Reproducibility of CT Radiomic Features within the Same Patient: Influence of Radiation Dose and CT Reconstruction Settings
Open accessAbstract
Background Results of recent phantom studies show that variation in CT acquisition parameters and reconstruction techniques may make radiomic features largely nonreproduceable and of limited use for prognostic clinical studies. Purpose To investigate the effect of CT radiation dose and reconstruction settings on the reproducibility of radiomic features, as well as to identify correction factors for mitigating these sources of variability. Materials and Methods This was a secondary analysis of a prospective study of metastatic liver lesions in patients who underwent staging with single-energy dual-source contrast material–enhanced staging CT between September 2011 and April 2012. Technique parameters were altered, resulting in 28 CT data sets per patient that included different dose levels, section thicknesses, kernels, and reconstruction algorithm settings. By using a training data set (n = 76), reproducible intensity, shape, and texture radiomic features (reproducibility threshold, R2 ≥ 0.95) were selected and correction factors were calculated by using a linear model to convert each radiomic feature to its estimated value in a reference technique. By using a test data set (n = 75), the reproducibility of hierarchical clustering based on 106 radiomic features measured with different CT techniques was assessed. Results Data in 78 patients (mean age, 60 years ± 10; 33 women) with 151 liver lesions were included. The percentage of radiomic features deemed reproducible for any variation of the different technical parameters was 11% (12 of 106). Of all technical parameters, reconstructed section thickness had the largest impact on the reproducibility of radiomic features (12.3% [13 of 106]) if only one technical parameter was changed while all other technical parameters were kept constant. The results of the hierarchical cluster analysis showed improved clustering reproducibility when reproducible radiomic features with dedicated correction factors were used (ρ = 0.39–0.71 vs ρ = 0.14–0.47). Conclusion Most radiomic features are highly affected by CT acquisition and reconstruction settings, to the point of being nonreproducible. Selecting reproducible radiomic features along with study-specific correction factors offers improved clustering reproducibility. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Sosna in this issue.
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Radiomic features are highly sensitive to CT acquisition and reconstruction parameters; features identified as strongly predictive in one scanner environment or protocol may not generalise to another.
“However, radiomic features are highly sensitive to CT acquisition and reconstruction parameters; features identified as strongly predictive in one scanner environment or protocol may not generalise to another.3839”
From Prognostication and clinical opportunities with AI for coronary artery calcium: a scoping review (2026) · cites this paper as [38]
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Clinical Artificial Intelligence Applications in Radiology
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Assessing radiomics feature stability with simulated CT acquisitions
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Machine Learning in Lung Cancer Radiomics
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Spectral CT: Current Liver Applications
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- Authors
- Meyer, M., Ronald, J., Vernuccio, F., Nelson, R. C., Ramirez-Giraldo, J. C., Solomon, J., Patel, B. N., Samei, E., & Marin, D.
- Affiliations
- Heidelberg University (DE)
- ORCID
- Venue
- Radiology
- Published
- 2019-12
- Volume
- 293
- Issue
- 3
- Pages
- 583-591
- ISSN
- 0033-8419 (ISSN-L)
- 1527-1315
- PMC
- PMC10860741
- OpenAlex
- W2977270522
- Cited by
- 258
- References
- 25
- Citations by year
- 2026: 25 · 2025: 23 · 2024: 32 · 2023: 44 · 2022: 51 · 2021: 57 · 2020: 23 · 2019: 2
- FWCI
- 21.98
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Radiology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Nova Science Publishers (Nova Science Publishers, Inc.) (submittedVersion)
- Funders
- Siemens Healthineers
- National Institute of Biomedical Imaging and Bioengineering (R01EB001838)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiation Dose and Imaging
- Keywords
- CT radiomics, reproducibility, radiation dose, section thickness, kernel, reconstruction algorithms, correction factors, metastatic liver lesions, hierarchical clustering
- MeSH terms
- Algorithms, Contrast Media, Female, Humans, Liver Neoplasms, Male, Middle Aged, Neoplasm Staging, Prospective Studies, Radiation Dosage, Radiographic Image Interpretation, Computer-Assisted, Tomography, X-Ray Computed, Reproducibility of Results
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