机器学习预测模型,用于在直肠癌手术后长时间延迟循环静脉关闭:一项回顾性研究
Jianying Liu1, Mengxiao Jiang2, Xiaoping Chen2
1Department of Colorectal Surgery, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, 510060, P.R. China.
World journal of surgical oncology
|May 14, 2025
概括
一个机器学习模型有效地预测了直肠癌患者的延迟静脉切口关闭,识别了诸如BMI和化疗等关键因素. 这有助于管理与长期使用结节切开术相关的患者负担.
科学领域:
- 在瘤学瘤学.
- 手术创新 在外科创新.
- 数据科学在医学中的数据科学
背景情况:
- 在直肠癌患者中,延迟的静脉切口关闭给患者带来了重大的心理,生理和社会经济负担.
- 确定延迟关闭的预测因素对于优化患者护理和资源配置至关重要.
研究的目的:
- 开发和验证一种机器学习 (ML) 模型,用于预测直肠癌患者的延迟结石管关闭.
- 为了确定影响延迟结节切开关闭的关键临床和病理因素.
主要方法:
- 这是一项追溯性研究,涉及442名直肠癌患者,他们接受了循环结节术.
- 使用LASSO回归来选择特征,使用XGBoost来构建ML模型.
- 模型性能使用ROC曲线,校准,决策曲线,灵敏度,特异性,准确性和F1得分进行评估; SHAP用于解释.
主要成果:
- 69% (305/442) 的患者经历了延迟的结节管关闭.
- 在测试组中,XGBoost模型实现了0.809的AUC.
- 确定的关键预测因素包括BMI,术后化疗,瘤与门边缘的距离,瘤透深度,新辅助化疗和静脉静脉狭窄.
结论:
- 开发的XGBoost模型在预测直肠癌患者延迟结肠口关闭方面的表现强.
- SHAP方法提高了ML模型的可解释性,澄清了各种预测因素的影响.
- 这种预测工具可以帮助临床决策和患者管理策略.
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