Predictive Deep Learning Model based on Contrast-Enhanced Mammography for Breast Cancer Diagnosis: A Pilot Study

Objectives The interpretation of contrast-enhanced mammography (CEM) images heavily depends on radiologists' expertise, highlighting the need for automated tools to assist clinical decision-making.This pilot study aimed to develop a deep learning model using CEM images to predict the histological diagnosis of breast cancer. Methods We retrospectively analyzed patients who underwent contrast-enhanced mammography (CEM) followed by histopathological assessment (October 2022-May 2023) across two centers. CEM images from center 1 were used for model development, including training and 10-fold cross-validation, while images from center 2 served as an independent external test set. The breast region was manually segmented, and two deep learning architectures were implemented as ensemble classifiers, using biopsy results as reference standard. Performance metrics included accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the ROC curve (AUC). Results A total of 106 CEM images were retrospectively analyzed (50 from center 1 and 56 from center 2), obtained using different mammography systems. Histopathology identified 51 lesions (48%) as malignant and 55 (52%) as benign. Cross-validation yielded a ROC-AUC of 75% [58.4–91.6], accuracy of 68.7% [58.3– 79], sensitivity of 68.9% [52.2–85.6], specificity of 67.8% [65.4–70.2], PPV of 69.3% [55.5–83.1], and NPV of 73.5% [55.2–91.8] (p<0.05). External testing on images from center 2 (27 malignant, 29 benign) achieved an accuracy of 64.3%. Conclusions The study showed promising performance but requires further development and dataset expansion. The model has the potential to be integrated into clinical practice.