在非小细胞肺癌患者中使用计算机断层扫描预测辐射肺炎的终端到终端预处理:视觉转换器方法
Julie Midroni1,2, Felipe S Torres3,4, Jay Hennessy5
1Temerty Faculty of Medicine, University of Toronto, Toronto, Canada.
JCO clinical cancer informatics
|February 25, 2026
概括
人工智能可以预测肺癌患者的辐射肺炎 (RP). 将CT扫描数据与临床因素结合起来,可以提高预测准确度,突出显示CT扫描.
科学领域:
- 放射治疗和医学成像技术
- 在瘤学中使用人工智能
- 肺部医学 肺部医学
背景情况:
- 辐射性肺炎 (RP) 是胸部放射治疗后的显著毒性.
- 准确预测RP对于优化治疗和患者结果至关重要.
- 当前的预测模型可能无法充分利用所有可用的患者数据.
研究的目的:
- 开发和评估一种人工智能 (AI) 模型,用于预测非小细胞肺癌 (NSCLC) 患者的RP.
- 评估计算机断层扫描 (CT) 扫描衍生特征在RP风险分层中的附加值.
- 探索基于CT的风险得分与临床和剂量测量变量的整合.
主要方法:
- 开发了一个基于变压器的AI模型,在CT扫描中的肺功能数据上进行预训练.
- 使用转移学习来调整使用机构患者数据 (2002-2020) 的RP预测模型.
- 结合临床/剂量计变量,CT衍生风险得分和综合特征的模型进行了训练和交叉验证.
主要成果:
- 综合模型,将基于CT的风险得分与临床参数相结合,实现了0.74.7的接收器操作特征曲线 (AUC) 下的改善面积.
- 与仅使用临床/剂量计或CT数据的模型相比,组合模型在固定的特异性为60%时显示出更高的灵敏度.
- 激活映射表明,CT-only模型专注于上和右肺区域.
结论:
- 将AI衍生的CT风险评分与临床数据相结合,显著提高了NSCLC患者的RP预测.
- CT扫描包含有价值的信息,可以改善传统因素之外的RP风险分层.
- 开发的AI模型及其发现为更个性化的放射治疗规划提供了潜力.
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