CoChat
Sign Up
Cited by 1Radiomics and Machine Learning in Medical ImagingMedical Imaging Techniques and ApplicationsImage Retrieval and Classification Techniques

Influence of scan mode, tilt, and radiation dose on CT radiomic metrics

Open access
Yadav, Neha, Lei, Xiaomeng, Cen, Steven Y., Levy, Joshua, Jensen, Kristin, Varghese, Bino A.·2026·Journal of Applied Clinical Medical Physics
DOI 10.1002/acm2.70462PMC PMC12800920
Open this paper in CoChat — free accountOpen PDF

Abstract

Background: Radiomic features derived from computed tomography (CT) are highly susceptible to variations in acquisition parameters, which can introduce confounding effects in multicenter research and reduce diagnostic accuracy. While the effects of parameters such as scanning mode and dose have been studied, the impact of gantry tilt—despite its routine clinical use—remains underexplored in radiomics literature. Purpose: To systematically evaluate how scan mode (axial vs. helical), gantry tilt (0° vs. 5°), and radiation dose affect CT‐based radiomic metrics using an anthropomorphic liver phantom containing six 3D‐printed texture inserts, with special emphasis on the novel inclusion of tilt. Methods: Twelve unique image acquisition configurations were scanned on a GE Revolution Apex CT scanner, with each configuration repeated once. Manual segmentation of volumes of interest (VOIs) was performed, and 93 radiomic features spanning six texture families were extracted using PyRadiomics. First‐order dispersion metrics (standard deviation, interquartile range, and coefficient of variation) were analyzed alongside higher‐order features via regression with heatmap visualization, and repeatable, robust, and calibratable features were identified. Results: Helical scans without tilt generally exhibited lower first‐order dispersion than axial scans. Introducing a 5° tilt reduced dispersion in axial scans but had inconsistent effects in helical scans, with no coherent trend observed. Radiation dose demonstrated an expected inverse relationship with dispersion metrics. Intraclass correlation coefficient (ICC) analysis revealed that 34% of radiomic metrics exhibited good or excellent repeatability across all trials (ICC ≥ 0.6), but only 13% demonstrated good or excellent robustness, highlighting the sensitivity of radiomic metrics to scanning conditions. Regression analysis yielded 31 metrics (33%) that can be calibrated using their significant linear relationships with the parameters varied in this study, thereby allowing researchers to correct for variations in acquisition settings. Conclusions: These findings underscore the importance of accounting for acquisition variability—including less frequently examined parameters such as tilt—when designing radiomic studies, selecting robust features, and interpreting results in clinical and multicenter studies. This approach helps distinguish meaningful biological variation from imaging artifacts, thereby improving the reliability of radiomic analysis in personalized medicine.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1002/acm2.70462

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • 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 [39]

    The Introduction says scanner hardware, acquisition techniques, and reconstruction settings can significantly influence radiomic feature values. The Discussion reports that about one-third of features were sensitive to the acquisition parameters studied and warns that protocol differences can bias multicenter results. The paper does not test whether features found strongly predictive under one scanner or protocol retain their predictive performance under another. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 36

