Radiomics-based differentiation of benign and malignant breast masses on contrast-enhanced mammography: a reproducible workflow using open-source tools

A radiomics workflow based on recombined CEM images and open-source tools may complement conventional CEM interpretation, improve non-invasive lesion characterization, and support further research toward clinically validated decision-support applications.

Authors

Cemal Aydoğan (ORCID: https://orcid.org/0000-0002-0383-1120)
Sinan Erdemi (ORCID: https://orcid.org/0000-0001-6973-6655)
Ramazan Özgür Doğan (ORCID: https://orcid.org/0000-0001-6415-5755)
SİBEL KUL
Aykut Teymur
Onur Bektaş (ORCID: https://orcid.org/0000-0003-4761-7668)
Hayati Türe
Temel Kayıkçıoğlu

Institutions

Avrasya University (TR)
Karadeniz Technical University (TR)
State Hospital (GB)
Uni Research (Norway) (NO)
Antalya Eğitim ve Araştırma Hastanesi (TR)
Sakarya Eğitim ve Araştırma Hastanesi (TR)
Sağlık Bilimleri Üniversitesi (TR)
Antalya IVF (TR)
University of Health Sciences Antigua (AG)

Publication Details

Journal: Diagnostic and Interventional Radiology
Published: 2026-02-12
DOI: https://doi.org/10.4274/dir.2026.263222
Primary Topic: Radiomics and Machine Learning in Medical Imaging
Type: article
Field-Weighted Citation Impact: 0.00

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article

Radiomics-based differentiation of benign and malignant breast masses on contrast-enhanced mammography: a reproducible workflow using open-source tools

Cemal Aydoğan, Sinan Erdemi, Ramazan Özgür Doğan, SİBEL KUL et al.

Feb 12, 2026

Diagnostic and Interventional Radiology

Radiomics and Machine Learning in Medical Imaging

article

Radiomics-based differentiation of benign and malignant breast masses on contrast-enhanced mammography: a reproducible workflow using open-source tools

Cemal Aydoğan, Sinan Erdemi, Ramazan Özgür Doğan, SİBEL KUL, Aykut Teymur, Onur Bektaş, Hayati Türe, Temel Kayıkçıoğlu

February 12, 2026article en

Abstract

A radiomics workflow based on recombined CEM images and open-source tools may complement conventional CEM interpretation, improve non-invasive lesion characterization, and support further research toward clinically validated decision-support applications.

Diagnostic and Interventional Radiology

Avrasya University (TR), Karadeniz Technical University (TR), State Hospital (GB), Uni Research (Norway) (NO), Antalya Eğitim ve Araştırma Hastanesi (TR), Sakarya Eğitim ve Araştırma Hastanesi (TR), Sağlık Bilimleri Üniversitesi (TR), Antalya IVF (TR), University of Health Sciences Antigua (AG)

Gender equality

Openalex Percentile: Top 58%

Radiomics and Machine Learning in Medical Imaging

DOI Open Access PDF OpenAlex

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Radiomics-based differentiation of benign and malignant breast masses on contrast-enhanced mammography: a reproducible workflow using open-source tools — Cemal Aydoğan, Sinan Erdemi, et al. · Diagnostic and Interventional Radiology (2026) | TGRS Research Map | TGRS