中国普通外科杂志2026，Vol.35Issue(7)：1336-1345,10.DOI:10.7659/j.issn.1005-6947.250674
基于LASSO-Logistic回归的原发性肝癌靶向治疗患者预后不良风险预测模型构建与验证
Development and validation of a LASSO-Logistic regression-based risk prediction model for poor prognosis in patients with primary liver cancer receiving targeted therapy
摘要
Abstract
Background and Aims:Clinical outcomes after targeted therapy vary substantially among patients with primary liver cancer(PLC).Early identification of patients at high risk of poor prognosis is essential for individualized treatment and prognostic assessment.This study aimed to identify risk factors associated with poor prognosis and develop a risk prediction model for PLC patients receiving targeted therapy. Methods:Clinical data from 160 PLC patients who underwent targeted therapy between June 2016 and June 2019 were retrospectively collected and followed for 5 years.According to RECIST 1.1,patients were classified into a favorable prognosis group(n=112)and an unfavorable prognosis group(n=48).LASSO regression was used for variable selection,followed by multivariate Logistic regression to identify independent prognostic factors.A risk prediction model was established and evaluated using calibration analysis,the Hosmer-Lemeshow goodness-of-fit test,and receiver operating characteristic(ROC)curve analysis. Results:Poor prognosis occurred in 48 patients(30.0%).Ten candidate variables were selected by LASSO regression.Multivariate Logistic regression identified tumor diameter>5 cm(OR=3.216,95%CI=1.582-6.537),BCLC stage C(OR=2.779,95%CI=1.367-5.649),Cheng's classification type Ⅱ-Ⅳ portal vein tumor thrombus(OR=4.573,95%CI=2.191-9.545),incomplete tumor capsule(OR=3.099,95%CI=1.524-6.300),multinodular confluent tumor margin(OR=4.121,95%CI=2.027-8.377),and elevated AFP level(OR=2.380,95%CI=1.171-4.838)as independent predictors of poor prognosis(all P<0.05).The model demonstrated satisfactory performance,with a C-index of 0.784 and good calibration(Hosmer-Lemeshow test,P=0.359).The AUC was 0.851(95%CI=0.764-0.938),with a sensitivity of 0.846 and a specificity of 0.894. Conclusion:Large tumor size,advanced BCLC stage,extensive portal vein tumor thrombus,incomplete capsule,multinodular confluent tumor margin,and elevated AFP level are independent risk factors for poor prognosis in PLC patients receiving targeted therapy.The LASSO-Logistic regression-based model shows good discrimination and calibration and may facilitate risk stratification and individualized clinical management.关键词
肝肿瘤/靶向治疗/预后/危险因素Key words
Liver Neoplasms/Targeted Therapy/Prognosis/Risk Factors分类
医药卫生引用本文复制引用
田涛,郭伟,孙晓刚,张双卫,李宁..基于LASSO-Logistic回归的原发性肝癌靶向治疗患者预后不良风险预测模型构建与验证[J].中国普通外科杂志,2026,35(7):1336-1345,10.基金项目
山西省晋城市重点科技研发基金资助项目(20210105). （20210105）
