BreastDCEDL: A standardized deep learning-ready breast DCE-MRI dataset of 2,070 patients.
Authors
Affiliations (3)
Affiliations (3)
- Department of Industrial Engineering, Ariel University, Ariel, Israel. [email protected].
- NF Algorithms & AI, Tel Aviv, Israel.
- Department of Industrial Engineering, Ariel University, Ariel, Israel.
Abstract
Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) is essential for monitoring breast cancer treatment response, yet deep learning progress is limited by the lack of standardized, multi-center datasets. We present BreastDCEDL, a deep learning-ready dataset comprising pretreatment 3D DCE-MRI scans from 2,070 patients across three cohorts: I-SPY1 (n = 172), I-SPY2 (n = 982), and Duke (n = 916). Raw DICOM files were converted to standardized 3D NIfTI format, preserving signal integrity and spatial resolution. The dataset includes unified tumor annotations-binary masks for I-SPY cohorts and bounding boxes for Duke-and harmonized clinical metadata. Pathologic complete response (pCR) data are available for 1,452 patients (100% of I-SPY1/I-SPY2, 32.5% of Duke). Clinical biomarkers include HR status (64.1% positive) and HER2 status (22.2% positive) for over 99% of patients. Predefined train-validation-test splits (1,532/268/270) maintain balanced distributions. BreastDCEDL fills a critical gap in public imaging resources, supporting development of advanced models for pCR prediction and other breast cancer analyses. The dataset is designed for broad reuse and is accompanied by processing tools to support community-driven AI research in oncology.