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预测临床,放射学和深度学习特征的NSCLC患者的整体存活率

Hemalatha Kanakarajan¹, Jikai Zhou², Aiara Lobo Gomes^3,4

¹Department of Cognitive Neuropsychology, Tilburg University, Tilburg, The Netherlands. H.Kanakarajan@tilburguniversity.edu.

Journal of imaging informatics in medicine

|January 13, 2026

概括

将临床数据与放射学,深度学习和剂量测量特征相结合,显著改善了接受放射治疗的非小细胞肺癌 (NSCLC) 患者的整体生存预测.

科学领域:

在瘤学瘤学.
医疗成像医学成像
放射疗法是一种放射治疗.

背景情况:

准确的整体存活率 (OS) 估计对于非小细胞肺癌 (NSCLC) 治疗计划至关重要.
之前的研究强调了放射学和深度学习 (DL) 在改善生存预测方面的潜力.

研究的目的:

评估一个整合临床,放射学,DL和剂量测量特征的模型是否优于仅使用这些数据的子集进行NSCLCOS预测的模型.
评估多模式数据集成在预测放射治疗后患者结果的附加值.

主要方法:

分析了219名NSCLC患者治疗前肺部CT扫描和临床数据的队列.
放射性,DL (3D ResNet) 和剂量测量特征被提取并与临床数据结合.
一个整体模型 (XGB和NN分类器) 被开发并使用各种特征组合进行评估.

主要成果:

仅使用临床特征的模型实现了0.71和72.73%的AUC准确度.
结合临床,放射学,剂量和DL特征的综合模型表现出卓越的性能,AUC为0.84和88.64%的准确性.
世界卫生组织的绩效状态被确定为组合模型中最重要的因素.

结论:

将放射学,剂量计和DL特征与临床数据相结合,显著提高了接受放射治疗的NSCLC患者的OS预测准确度.
这种增强的预测准确性支持个性化,基于风险的治疗规划,可能导致改善患者的治疗结果和生活质量.

关键词:

深度学习 (Deep Learning) 是一种深度学习.肺癌肺癌是一种肺癌.总体生存率总体生存率无线电学 (Radiomics) 是一种辐射学.放射疗法是一种放射治疗.

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