Clinical-MRI radiomics combined model for differentiating borderline ovarian tumor from epithelial ovarian cancer
10.13929/j.issn.1003-3289.2025.10.021
- VernacularTitle:临床-MRI影像组学联合模型鉴别交界性卵巢肿瘤与上皮性卵巢癌
- Author:
Xiaomin LIU
1
;
Yu ZOU
;
Jingjing YU
;
Xiaochen WANG
;
Yuhan LIN
;
Jiale QIN
Author Information
1. 浙江大学医学院附属妇产科医院放射科,浙江 杭州 310006
- Publication Type:Journal Article
- Keywords:
ovarian neoplasms;
diagnosis,differential;
magnetic resonance imaging;
radiomics
- From:
Chinese Journal of Medical Imaging Technology
2025;41(10):1701-1705
- CountryChina
- Language:Chinese
-
Abstract:
Objective To explore the value of clinical-MRI radiomics combined model for differentiating borderline ovarian tumor(BOT)from epithelial ovarian cancer(EOC).Methods Totally 139 patients with BOT(BOT group)and 307 patients with EOC(EOC group)confirmed by postoperative pathology and underwent preoperative pelvic MRI were retrospectively enrolled and randomly divided into training set(n=312)and test set(n=134)at a ratio of 7∶3.Multivariable logistic regression was used to identify independent clinical predictors for differentiating BOT and EOC,then a clinical model was constructed.Radiomics features were extracted from the volumes of interest(VOI)of lesions on T2WI,diffusion weighted imaging(DWI)and apparent diffusion coefficient(ADC)images,respectively,and single-sequence and multi-sequence MRI radiomics models were built using extreme gradient boosting(XGBoost)based on data in training set.The optimal MRI radiomics model was selected according to the highest area under the curve(AUC)in test set,and a clinical-MRI radiomics combined model was constructed combined the optimal radiomics model with independent clinical predictors.The performances of clinical model,the optimal MRI radiomics model and the combined model for differentiating BOT and EOC were compared in test set.SHapley Additive exPlanations(SHAP)analysis was applied to interpret key predictive features in the best model.Results Patients' age,carbohydrate antigen 153(CA153)and carbohydrate antigen 125(CA125)were all independent predictors for differentiating BOT and EOC(all P＜0.05).Multi-sequence MRI radiomics model was the optimal MRI radiomics model.The combined model showed superior performance(AUC=0.929)for discriminating BOT and EOC compared with clinical model(AUC=0.881)and multi-sequence MRI radiomics model(AUC=0.871)(both P＜0.05).SHAP beeswarm plot revealed that the top 10 important features of combined model included age,CA153 and CA125,as well as entropy,kurtosis and gray level non-uniformity from ADC and DWI sequences.Conclusion Clinical-MRI radiomics combined model based on multi-sequence MRI radiomics features and clinical features could be used to effectively differentiate BOT from EOC.
