使用临床和成像数据,动态预测头部和部癌症的放射治疗毒性
1Centre Hospitalier de l'Université de Montréal (CHUM), Montréal, QC, Canada.
概括
临床数据,特别是早期减肥,最好预测头癌和鼻管需要的放射治疗毒性. 在这项研究中,成像和放射学并没有改善早期毒性预测.
科学领域:
- 在瘤学瘤学.
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 对头癌 (HNC) 的放射治疗 (RT) 是有效的,但导致患者显著的毒性.
- 预测诸如鼻胃 (NG) 管放置,住院和放射性瘤等毒性对于优化HNC治疗至关重要.
- 当前的预测模型可能无法充分利用动态成像和临床数据.
研究的目的:
- 开发一个动态的深度学习模型来预测主要的HNC RT毒性.
- 评估整合每日束计算机断层扫描 (CBCT) 数据 (解剖学变形和放射学) 与临床变量用于早期毒性预测的附加值.
- 为了确定序列成像或剂量测量功能是否提高了早期预测的准确性.
主要方法:
- 对接受RT治疗的1,012名HNC患者 (2017-2022) 的回顾性分析.
- 开发一个多分支的3D ResNet50和多层感知子模型,其中包括临床数据,每日CBCT的雅可比决定数矩阵 (Jf) 和放射学.
- 权重二进制交叉损失被用于类不平衡,预测被评估在第10个RT分数.
主要成果:
- 仅仅临床特征就提供了最高的预测准确度:NG管70%;住院67.3%;放射性死74.2%.
- 早期体重减轻成为毒性最强的个体预测因素.
- 与单独的临床数据相比,早期Jf或放射学的整合并没有显著改善预测性能.
结论:
- 临床数据,特别是早期体重减轻,仍然是HNC RT诱导毒性的最可靠预测指标.
- 将连续成像或放射性特征添加到模型中并没有增强早期毒性预测.
- 进一步的研究可能需要用于后期预测或不同的毒性终点.
关键词:
圆束计算机断层扫描仪深度学习 (Deep Learning) 是一种深度学习.消化不良症 消化不良症头部和部新生体.机器学习 机器学习辐射伤害 辐射伤害无线电学 (Radiomics) 是一种无线电学.辐射疗法 辐射疗法风险评估 风险评估治疗结果 治疗结果更多相关视频
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