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

Erika Sánchez-Femat, Carlos E. Galván-Tejada, Jorge I. Galván-Tejada, Hamurabi Gamboa-Rosales, Huizilopoztli Luna-García, Luis Alberto Flores-Chaires, Javier Saldívar-Pérez, Rafael Reveles-Martínez () and José M. Celaya-Padilla ()
Additional contact information
Erika Sánchez-Femat: Unidad de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Zacatecas 98160, Mexico
Carlos E. Galván-Tejada: Unidad de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Zacatecas 98160, Mexico
Jorge I. Galván-Tejada: Unidad de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Zacatecas 98160, Mexico
Hamurabi Gamboa-Rosales: Unidad de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Zacatecas 98160, Mexico
Huizilopoztli Luna-García: Unidad de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Zacatecas 98160, Mexico
Luis Alberto Flores-Chaires: Unidad de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Zacatecas 98160, Mexico
Javier Saldívar-Pérez: Unidad de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Zacatecas 98160, Mexico
Rafael Reveles-Martínez: Unidad de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Zacatecas 98160, Mexico
José M. Celaya-Padilla: Unidad de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Zacatecas 98160, Mexico

Data, 2025, vol. 10, issue 11, 1-13

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.

Keywords: CBIS-DDSM; breast cancer; mammography; radiomics; Medical Imaging Dataset; lesion segmentation; deep learning (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2306-5729/10/11/179/pdf (application/pdf)
https://www.mdpi.com/2306-5729/10/11/179/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jdataj:v:10:y:2025:i:11:p:179-:d:1787088

Access Statistics for this article

Data is currently edited by Ms. Becky Zhang

More articles in Data from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().