FPN-SENet-FL deep convolutional neural network model for differentiating benign and malignant breast tumors and predicting molecular subtypes of breast cancers based on ultrasonic images
Objective To explore the feasibility of FPN-SENet-FL deep convolutional neural network models for differentiating benign and malignant breast tumors and predicting molecular subtypes of breast cancers based on ultrasonic images.Methods Totally 480 preoperative ultrasonic images of 273 breast cancer patients and 113 preoperative ultrasonic images of 41 benign breast tumor patients were retrospectively analyzed.The ultrasonic image dataset was constructed,and the images were randomly divided into the training set or validation set at the ratio of 7∶3.Data augmentations were applied in the training set,based on which a binary task model and a quinary task model were trained,respectively.The diagnostic performance of the binary task model in differentiating benign breast tumors from malignant ones and the quinary task model in identifying benign breast tumors and different molecular subtypes of breast cancers were evaluated with the receiver operating characteristic curve and the area under the curve(AUC),and the confusion matrix,as well as the accuracy,precision,recall rate and F1-score of the tasks were calculated.Results The accuracy,precision,recall rate and F1-score of binary task model was 94.71％,91.32％,91.30％,and 0.913,respectively,with AUC of 0.976,of quinary task model was 71.78％,72.48％,72.11％and 0.721,respectively,with AUC of 0.860 to 0.976,and the highest AUC(0.976)was noticed in differentiating benign breast tumors from malignant ones,followed(0.944)in differentiating Luminal B breast cancers from others.Conclusion FPN-SENet-FL deep convolutional neural network model might assist ultrasonic differentiation of benign and malignant breast tumors,with high efficacy for predicting Luminal B breast cancers.
deep learningbreast neoplasmsimmunophenotypingultrasonography
万方数据
维普
