乳腺X线及MRI特征联合临床病理预测乳腺导管原位癌伴微浸润OA北大核心CSTPCD

Objective:To explore the value of clinical-pathological,mammographic(MG),and MRI features in predicting ductal carcinoma in situ with microinvasion(DCISM).Materials and Methods:A retrospective study was conducted on female patients diagnosed with pure ductal carcinoma in situ(DCIS)and DCISM confirmed by final surgical pathology from June 2019 to June 2022 at General Hospital of Ningxia Medical University.Clinical-pathological,MG,and MRI features of the patients were evaluated.The univariate and multivariate logistic regression analysis was used to identify independent risk factors for DCISM and develop a combined model.The diagnostic performance of the model was assessed using the area under the receiver operating characteristic(ROC)curve(AUC)and calibration plot.The clinical utility of the combined model was evaluated using decision curve analysis(DCA).A prospective validation was performed on patients who meet the eligibility criteria for inclusion and exclusion from July 2022 to July 2023.Shapley Additive exPlanation(SHAP)analysis was applied to assess the value of the combined model in predicting DCISM based on the longest diameter of the lesion,nuclear grade,necrosis,Ki-67 index,P63 status,calcification status,and minimum ADC value.A total of 535 patients with 550 lesions(15 cases were synchronous bilateral breast cancer)were collected.The patients'ages ranged from 23 to 81 years,with a median age of 50 years.Among the training group(n=382),102 lesions(27%)were diagnosed as DCISM,while in the validation group(n=168),52 lesions(31%)were diagnosed as DCISM.Results:The multivariable logistic regression analysis showed the independent risk factors of DCISM included longest diameter of the lesion,nuclear grade,necrosis,Ki-67 index,P63 status,calcification status,and the minimum value of apparent diffusion coefficient(ADCmin).A predictive model combining the above parameters with preoperative clinical-pathological,mammography,and MRI features was constructed,demonstrating high predictive performance in both the training and validation groups(AUC:0.937,0.899).According to SHAP analysis,the longest diameter of the lesion,Ki-67 index,and ADCmin make the primary contributions in the combined model for predicting DCISM,while the calcification status,nuclear grade,P63 status,and necrosis are supplementary factors.Conclusions:A combined predictive model using clinical-pathological,preoperative MG and MRI features can effectively differentiate DCISM from pure DCIS,thereby improving the accuracy of clinical decision-making and treatment planning.