BreastDCEDL_ISPY2 - DCE MRI dataset 982 cases

BreastDCEDL_ISPY2 Dataset (n=982) The BreastDCEDL_ISPY2 dataset is a curated subset of the I-SPY2 trial, comprising 982 breast cancer cases with pre-treatment DCE-MRI scans. It includes: DCE-MRI sequences (3–12 time points per scan, typically 7) Derived maps and full 3D tumor segmentations Clinical annotations: pathological complete response (pCR), hormone receptor (HR) status, HER2 status, MammaPrint risk level, and age at screening Demo: https://github.com/naomifridman/BreastDCEDL/blob/main/ISPY2/BrestDCEDL_ISPY2_demo.ipynb Citation @article{fridman2026breastdcedl, author = {Fridman, N. and Solway, B. and Fridman, T. and others}, title = {{BreastDCEDL}: A standardized deep learning-ready breast {DCE-MRI} dataset of 2,070 patients}, journal = {Scientific Data}, year = {2026}, doi = {10.1038/s41597-026-06589-6}, url = {https://doi.org/10.1038/s41597-026-06589-6}} Dataset Specifications Total Patients: 982 Total Size: ~54 GB Image Format: NIfTI (.nii.gz) Clinical Centers: 22+ institutions File Organization BreastDCEDL_ISPY2/ ├── BreastDCEDL_ISPY2_metadata.csv # Clinical and demographic data ├── dce/ # DCE-MRI sequences │ ├── ACRIN-6698-102212/ │ │ ├── ACRIN-6698-102212_spy2_vis1_dce_aqc_0.nii.gz # Pre-contrast │ │ ├── ACRIN-6698-102212_spy2_vis1_dce_aqc_1.nii.gz # Post-contrast 1 │ │ ├── ACRIN-6698-102212_spy2_vis1_dce_aqc_2.nii.gz # Post-contrast 2 │ │ ├── ACRIN-6698-102212_spy2_vis1_dce_aqc_3.nii.gz # Post-contrast 3 │ │ ├── ACRIN-6698-102212_spy2_vis1_dce_aqc_4.nii.gz # Post-contrast 4 │ │ ├── ACRIN-6698-102212_spy2_vis1_dce_aqc_5.nii.gz # Post-contrast 5 │ │ └── ACRIN-6698-102212_spy2_vis1_dce_aqc_6.nii.gz # Post-contrast 6 │ ├── ACRIN-6698-103939/ │ │ └── ... (3-12 DCE time points, typically 7) │ └── ... (982 patient directories total) └── masks/ # Tumor segmentations ├── ACRIN-6698-102212_spy2_vis1_mask.nii.gz ├── ACRIN-6698-103939_spy2_vis1_mask.nii.gz └── ... (982 binary mask files) Data Components Example of all acquisitions for random 7 patients, whith all the acquisitions. Resources Demo: https://github.com/naomifridman/BreastDCEDL/blob/main/ISPY2/BrestDCEDL_ISPY2_zenodo_demo.ipynb Full Methodology: Fridman et al., 2025 - arXiv:2506.12190 https://arxiv.org/abs/2506.12190 Citation @article{fridman2025breastdcedl, title={BreastDCEDL: A Comprehensive Breast Cancer DCE-MRI Dataset and Transformer Implementation for Treatment Response Prediction}, author={Fridman, Naomi and Solway, Bubby and Fridman, Tomer and Barnea, Itamar and Goldstein, Anat}, journal={arXiv preprint arXiv:2506.12190}, year={2025}, doi={10.48550/arXiv.2506.12190} }