| Objective To investigate the clinical value of different radiomics based on gray scale ultrasound imaging in differentiating malignant from benign breast masses. Methods A retrospective analysis was performed for the breast mass gray scale ultrasound imaging of 180 patients who had been confirmed by pathology in the People's Hospital of Taihe County Affiliated to Wannan Medical College from October 2018 to October 2020, and radiomics features were extracted from imaging. All masses were divided into training(n=126) and testing(n=54) groups in a ratio of 7:3 randomly. Then one-way analysis of variance and Lasso were used to select the most important features to bulid radiomics model with five methods. Each model performance and the optimal model performance was assessed with respect to discrimination by the receiver operating characteristic curve(ROC). Results A total of eightoptimal features were selected to develop radiomics model. For the training group, the performance of random forest and support vector machine model wasslightly higher than that of decision tree and logistic regression model, and the integrated algorithm model was the worst,but in the testing group, the performance of random forest and logistic regression model was the strongest. In the testing group,the accuracy, sensitivity, specificity, positive predictive value and negative predictive value of logistic regression model in differentiating malignant from benign breast masses were higher than those of random forest, which was 83.33%,91.70%,83.33%,85.71%,81.82% and 81.48%,83.33%,80.00%,76.92%,85.71% respectively. Conclusions Radiomics based on gray scale ultrasound imaging has high clinical value in differentiating malignant from benign breast masses,and logistic regression model has the strongest performance among the five models. |
