Clinically Robust Deep Learning for Contrast-Enhanced Mammography: Multicenter Evaluation Across Convolutional Neural...

Area of Science:

• Radiology and Medical Imaging
• Artificial Intelligence in Healthcare
• Biomedical Engineering

Background:

• Deep learning models for breast lesion classification in contrast-enhanced mammography (CEM) face challenges with robustness and reliability across different datasets.
• Anatomically constrained preprocessing and deep learning architecture selection are key factors influencing model performance.

Purpose of the Study:

• To investigate the impact of anatomically constrained preprocessing (breast-mask segmentation) versus original DICOM images on deep learning model performance for CEM breast lesion classification.
• To evaluate various deep learning architectures, including CNNs, attention-based networks, and Transformers, for their effectiveness in this task.

Main Methods:

• A retrospective multicenter study combined 300 patient CEM images with 1003 public images (total 1120 cases).
• Automatic breast segmentation was performed using the LIBRA framework to create breast-mask images.
• Eleven deep learning models were trained and evaluated on both original DICOM and breast-mask inputs, assessing performance metrics like AUROC and balanced accuracy.

Main Results:

• Models trained with breast-mask images consistently outperformed those trained on original DICOM images, with AUROC improvements of +0.06 to +0.21.
• ResNet50 with breast-mask input achieved the highest performance (AUROC=0.931), further improved after optimization (balanced accuracy=0.886).
• Classical CNNs performed comparably to or better than complex hybrid models when using anatomically focused preprocessing and optimization.

Conclusions:

• Anatomically constrained preprocessing via breast-mask segmentation significantly enhances the performance and stability of deep learning models for CEM breast lesion classification.
• Input data quality and training optimization are critical for achieving clinically relevant AI performance, often more so than architectural complexity.
• These findings support the development of more reliable AI-assisted decision support tools for CEM workflows.