  Background \& purpose: The recent emergence of neural networks models for the
analysis of breast images has been a breakthrough in computer aided diagnostic.
This approach was not yet developed in Contrast Enhanced Spectral Mammography
(CESM) where access to large databases is complex. This work proposes a
deep-learning-based Computer Aided Diagnostic development for CESM recombined
images able to detect lesions and classify cases. Material \& methods: A large
CESM diagnostic dataset with biopsy-proven lesions was collected from various
hospitals and different acquisition systems. The annotated data were split on a
patient level for the training (55%), validation (15%) and test (30%) of a deep
neural network with a state-of-the-art detection architecture. Free Receiver
Operating Characteristic (FROC) was used to evaluate the model for the
detection of 1) all lesions, 2) biopsied lesions and 3) malignant lesions. ROC
curve was used to evaluate breast cancer classification. The metrics were
finally compared to clinical results. Results: For the evaluation of the
malignant lesion detection, at high sensitivity (Se>0.95), the false positive
rate was at 0.61 per image. For the classification of malignant cases, the
model reached an Area Under the Curve (AUC) in the range of clinical CESM
diagnostic results. Conclusion: This CAD is the first development of a lesion
detection and classification model for CESM images. Trained on a large dataset,
it has the potential to be used for helping the management of biopsy decision
and for helping the radiologist detecting complex lesions that could modify the
clinical treatment.
