RadRepro CBCT: An Open-Access CBCT Phantom Dataset for Improved Standardization and Reproducibility of Radiomics Research

Radiomics, the extraction of quantitative features from medical images, has shown great potential in improving precision diagnosis, prognosis, and treatment planning. However, the reproducibility of radiomics features remains a major challenge due to the variability introduced by differences in imaging devices, acquisition protocols, and image reconstruction methods. This study introduces the first open-access cone-beam computed tomography (CBCT) phantom dataset specifically designed to test reproducibility in on-board imaging systems used in C-arm linear accelerators for radiotherapy. Using a widely recognized Catphan phantom, CBCT images were acquired from multiple devices across different imaging parameters, including variations in mAs, slice thickness, and reconstruction filters. The dataset includes 120 CBCT volumes with corresponding region of interest (ROI) segmentations and radiomics features enabling comprehensive testing of radiomics feature stability across intra- and inter-vendor comparisons. By providing this open-access dataset, the study aims to facilitate the standardization of CBCT radiomics research, improve feature reproducibility, and support the development of robust radiomics models for clinical applications.

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Journal
Scientific Data
Published
2026-02-14
DOI
https://doi.org/10.1038/s41597-026-06781-8
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.00

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article

RadRepro CBCT: An Open-Access CBCT Phantom Dataset for Improved Standardization and Reproducibility of Radiomics Research

Wolfgang Birkfellner, Martin Buschmann, Soraya Elmirad, Elisabeth Steiner et al.
Scientific Data
Radiomics and Machine Learning in Medical Imaging
article

RadRepro CBCT: An Open-Access CBCT Phantom Dataset for Improved Standardization and Reproducibility of Radiomics Research

Wolfgang Birkfellner, Martin Buschmann, Soraya Elmirad, Elisabeth Steiner, Arezoo Borji, Sepideh Hatamikia, Eashrat Jahan Muniya
article en

Abstract

Radiomics, the extraction of quantitative features from medical images, has shown great potential in improving precision diagnosis, prognosis, and treatment planning. However, the reproducibility of radiomics features remains a major challenge due to the variability introduced by differences in imaging devices, acquisition protocols, and image reconstruction methods. This study introduces the first open-access cone-beam computed tomography (CBCT) phantom dataset specifically designed to test reproducibility in on-board imaging systems used in C-arm linear accelerators for radiotherapy. Using a widely recognized Catphan phantom, CBCT images were acquired from multiple devices across different imaging parameters, including variations in mAs, slice thickness, and reconstruction filters. The dataset includes 120 CBCT volumes with corresponding region of interest (ROI) segmentations and radiomics features enabling comprehensive testing of radiomics feature stability across intra- and inter-vendor comparisons. By providing this open-access dataset, the study aims to facilitate the standardization of CBCT radiomics research, improve feature reproducibility, and support the development of robust radiomics models for clinical applications.

Scientific DataVol. 13(1)
Danube Private University (AT), Vienna General Hospital (AT), Fachhochschule Wiener Neustadt (AT), Austrian Center for Medical Innovation and Technology (AT), Medical University of Vienna (AT)
Austrian Center for Medical Innovation and Technology
Openalex Percentile: Top 56%
Radiomics and Machine Learning in Medical Imaging
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RadRepro CBCT: An Open-Access CBCT Phantom Dataset for Improved Standardization and Reproducibility of Radiomics Research — Wolfgang Birkfellner, Martin Buschmann, et al. · Scientific Data (2026) | TGRS Research Map | TGRS