BreastDCEDL: A Breast Cancer DCE-MRI Dataset and Transformer Implementation for Response Prediction (Preprint)

BACKGROUND Breast cancer is a leading cause of cancer-related mortality worldwide, making accurate treatment response monitoring critical. Artificial intelligence offers unprecedented potential for analyzing complex medical imaging data, yet progress in breast cancer has been limited by the lack of accessible, standardized public datasets. OBJECTIVE To present BreastDCEDL, a comprehensive deep learning-ready dataset for breast Dynamic Contrast-Enhanced MRI (DCE-MRI), and demonstrate its capacity by developing the first transformer-based model for breast DCE-MRI response prediction. METHODS We curated a dataset comprising pretreatment 3D DCE-MRI scans from 2,070 breast cancer patients drawn from the I-SPY 1, I-SPY 2, and Duke cohorts, all sourced from The Cancer Imaging Archive (TCIA). Raw DICOM imaging data was converted into standardized 3D NIfTI volumes, preserving signal integrity. Unified tumor annotations and harmonized clinical metadata, including pathologic complete response (pCR), hormone receptor (HR), and HER2 status, were provided. We implemented a Vision Transformer (ViT) approach with predefined benchmark splits for reproducible research. RESULTS BreastDCEDL consolidates 2,070 patients with preserved original DICOM intensity values, spatial resolution, and imaging parameters in standardized 3D NIfTI format. Our transformer-based model achieved state-of-the-art pCR prediction performance with AUC 0.94 and accuracy 0.93 for HR+/HER2⁻ tumors, demonstrating the first successful application of transformer models to breast DCE-MRI data. CONCLUSIONS This comprehensive resource addresses the critical need for large-scale, standardized datasets, enabling the development of advanced models including transformer architectures that require substantial training data. BreastDCEDL provides a framework for reproducible research and accelerates progress in AI-assisted breast cancer care.