根据3D剂量,CT和细分的基础,深度学习NTCP模型用于头部和部癌症患者放射治疗后晚期消化障碍
S P M de Vette1, H Neh1, L van der Hoek1
1Department of Radiotherapy, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands.
概括
深度学习模型显著改善了对头癌患者辐射诱导的食障碍的预测. 这些先进的模型利用3D数据,优于传统方法,以获得更好的患者结果.
科学领域:
- 辐射瘤学 辐射瘤学
- 医学成像医学成像
- 机器学习是机器学习.
背景情况:
- 晚期消化不良是头癌 (HNC) 放射治疗的削弱副作用.
- 传统的正常组织并发症概率 (NTCP) 模型在预测这种毒性方面存在局限性.
- 深度学习 (DL) 通过利用复杂的3D剂量数据提供了一个有希望的方法.
研究的目的:
- 开发和评估基于3D深度学习 (DL) 的NTCP模型,用于预测晚期辐射相关的食障碍.
- 将3D DL NTCP模型的性能与传统的NTCP模型进行比较.
- 通过DL注意力地图来识别影响食障碍预测的关键解剖区域.
主要方法:
- 分析了1484名HNC患者的多机构队列.
- 训练了一个3D残余网络DL模型,包括3D剂量分布,器官细分和CT扫描.
- 与传统模型相比,使用曲线下面积 (AUC) 和校准曲线来评估模型性能.
主要成果:
- 3D DL NTCP模型在独立和外部测试集 (AUC 0.80-0.84对0.76和0.73-0.74对0.63) 上表现出比传统模型更好的预测性能.
- 与传统方法相比,DL模型显示了更好的校准.
- 注意地图突出显示了口腔和上大收缩肌作为难食预测的关键区域.
结论:
- 3D DL NTCP模型在预测晚期辐射相关的食障碍方面明显优于传统模型.
- 使用3D输入数据比传统的离散剂量参数提供了实质性的优势.
- 由DL驱动的注意力地图提供了对食障碍的解剖学驱动因素的宝贵见解,增强了预测效用.
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