Evidence map›Paper›PMID 41690976›Full record

ArticleScientific data2026

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

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

Abstract read
In one paragraph

Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Sepideh HatamikiaClinical AI-Research in Omics and Medical Data Science (CAROM) group, Department of Medicine, Danube Private University (DPU), Krems, Austria. sepideh.hatamikia@dp-uni.ac.at.ORCID http://orcid.org/0000-0002-0182-0954
Elisabeth SteinerClinical Institute for Radiation Oncology and Radiotherapy, University Hospital Wiener Neustadt, Wiener Neustadt, Austria.
Eashrat Jahan MuniyaAustrain Center for Medical Innovation and Technology (ACMIT), Wiener Neustadt, Austria.
Soraya ElmiradClinical Institute for Radiation Oncology and Radiotherapy, University Hospital Wiener Neustadt, Wiener Neustadt, Austria.
Arezoo BorjiClinical AI-Research in Omics and Medical Data Science (CAROM) group, Department of Medicine, Danube Private University (DPU), Krems, Austria.
Gernot KronreifAustrain Center for Medical Innovation and Technology (ACMIT), Wiener Neustadt, Austria.
Wolfgang BirkfellnerCenter for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria.
Martin BuschmannDepartment of Radiation Oncology, Medical University of Vienna and University Hospital Vienna, Vienna, Austria.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Cone-Beam Computed TomographyPhantoms, ImagingRadiomicsDatasets as TopicHumansImage Processing, Computer-AssistedReproducibility of Results

Identifiers

PMID41690976
PMCPMC13018616

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.