  1. [1]Gillies RJ, Kinahan PE, Radiomics HH. Images are more than pictures, they are data. Radiology. 2016; 278(2): 563‐ 577. doi: 10.1148/radiol.2015151169 26579733 PMC4734157 · DOI · Paper page
  2. [2]Lambin P, Rios‐Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer. 2012; 48(4): 441‐ 446. doi: 10.1016/j.ejca.2011.11.036 22257792 PMC4533986 · DOI · Paper page
  3. [3]Varghese BA, Cen SY, Hwang DH, Duddalwar VA. Texture analysis of imaging: what radiologists need to know. Am J Roentgenol. 2019; 212(3): 520‐ 528. doi: 10.2214/ajr.18.20624 30645163 · DOI
  4. [4]Varghese BA, Hwang D, Cen SY, et al. Reliability of CT‐based texture features: phantom study. J Appl Clin Med Phys. 2019; 20(8): 155‐ 163. doi: 10.1002/acm2.12666 31222919 PMC6698768 · DOI
  5. [5]Kunapuli G, Varghese BA, Ganapathy P, et al. A decision‐support tool for renal mass classification. J Digit Imaging. 2018; 31(6): 929‐ 939. doi: 10.1007/s10278-018-0100-0 29980960 PMC6261185 · DOI · Paper page
  6. [6]Horvat N, Papanikolaou N, Koh D‐M. Radiomics beyond the hype: a critical evaluation toward oncologic clinical use. Radiol Artif Intell. 2024; 6(4): e230437. doi: 10.1148/ryai.230437 38717290 PMC11294952 · DOI
  7. [8]Zwanenburg A, Vallières M, Abdalah MA, et al. The image biomarker standardization initiative: standardized quantitative radiomics for high‐throughput image‐based phenotyping. Radiology. 2020; 295(2): 328‐ 338. doi: 10.1148/radiol.2020191145 32154773 PMC7193906 · DOI · Paper page
  8. [9]Yadav N, Turangan A, Han H, et al. Systematic approach to identifying sources of variation in CT radiomics: a phantom study. In: 2024 20th International Symposium on Medical Information Processing and Analysis (SIPAIM). 2024 Antigua, Guatemala, pp. 1‐4. Published online November, doi: 10.1109/SIPAIM62974.2024.10783586 · DOI
  9. [10]Shafiq‐Ul‐Hassan M, Zhang GG, Latifi K, et al. Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels. Med Phys. 2017; 44(3): 1050‐ 1062. doi: 10.1002/mp.12123 28112418 PMC5462462 · DOI
  10. [11]Berenguer R, Pastor‐Juan MDR, Canales‐Vázquez J, et al. Radiomics of CT features may be non‐reproducible and redundant: influence of CT acquisition parameters. Radiology. 2018; 288(2): 407‐ 415. doi: 10.1148/radiol.2018172361 29688159 · DOI
  11. [12]Noo F, Defrise M, Kudo H. General reconstruction theory for multislice x‐ray computed tomography with a gantry tilt. IEEE Trans Med Imaging. 2004; 23(9): 1109‐ 1116. doi: 10.1109/TMI_2004.829337 15377120 · DOI
  12. [13]Nikupaavo U, Kaasalainen T, Reijonen V, Ahonen SM, Kortesniemi M. Lens dose in routine head CT: comparison of different optimization methods with anthropomorphic phantoms. Am J Roentgenol. 2014; 204(1): 117‐ 23. doi: 10.2214/AJR.14.12763 25539246 · DOI
  13. [14]Pandu B, Khanna D, Palanisamy M, Jacob S, Maddipati T. Analyzing the patient isocenter positional accuracy with portal imaging acquired at different gantry angles and its dosimetrical impact on the dose delivery for head and neck cancers. Precis Radiat Oncol. 2024; 8(2): 77‐ 84. doi: 10.1002/pro6.1234 40336642 PMC11935235 · DOI
  14. [15]Nasada T, Hirayama S, Tanooka M, Fujita T, Sakai T. Evaluation of gantry tilt correction in multi detector‐row CT: effect on multi‐planar reconstruction images. Japanese J Radiol Technol. 2003; 59(1): 136‐ 142. doi: 10.6009/jjrt.kj00000921603 12577011 · DOI
  15. [17]Pati S, Singh A, Rathore S, et al. The cancer imaging phenomics toolkit (CaPTk): technical overview. Brainlesion. 2020; 11993: 380‐ 394. doi: 10.1007/978-3-030-46643-5_38 32754723 PMC7402244 · DOI · Paper page
