提升肺癌免疫治疗后静脉血栓栓塞风险预测
Enhancing venous thromboembolism risk prediction after immunotherapy for lung cancer.
作者
作者单位
- Department of Pulmonary and Critical Care Medicine, Tongji Hospital, School of Medicine, Tongji University, Shanghai, People's Republic of China.
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China.
- Department of Thoracic Surgery, Ningbo No. 2 Hospital, Wenzhou Medical University, Zhejiang, China.
摘要
中文
评估免疫治疗后的静脉血栓栓塞（VTE）风险对肺癌管理仍然重要。我们分析了来自两个中心的2300例接受一线免疫治疗的患者，随机分为训练集（70%）、验证集（15%）和内部测试集（15%），并纳入来自独立外部中心的491例患者进行外部验证。比较了五种特征选择方法和五种机器学习算法，以开发6个月VTE预测模型。Lasso-logistic模型表现最佳，在内部和外部测试集中的曲线下面积分别为0.692和0.728，优于Khorana、Padua、PROTECHT、ONKOTEV和COMPASS-CAT评分（均p < 0.05）。高危组的累积VTE发生率高于低危组（12.3% vs. 4.8%，p = 0.006）。Shapley加法解释（SHAP）分析确定D-二聚体和东部肿瘤协作组（ECOG）体能状态为最有影响力的预测因子，支持个体化血栓预防决策。
English
Assessing venous thromboembolism (VTE) risk after immunotherapy remains important for lung cancer management. We analyzed 2,300 patients receiving first-line immunotherapy from two centers, randomly assigned to training (70%), validation (15%), and internal test (15%) sets, and included 491 patients from an independent external center for external validation. Five feature-selection methods and five machine-learning algorithms were compared to develop a 6-month VTE prediction model. The Lasso-logistic model showed the best performance, with areas under the curve of 0.692 and 0.728 in the internal and external test sets, respectively, outperforming Khorana, Padua, PROTECHT, ONKOTEV, and COMPASS-CAT scores (all p < 0.05). The high-risk group had a higher cumulative VTE incidence than the low-risk group (12.3% vs. 4.8%, p = 0.006). Shapley additive explanations (SHAP) analysis identified D-dimer and Eastern Cooperative Oncology Group (ECOG) performance status as the most influential predictors, supporting individualized thromboprophylaxis decisions.
分类与指标
- 研究类型
- AI/ML
- 病种
- 肺癌
- JCR 分区
- Q1
- 影响因子
- 4.5
- 新锐分区
- 3区