基于机器学习的预测模型和在线计算器用于结直肠癌术后的LDVT
Zhihui Zhang1,2, ShiCong Xu3, MeiXuan Song2
1School of Nursing, Southwest Medical University, Luzhou, Sichuan, China.
Frontiers in oncology
|October 27, 2025
概括
一个新的机器学习模型准确地预测了下肢深静脉血栓塞 (LDVT) 在结直肠癌 (CRC) 手术后的风险. 这种工具有助于识别高风险患者进行早期预防,改善结果.
科学领域:
- 在瘤学瘤学.
- 血管外科 血管外科
- 医疗信息学 医疗信息学
背景情况:
- 下肢深静脉血栓塞 (LDVT) 是常见的,经常未被发现的结肠直肠癌 (CRC) 手术后的并发症.
- 微妙的早期症状和LDVT的延迟诊断可能导致严重的并发症,如血栓后综合征或致命事件.
- 目前在CRC患者中早期LDVT风险评估的方法不足.
研究的目的:
- 确定CRC患者术后LDVT的危险因素.
- 开发一个基于机器学习 (ML) 的风险预测模型,用于LDVT.
- 为实时风险评估创建一个可访问的网络计算器.
主要方法:
- 一个回顾性分析的1200个CRC患者接受了彻底的手术.
- 使用培训/测试分割 (1000名患者) 和外部验证 (200名患者) 开发ML模型.
- 通过单变量分析和LASSO回归对风险因素进行选,然后使用AUC,精度,灵敏度和F1得分进行模型比较.
主要成果:
- 30.75%的患者发生了LDVT;随机森林 (RF) 模型实现了高预测性能 (AUC:0.942培训,0.897测试,0.891验证).
- 通过SHAP分析识别的关键预测因素包括D-二次体,手术前肠道阻塞,卡普里尼得分,年龄,血液损失和糖尿病.
- 开发了一个基于网络的计算器,用于个性化LDVT风险估计.
结论:
- 一个经过验证的基于ML的RF模型有效地预测了CRC患者的术后LDVT.
- 该模型整合了重要的临床预测因素,提供了高准确性和通用性.
- 在线计算器有助于迅速评估风险,有助于实施预防策略并改善患者的治疗结果.
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