Nomogram based on automatic breast volume scanner radiomics combined with clinical and ultrasonic features for differentiating benign or malignant breast intraductal lesions
Objective To observe the value of nomogram based on automated breast volume scanner(ABVS)radiomics combined with clinical and ultrasonic features for differentiating benign or malignant breast intraductal lesions.Methods Clinical and ultrasonic data of 144 female patients with pathologically confirmed breast intraductal lesions were retrospectively analyzed.The patients were randomly divided into training set(n=96)or validation set(n=48)at the ratio of 2∶1.The optimal radiomics features were extracted and screened based on ABVS images,then radiomics model was constructed,and Radscore was calculated.Univariate and multivariate logistic regression analysis of clinical,ultrasonic features and Radscores were performed to screen the independent impact factors of benign or malignant breast intraductal lesions,and clinic-ultrasound model was established.Nomogram model was constructed by combining the clinic-ultrasound model with radiomics.Receiver operating characteristic(ROC)curve was used to evaluate the efficacy of each model for differentiating benign or malignant breast intraductal lesions.Results Patients'age(OR[95％CI]=1.104[1.045,1.180],P=0.001),lesion's margin(OR[95％CI]=0.273[0.075,0.917],P=0.039),microcalcification(OR[95％CI]=9.759[2.240,60.730],P=0.006)and Radscore(OR[95％CI]=3.818[1.435,11.994],P=0.012)were all independent impact factors for benign or malignant intraductal lesions.The area under the curve(AUC)of radiomics model,clinic-ultrasound model and nomogram model for differentiating benign or malignant breast intraductal lesions was 0.766,0.866 and 0.901 in training set,while 0.770,0.765 and 0.854 in validation set,respectively.Conclusion Nomogram based on ABVS radiomics combined with clinical and ultrasonic features had good efficacy for differentiating benign or malignant breast intraductal lesions.
breast neoplasmsultrasonographybreast imaging reporting and data systemradiomics
万方数据
维普
