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OA

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.