Breast cancer diagnosis from contrast-enhanced mammography using multi-feature fusion neural network

ObjectivesTo develop an end-to-end deep neural network for the classification of contrast-enhanced mammography (CEM) images to facilitate breast cancer diagnosis in the clinic.MethodsIn this retrospective mono-centric study, patients who underwent CEM examinations from January 2019 to August 2021 were enrolled. A multi-feature fusion network combining low-energy (LE) and dual-energy subtracted (DES) images and dual view, as well as bilateral information, was trained and tested using a large CEM dataset with a diversity of breast tumors for breast lesion classification. Its generalization performance was further evaluated on two external datasets. Results were reported using AUC, accuracy, sensitivity, and specificity.ResultsA total of 2496 patients (mean age, 53 years & PLUSMN; 12 (standard deviation)) were included and divided into a training set (1718), a validation set (255), and a testing set (523). The proposed CEM-based multi-feature fusion network achieved the best diagnosis performance with an AUC of 0.96 (95% confidence interval (CI): 0.95, 0.97), compared with the no-fusion model, the left-right fusion model, and the multi-feature fusion network with only LE image inputs. Our models reached an AUC of 0.90 (95% CI: 0.85, 0.94) on a full-field digital mammograph (FFDM) external dataset (86 patients), and an AUC of 0.92 (95% CI: 0.89, 0.95) on a CEM external dataset (193 patients).ConclusionThe developed multi-feature fusion neural network achieved high performance in CEM image classification and was able to facilitate CEM-based breast cancer diagnosis.Clinical relevance statementCompared with low-energy images, CEM images have greater sensitivity and similar specificity in malignant breast lesion detection. The multi-feature fusion neural network is a promising computer-aided diagnostic tool for the clinical diagnosis of breast cancer.Key Points & BULL; Deep convolutional neural networks have the potential to facilitate contrast-enhanced mammography-based breast cancer diagnosis.& BULL; The multi-feature fusion neural network reaches high accuracies in the classification of contrast-enhanced mammography images.& BULL; The developed model is a promising diagnostic tool to facilitate clinical breast cancer diagnosis.Key Points & BULL; Deep convolutional neural networks have the potential to facilitate contrast-enhanced mammography-based breast cancer diagnosis.& BULL; The multi-feature fusion neural network reaches high accuracies in the classification of contrast-enhanced mammography images.& BULL; The developed model is a promising diagnostic tool to facilitate clinical breast cancer diagnosis.Key Points & BULL; Deep convolutional neural networks have the potential to facilitate contrast-enhanced mammography-based breast cancer diagnosis.& BULL; The multi-feature fusion neural network reaches high accuracies in the classification of contrast-enhanced mammography images.& BULL; The developed model is a promising diagnostic tool to facilitate clinical breast cancer diagnosis.