Objective To explore the value of imaging models with different machine learning methods in predicting benign and malignant breast lesions.Methods The clinical and imaging data of 271 patients confirmed by histopathology in the second affiliated Hospital of Xiamen Medical College from August 2018 to May 2022 were analyzed retrospectively.The stratified sampling method was used to divide the training group and the verification group at the proportion of 7:3.The third phase of dynamic contrast enhanced MRI(DCE-MRI)was used to extract imaging features.The training group uses redundancy analysis,minimum absolute contraction and selection operator cross-validation algorithm for feature screening.Four different machine learning methods with supervised learning,logical regression,support vector machine,adaptive enhancement algorithm and decision tree,are used to predict benign and malignant breast lesions.The receiver operating characteristic curve(ROC),accuracy and F1 measure were used to evaluate the advantages and disadvantages of the four machine algorithms,and verified by the verification group.The calibration curve is used to evaluate the deviation between the predicted probability and the actual probability.Results Based on 17 imaging features,the prediction effect of logical regression algorithm is the best in distinguishing benign and malignant breast lesions.The highest area under the curve(AUC value)is 0.832(0.744-0.919)in the verification group,and the accuracy is 78％.The F1 measure is 0.790.Finally,the prediction model is established by logical regression machine learning algorithm,and the calibration curve of logical regression algorithm model has good overlap.Conclusion Logical regression machine learning based on breast dynamic contrast enhanced MR imaging model is helpful to distinguish benign and malignant breast lesions and provide guidance for clinicians to make decisions.
维普
万方数据