cbis-ddsm-r

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.


@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}
}