实用医学杂志2026，Vol.42Issue(7)：1192-1200,9.DOI:10.3969/j.issn.1006-5725.2026.07.011
基于超声影像组学、O-RADS分类与临床因素的卵巢肿瘤诊断模型
Developing an ovarian cancer diagnostic model from ultrasound radiomics,O-RADS classification,and clinical factors
摘要
Abstract
Objective This study aims to integrate ultrasound radiomics,the O-RADS(v2022)classifica-tion system,and clinical risk factors to develop and validate an intelligent diagnostic model for improving the accu-racy of differentiating between benign and malignant ovarian tumors.Methods A multicenter,retrospective study design was adopted.A Lotal of 596 patients who underwent surgery at our institution were enrolled and randomly split into a training set(n=418)and an internal validation set(n=178)at a 7∶3 ratio.Additionally,110 pa-tients from an external hospital were recruited as an external test set.Model construction consisted of three core components:(1)extraction of 12 radiomics features from standardized ultrasound images;(2)O-RADS classifica-tion results derived from blinded assessments and consensus among three physicians;(3)clinical predictors(age,maximum tumor diameter,CA125,HE4,and menopausal status)identified through univariate and multivariate lo-gistic regression screening.Three models were developed and compared:a standalone O-RADS model,a combined clinical-O-RADS model,and an integrated radiomics-clinical-O-RADS model.Results The integrated model ex-hibited the optimal diagnostic performance,with area under the curve(AUC)values of 0.95 in the training set,0.92 in the internal validation set,and 0.89 in the external test set(P＜0.05).Decision curve analysis(DCA)fur-ther confirmed that this model achieved a higher clinical net benefit across a wide range of threshold probabilities.Feature importance analysis revealed that radiomics features contributed the most to the model's predictive power(approximately 60％).Conclusions The integrated model combining ultrasound radiomics,O-RADS classifica-tion,and clinical factors significantly improves the preoperative diagnostic accuracy for distinguishing between be-nign and malignant ovarian lesions.It demonstrates good generalization ability and clinical utility,providing an ob-jective and precise auxiliary tool to support clinical decision-making in ovarian tumor management.关键词
卵巢肿瘤/超声诊断/影像组学/O-RADS分类/诊断模型Key words
ovarian cancer/ultrasound diagnosis/radiomics/O-RADS classification system/di-agnostic model分类
医药卫生引用本文复制引用
尹晶,张平洋,汪珺莉,尹薇薇,储小爱,赵文燕..基于超声影像组学、O-RADS分类与临床因素的卵巢肿瘤诊断模型[J].实用医学杂志,2026,42(7):1192-1200,9.基金项目
江苏省卫生健康委科研项目(编号:ZD2021048) （编号:ZD2021048）
皖南医学院校级科研项目(编号:WK2023JXYY133) （编号:WK2023JXYY133）
