关键词:
Deep learning
Ultrasonography
Breast diseases
Diagnosis
摘要:
Background:The current deep learning diagnosis of breast masses is mainly reflected by the diagnosis of benign and malignant *** China,breast masses are divided into four categories according to the treatment method:inflammatory masses,adenosis,benign tumors,and malignant *** categorizations are important for guiding clinical *** this study,we aimed to develop a convolutional neural network(CNN)for classification of these four breast mass types using ultrasound(US)***:Taking breast biopsy or pathological examinations as the reference standard,CNNs were used to establish models for the four-way classification of 3623 breast cancer patients from 13 *** patients were randomly divided into training and test groups(n=1810 vs.n=1813).Separate models were created for two-dimensional(2D)images only,2D and color Doppler flow imaging(2D-CDFI),and 2D-CDFI and pulsed wave Doppler(2D-CDFI-PW)*** performance of these three models was compared using sensitivity,specificity,area under receiver operating characteristic curve(AUC),positive(PPV)and negative predictive values(NPV),positive(LR+)and negative likelihood ratios(LR-),and the performance of the 2D model was further compared between masses of different sizes with above statistical indicators,between images from different hospitals with AUC,and with the performance of 37 ***:The accuracies of the 2D,2D-CDFI,and 2D-CDFI-PW models on the test set were 87.9%,89.2%,and 88.7%,*** AUCs for classification of benign tumors,malignant tumors,inflammatory masses,and adenosis were 0.90,0.91,0.90,and 0.89,respectively(95%confidence intervals[CIs],0.87-0.91,0.89-0.92,0.87-0.91,and 0.86-0.90).The 2D-CDFI model showed better accuracy(89.2%)on the test set than the 2D(87.9%)and 2D-CDFI-PW(88.7%)*** 2D model showed accuracy of 81.7%on breast masses≤1 cm and 82.3%on breast masses>1 cm;there was a significant difference between the two groups(P<0.001).The accu