基于身体组成和机器学习,预测直肠癌患者3年全因死亡率
Xiangyong Li1, Zeyang Zhou1, Xiaoyang Zhang1
1Department of Gastrointestinal Surgery, The Second Affiliated Hospital of Soochow University, Suzhou, China.
Frontiers in nutrition
|March 18, 2025
概括
机器学习模型通过分析身体成分来预测直肠癌手术后3年死亡率. 使用脂肪组织和肌肉质量等参数的XGBoost模型显示了对患者结果的最佳预测准确性.
科学领域:
- 在瘤学瘤学.
- 放射学 放射学是指放射学
- 数据科学数据科学数据科学
背景情况:
- 腹部脂肪组织和肌肉质量显著影响直肠癌的预后.
- 腹腔镜全腹腔切除术 (LaTME) 是用于直肠癌的常见手术程序.
研究的目的:
- 开发和验证机器学习 (ML) 模型,以预测在LATME后3年内因任何原因死亡率.
- 为了确定影响直肠癌手术后生存的关键身体组成参数.
主要方法:
- 收集了手术前CT扫描数据和从接受LaTME的186名患者的临床特征.
- 开发了七种ML模型来预测3年生存率,将患者分为培训和验证队列.
- 使用SHAP值来解释模型预测并识别重要变量.
主要成果:
- 在验证队列中,XGBoost模型实现了最高的预测性能,AUROC为0.911.
- 死亡率的关键预测因素包括皮下脂肪组织指数 (SAI),内脏脂肪组织指数 (VAI) 和骨肌密度 (SMD).
- 其他重要因素是内脏和皮下脂肪组织比率 (VSR) 和皮下脂肪组织密度 (SAD).
结论:
- 整合身体组成的机器学习模型有效地预测了拉特米后所有原因的死亡率.
- XGBoost模型在识别死亡风险较高的患者方面表现出卓越的性能.
- 身体组成分析对于改善直肠癌患者的预后准确性至关重要.
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