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.
medRxiv : the preprint server for health sciences
|June 30, 2025
概括
与传统方法相比,深度学习模型显著改善了头癌症的预测能力. 使用3D剂量数据可以提高预测这种常见辐射副作用的准确性.
科学领域:
- 辐射瘤学 辐射瘤学
- 医疗成像医学成像
- 机器学习 机器学习
背景情况:
- 晚期消化不良是头癌 (HNC) 放射治疗后的一个重要并发症.
- 传统的正常组织并发症概率 (NTCP) 模型在预测毒性方面存在局限性.
- 深度学习 (DL) 提供了一种使用3D剂量数据进行改进预测的新方法.
研究的目的:
- 通过使用3D DL NTCP模型来提高失消症预测的准确性.
- 将3D DL NTCP模型的性能与传统的NTCP模型进行比较.
- 通过DL注意力地图识别影响失调风险的关键解剖区域.
主要方法:
- 分析了1484名HNC患者的多机构队列.
- 使用3D剂量分布和患者数据训练和验证了一种3DDL模型 (残留网络).
- 使用曲线下的面积 (AUC) 和校准来评估模型性能,与传统的NTCP模型相比.
主要成果:
- 3D DL 模型表现出比传统的 NTCP 模型更高的性能.
- DL模型在独立的 (0.80-0.84对比0.76) 和外部的 (0.73-0.74对比0.63) 测试集中实现了更高的AUC值.
- DL注意力图强调了口腔和上喉收缩肌作为关键区域.
结论:
- 3D DL NTCP模型可以更好地预测与辐射相关的食障碍.
- 使用3D输入数据比传统的离散剂量参数提供了显著的优势.
- DL注意力图增强了对失调病因的理解,并支持了临床实用性.
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