一种后勤回归模型,用于预测边缘切除胰腺癌患者的长期存活率,这些患者接受了前期手术
Jin-Can Huang1, Shao-Cheng Lyu1, Bing Pan1
1Hepatobiliary, Pancreas & Spleen Surgery Department, Beijing Chao-Yang Hospital, Capital Medical University, 8 Gongren Tiyuchang Nanlu, Chaoyang District, Beijing, 100020, P. R. China.
概括
一种后勤回归模型预测了接受前期手术的边缘切除性胰腺癌 (BRPC) 患者的长期存活率. 关键因素包括年龄,血管侵袭和淋巴腺病,优于其他机器学习模型.
科学领域:
- 在瘤学瘤学.
- 手术瘤学手术瘤学
- 机器学习在医学中的应用
背景情况:
- 机器学习模型越来越多地用于癌症的预后评估.
- 边缘切除性胰腺癌 (BRPC) 的预测策略仍在争论中.
- 这项研究开发了一种后勤回归模型,用于预测BRPC患者在前期手术后的长期存活率.
研究的目的:
- 建立一个逻辑回归模型来预测BRPC患者的长期存活率.
- 在这个队列中确定影响长期生存的独立预后因素.
- 将逻辑回归的预测精度与其他机器学习模型进行比较.
主要方法:
- 对104名接受了门静脉切除的前置手术的BRPC患者的回顾性审查 (2011年1月至2020年12月).
- 患者被分为短期 (≤2年) 和长期 (>2年) 存活组.
- 使用了单变量/多变量分析,逻辑回归建模,SHAP值分析和10倍交叉验证.
主要成果:
- 后勤回归模型实现了高精度 (AUC 0.881培训,0.875验证).
- 年龄,血管侵袭的长度,血管形态形和局部淋巴腺病被确定为独立的危险因素.
- 与SVM,随机森林,决策树和XGBoost.com相比,物流回归模型显示出更高的预测性能 (AUC0.864).
结论:
- 年龄,血管侵袭特征和淋巴腺病是BRPC患者长期存活的关键独立风险因素.
- 开发的后勤回归模型准确地预测了长期生存.
- 这个模型可以帮助外科医生为BRPC患者做出治疗决策.
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