中国癌症防治杂志2025，Vol.17Issue(3)：289-296,8.DOI:10.3969/j.issn.1674-5671.2025.03.05
基于生物学标志和影像学特征预测肝细胞癌转化治疗后病理完全缓解列线图模型的构建与验证
Development and validation of a nomogram model for predicting pathologic complete response following conversion therapy for hepatocellular carcinoma based on biological markers and imaging features
摘要
Abstract
Objective To construct a nomogram that integraties imaging features and biomarkers to predict pathologic complete response(pCR)in patients with hepatocellular carcinoma(HCC)undergoing conversion therapy.Methods The study cohort comprised HCC patients who received the transcatheter arterial chemoembolization(TACE)and/or hepatic arterial infusion chemotherapy(HAIC)in conjunction with targeted therapy and immunotherapy at Guangxi Medical University Cancer Hospital from November 2019 to October 2024.Independent predictors of pCR were identified through univariable and multivariable logistic regression analyses,and these predictors were utilized to develop the nomogram.The performance of the nomogram was evaluated using the area under the receiver operating characteristic curve(AUC),calibration curves,and decision curve analysis(DC A).Results Among the 135 patients with HCC,27.4％(37/135)achieved pCR following treatment.The systemic inflammatory response index(SIRI),tumor biomarker response,tumor number,and tumor complete response as assessed by the modified Response Evaluation Criteria in Solid Tumors(mRECIST)were identified as independent predictors of pCR(all P＜0.05).A nomogram model was developed with AUC of 0.925(95％CI:0.882-0.967),demonstrating significantly superior predictive performance compared to the alpha-fetoprotein(AFP)response(AUC=0.655)or mRECIST complete response(AUC=0.785)(both P＜0.001).Internal validation using 1,000 times bootstrap resamples resulted in an AUC of 0.918(95％CI:0.873-0.963)for the nomogram model.The calibration curve confirmed excellent model calibration,and DCA demonstrated significant clinical utility.Conclusions The nomogram model,incorporatingSIRI,tumor biomarker response,tumor number,and mRECIST complete response,provides an accurate pCR prediction following HCC conversion therapy in HCC patients and may serve as a foundation for individualized surgical decision-making.关键词
肝细胞癌/转化治疗/列线图/病理完全缓解/生物标志物/影像学特征Key words
Hepatocellular carcinoma/Conversion therapy/Nomogram/Pathologic complete response/Biomarker/Imaging features分类
医药卫生引用本文复制引用
许少伟,黎乐群,吴飞翔,唐置鸿,韦猛,刘丹希,袁度,庞清清,白涛,陈洁,王小波..基于生物学标志和影像学特征预测肝细胞癌转化治疗后病理完全缓解列线图模型的构建与验证[J].中国癌症防治杂志,2025,17(3):289-296,8.基金项目
国家自然科学基金项目(82360537) （82360537）
广西自然科学基金项目(2025GXNSFBA069386) （2025GXNSFBA069386）
区域性高发肿瘤早期防治研究教育部重点实验室项目(GKE-ZZ202309) （GKE-ZZ202309）
广西医科大学附属肿瘤医院青年基金项目(2024-027) （2024-027）
