岭南现代临床外科2025，Vol.25Issue(6)：367-374,8.DOI:10.3969/j.issn.1009-976X.2025.06.004
构建多参数MRI影像组学模型预测肝细胞癌组织分化程度
A multiparametric MRI-based radiomics model for the preoperative prediction of histological dif-ferentiation in hepatocellular carcinoma
摘要
Abstract
Objective To develop a multiparametric MRI-based radiomics model for the preoperative noninvasive prediction of histological differentiation in hepatocellular carcinoma(HCC).Methods A total of 206 patients with pathologically confirmed HCC who underwent surgical resection at Sun Yat-sen Memorial Hospital between September 2022 and December 2024 were retrospectively enrolled.Patients were classified into high-grade(n=57)and low-grade(n=149)groups based on the Edmondson-Steiner grading system and were then randomly assigned to training(n=144)and validation(n=62)cohorts at a ratio of 7∶3.Clinical predictors were identified through univariate and multivariate logistic regression analysis.Radiomics features were extracted from multiparametric MRI images and selected by applying univariate logistic regression analysis,maximum relevance minimum redundancy(mRMR),and least absolute shrinkage and selection operator(LASSO)algorithms.Predictive models were developed using logistic regression.Model performance was evaluated by the receiver operating characteristic(ROC)curves and area under the curve(AUC).The DeLong test was employed to compare AUC values.Calibra-tion curves and the Hosmer-Lemeshow test were used to evaluate the model's calibration.Results The combined radiomics model(Comb_Rad)achieved AUCs of 0.904 and 0.886 in the training and validation cohorts,significantly outperformed all single-parameter radiomics models in the validation cohort(all P<0.05,DeLong test).The radiomics-clinical combined model(Rad_Clin)achieved the best performance,with AUCs of 0.933 in the training cohort and 0.902 in the validation cohort.The Rad_Clin model demon-strated significantly better performance than the Comb_Rad model in the training cohort(P=0.047),while both models significantly outperformed the clinical model(Clin)in both cohorts(all P<0.05).Additionally,the Rad_Clin model demonstrated good fit(training cohort P=0.905,test cohort P=0.853).Conclusion A multiparametric MRI-based radiomics model can effectively predict the histological differentiation of HCC,and the integration of clinical factors further improves predictive performance.关键词
肝细胞癌/分化程度/磁共振成像/影像组学Key words
hepatocellular carcinoma/differentiation/magnetic resonance imaging/radiomics分类
医药卫生引用本文复制引用
曾佳乐,奉鑫,徐啟业,王智慧..构建多参数MRI影像组学模型预测肝细胞癌组织分化程度[J].岭南现代临床外科,2025,25(6):367-374,8.基金项目
广东省医学科学技术研究基金项目(A2023104) （A2023104）
