型MultiRecNet准确地预测了接受新辅助化学放射治疗 (nCRT) 的局部晚期直肠癌患者的无疾病生存率. 这种自动化工具有助于使用多模式MRI数据进行预后预测.
科学领域:
- 放射学和医学成像学 医学成像学
- 在瘤学瘤学.
- 人工智能在医学中的应用
背景情况:
- 局部晚期直肠癌 (LARC) 治疗涉及新辅助化学放射治疗 (nCRT),但预测无病生存率 (DFS) 仍然具有挑战性.
- 准确的预后预测对于定制治疗策略和改善患者结果至关重要.
研究的目的:
- 开发和验证一个深度多任务网络,MultiRecNet,用于在nCRT治疗的LARC患者中完全自动预测DFS.
- 通过使用多模式MRI数据和临床信息来评估MultiRecNet的性能.
主要方法:
- 一项回顾性研究收集了来自445名LARC患者的数据,这些患者在三个中心接受了nCRT治疗.
- MultiRecNet 是为了同时执行细分,分类和生存预测任务而开发的.
- 多模式MRI (T2,ADC,D_app,K_app) 和临床数据被用作输入.
主要成果:
- 最好的MultiRecNet模型在瘤细分方面实现了0.72的Dice相似系数 (DSC).
- 该模型在内部测试组中在分类复发或转移3年后 (AUC = 0.97) 和预测DFS (C指数 = 0.92) 中表现出高准确度.
- 该模型在外部测试组 (C指数 = 0.81) 中维持了对生存预测的强表现.
结论:
- 基于MultiRecNet的模型为LARC患者在nCRT后提供完全自动化的,端到端的预后预测.
- 这种深度学习方法显示了提高DFS预测准确度和指导临床决策的巨大潜力.
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