机器学习模型在患有结直肠癌的患者中预测骨肌肉尺度
Jun Young Lim1, Young Min Kim1, Hye Sun Lee2
1Yonsei University College of Medicine, Seoul, Republic of Korea.
Nutrition (Burbank, Los Angeles County, Calif.)
|August 2, 2023
概括
一个新的机器学习模型预测了结肠直肠癌患者的骨肌肉量表 (SMG),这是肉症的指标. 这种工具可以在没有计算机断层扫描的情况下估计石状况,帮助治疗监测.
科学领域:
- 在瘤学瘤学.
- 放射学 放射学是一门学科.
- 机器学习 机器学习
背景情况:
- 骨肌肉测量仪 (SMG) 是肉症的成像指标.
- 目前的SMG测量需要计算机断层扫描,限制了其使用.
- 皮症是结肠直肠癌患者的一个担忧.
研究的目的:
- 开发一种机器学习算法,以预测结直肠癌患者的SMG.
- 用临床和炎症标志物来预测SMG.
- 为了创建一个查工具,用于sarcopenia没有计算机断层扫描.
主要方法:
- 对于变量选择和预测签名构建,使用了最小绝对收缩和选择运算符 (LASSO) 回归模型.
- 评估了开发的模型的预测准确性,称为线性预测器-SMG (LP-SMG).
- 模型性能使用接收器运行特征曲线 (AUC) 下面面积和决策曲线分析进行了比较.
主要成果:
- 分析了1094名结肠直肠癌患者,在约21%的病例中发现了低SMG.
- LP-SMG被确定为低SMG的独立预测因子 (赔率比=1329.431,P<.001).
- 在训练和测试组中,LP-SMG在训练和测试组中都表现出强且类似的预测性表现 (AUC = 0.846与0.869),并且表现优于单个临床变量.
结论:
- 与单个临床变量相比,开发的LP-SMG模型在预测低SMG方面表现优异.
- 这种机器学习方法提供了一个基于非成像的查工具,用于结直肠癌患者的肉类症.
- 在治疗期间,LP-SMG可以在没有计算机断层扫描的情况下促进肉类的检测.
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