在直肠癌患者中预测治疗反应:对多模式成像方法的放射学研究
Yan Huang1, Le Lin1, Shuke Sun1
1Department of Medical Imaging, The First People's Hospital of Hechi, No.124 Guiyu Street, Hechi, 546300, Guangxi, China.
Medical engineering & physics
|December 6, 2025
概括
来自CT,MRI和EUS的放射学特征预测了直肠癌治疗反应. 内肠道超声波 (EUS) 使用机器学习模型显示了最高的准确性.
科学领域:
- 医疗成像医学成像
- 在瘤学瘤学.
- 机器学习 机器学习
背景情况:
- 局部晚期直肠癌 (LARC) 需要准确的治疗反应预测.
- 医学图像的放射学分析为非侵入性评估提供了潜力.
研究的目的:
- 评估CT,MRI和EUS的放射特征,结合临床和剂量计数据,用于预测LARC患者的治疗反应.
- 为了比较使用不同的成像模式的机器学习模型的预测性能.
主要方法:
- 从84名LARC患者的CT,MRI和EUS图像中提取了放射性特征.
- 使用拉索,MRMR和RFE进行了特征选择.
- 机器学习模型,包括SVM和LR,经过训练并对预测准确性进行评估.
主要成果:
- CT:用MRMR/RFE实现LR的ACC为0.79,AUC为0.78. 这是一个很好的结果.
- 核磁共振:使用MRMR的SVM实现了ACC0.88,AUC0.87.
- EUS:MRMR的LR实现了ACC0.89,AUC0.89,表现优于CT和MRI.
结论:
- 预治疗CT,MRI和EUS中的放射学特征可以预测LARC治疗反应.
- 机器学习模型,特别是带有MRMR/RFE的SVM和LR,显示出强大的预测能力.
- 在LARC中,EUS为预测治疗反应提供了最高的准确性.
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