通过利用基于OmicsMap的图像表示方式,深度放射学用于局部先进的非小细胞肺癌的预后预测
Runping Hou1, Wuyan Xia2, Md Tauhidul Islam3
1Shanghai Chest Hospital, No.241 Huai-Hai Road, Shanghai, Shanghai, 200030, CHINA.
Physics in medicine and biology
|January 21, 2026
概括
一个新的深度放射学模型,OmicsMap,准确地预测了局部晚期非小细胞肺癌 (LA-NSCLC) 患者的无进展生存率. 这种先进的生物标志物改善了风险分层,有助于为肺癌提供个性化治疗策略.
科学领域:
- 放射学和人工智能在瘤学中的应用
- 肺癌的生物标志物发现
背景情况:
- 局部晚期非小细胞肺癌 (LA-NSCLC) 患者尽管接受了标准治疗,预后却有所不同.
- 需要改善预后生物标志物以指导LA-NSCLC患者管理.
研究的目的:
- 开发和验证OmicsMap模型,这是一种深度放射学生物标志物,用于预测LA-NSCLC中无进展生存率 (PFS).
- 评估OmicsMap模型的性能与现有的临床模型和传统的放射学方法相比.
主要方法:
- 对329名LA-NSCLC患者进行了回顾性分析,这些患者接受了终极放射治疗.
- 开发OmicsMap通过将高维放射学数据转换为2D表示 (OmicsMaps) 来捕捉特征交互.
- 使用卷积神经网络从OmicsMaps中提取深度放射学特征以进行预后预测.
主要成果:
- 在独立测试组中,OmicsMap模型取得了显著的预测性能,在1-3年内,时间依赖的AUC为0.76-0.78.
- 该模型与临床模型和传统放射学模型相比,显示出更高的歧视性,C指数增加了7.69%.
- OmicsMap有效地将患者分为高风险和低风险分组,以无进展生存期 (PFS) 和整体生存期 (OS) 进行分层.
结论:
- OmicsMap模型提供了一种新的深度放射学方法,用于在LA-NSCLC中进行增强的预后预测.
- 使用OmicsMap改进的风险分层可以为个性化肺癌治疗策略的临床决策提供信息.
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