---
license: gpl-3.0
task_categories:
- image-classification
language:
- en
tags:
- radiomics
- bioniformatics
- cancer
- breast-cancer
- medical
pretty_name: CBIS-DDSM-R
---

# CBIS-DDSM-R: A Curated Radiomic Feature Dataset for Breast Cancer Classification

## Dataset Summary
CBIS-DDSM-R is an open-source, radiomics-ready extension of the Curated Breast Imaging Subset of the Digital Database for Screening Mammography (CBIS-DDSM). It is designed to facilitate reproducible radiomics and quantitative imaging research in breast cancer analysis.
The dataset provides a standardized preprocessing pipeline for mammograms and includes IBSI-compliant radiomics features extracted using PyRadiomics. Clinical metadata and radiomics features are combined into a unified, machine-readable format, making CBIS-DDSM-R a robust benchmark for developing and validating radiomics-based breast cancer models.

## Key Features
- Standardized mammogram preprocessing pipeline
- 93 radiomics features per lesion, extracted with PyRadiomics
- Full compliance with Image Biomarker Standardisation Initiative (IBSI) guidelines
- Unified dataset combining clinical metadata and radiomics features
- Designed for reproducibility and benchmarking in breast cancer radiomics
## Supported Tasks
- Breast cancer characterization
- Radiomics-based risk assessment
- Feature selection and reproducibility studies
- Benchmarking CAD and machine learning models
## Dataset Structure
The dataset is organized to support straightforward machine learning workflows:
- Radiomics features stored in tabular format
- Clinical and annotation metadata aligned at the lesion level
- Clear identifiers linking features to mammographic views and cases
## Intended Use
CBIS-DDSM-R is intended for research and educational purposes, particularly for:
- Radiomics and quantitative imaging studies
- Development and validation of machine learning models
- Reproducible research in medical imaging
## Citation
If you use this dataset, please cite the following paper.
```bibtex

@Article{data10110179,
AUTHOR = {Sánchez-Femat, Erika and Galván-Tejada, Carlos E. and Galván-Tejada, Jorge I. and Gamboa-Rosales, Hamurabi and Luna-García, Huizilopoztli and Flores-Chaires, Luis Alberto and Saldívar-Pérez, Javier and Reveles-Martínez, Rafael and Celaya-Padilla, José M.},
TITLE = {CBIS-DDSM-R: A Curated Radiomic Feature Dataset for Breast Cancer Classification},
JOURNAL = {Data},
VOLUME = {10},
YEAR = {2025},
NUMBER = {11},
ARTICLE-NUMBER = {179},
URL = {https://www.mdpi.com/2306-5729/10/11/179},
ISSN = {2306-5729},
ABSTRACT = {Early and accurate breast cancer detection is critical for patient outcomes. The Curated Breast Imaging Subset of the Digital Database for Screening Mammography (CBIS-DDSM) has been instrumental for computer-aided diagnosis (CAD) systems. However, the lack of a standardized preprocessing pipeline and consistent metadata has limited its utility for reproducible quantitative imaging or radiomics. This paper introduces CBIS-DDSM-R, an open-source, radiomics-ready extension of the original dataset. It provides an automated pipeline for preprocessing mammograms and extracts a standardized set of 93 radiomics features per lesion, adhering to Image Biomarker Standardisation Initiative (IBSI) guidelines using PyRadiomics. The resulting dataset combines clinical and radiomics data into a unified format, offering a robust benchmark for developing and validating reproducible radiomics models for breast cancer characterization.},
DOI = {10.3390/data10110179}
}
```