  16. [18]van Griethuysen JJM, Fedorov A, Parmar C, et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res. 2017; 77(21): e104‐ e107. doi: 10.1158/0008-5472.CAN-17-0339 29092951 PMC5672828 · DOI
  17. [19]Traverso A, Kazmierski M, Welch M, et al. Sensitivity of radiomic features to inter‐observer variability and image pre‐processing in apparent diffusion coefficient (ADC) maps of cervix cancer patients. Radiother Oncol. 2020; 143: 88‐ 94, doi: 10.1016/radonc.2019.08.008 31477335 · DOI
  18. [20]Jensen L, Kim D, Elgeti T, et al. Enhancing the stability of CT radiomics across different volume of interest sizes using parametric feature maps: a phantom study. Eur Radiol Exp. 2022; 6(1): 43. doi: 10.1186/s41747-022-00297-7 36104519 PMC9474978 · DOI
  19. [21]Larue RTHM, van Timmeren JE, Jong EEC, et al. Influence of gray level discretization on radiomic feature stability for different CT scanners, tube currents and slice thicknesses: a comprehensive phantom study. Acta Oncol. 2017; 56(11): 1544‐ 1553. doi: 10.1080/0284186X.2017.1351624 28885084 · DOI
  20. [22]Li Y, Tan G, Vangel M, Hall J, Cai W. Influence of feature calculating parameters on the reproducibility of CT radiomic features: a thoracic phantom study. Quant Imaging Med Surg. 2020; 10(9): 1775‐ 1785. doi: 10.21037/qims-19-921 32879856 PMC7417756 · DOI
  21. [23]Cicchetti DV. Guidelines, criteria, and rules of thumb for evaluating normed and standardized assessment instruments in psychology. Psychol Assess. 1994; 6(4): 284‐ 290. doi: 10.1037/1040-3590.6.4.284 · DOI · Paper page
  22. [24]Smits‐Engelsman BCM, Smit E, RX Doe‐Asingyo, et al. Inter‐rater reliability and test‐retest reliability of the performance and fitness (PERF‐FIT) test battery for children: a test for motor skill related fitness. BMC Pediatr. 2021; 21: 119. doi: 10.1186/s12886-021-02589-0 33706742 PMC7948346 · DOI
  23. [25]Ketcherside T, Shi C, Chen Q, et al. Evaluation of repeatability and reproducibility of radiomic features produced by the fan‐beam kV‐CT on a novel ring gantry‐based PET/CT linear accelerator. Med Phys. 2023; 50(6): 3719‐ 3725. doi: 10.1002/mp.16399 36995245 · DOI
  24. [26]Wang H, Zhou Y, Wang X, et al. Reproducibility and repeatability of CBCT‐derived radiomics features. Front Oncol. 2021; 11: 773512. doi: 10.3389/fonc.2021.773512 34869015 PMC8637922 · DOI
  25. [27]Hein I, Taguchi K, Silver MD, Kazama M, Mori I. Feldkamp‐based cone‐beam reconstruction for gantry‐tilted helical multislice CT. Med Phys. 2003; 30(12): 3233‐ 3242. doi: 10.1118/1.1625443 14713090 · DOI
  26. [28]Joskowicz L, Cohen D, Caplan N, Sosna J. Inter‐observer variability of manual contour delineation of structures in CT. Eur Radiol. 2018; 29(3): 1391‐ 1399. doi: 10.1007/s00330-018-5695-5 30194472 · DOI
  27. [29]van Timmeren J, Cester D, Tanadini‐Lang S, et al. Radiomics in medical imaging—“how‐to” guide and critical reflection. Insights Imaging. 2020; 11: 91. doi: 10.1186/s13244-020-00887-2 32785796 PMC7423816 · DOI · Paper page
  28. [30]Rizzo S, Botta F, Raimondi S, et al. Radiomics: the facts and the challenges of image analysis. Eur Radiol Exp. 2018; 2: 36. doi: 10.1186/s41747-018-0068-z 30426318 PMC6234198 · DOI
  29. [31]Valladares A, Beyer T, Rausch I. Physical imaging phantoms for simulation of tumor heterogeneity in PET, CT, and MRI: an overview of existing designs. Med Phys. 2020; 47(4): 2023‐ 2037. doi: 10.1002/mp.14045 31981214 PMC7216968 · DOI
  30. [32]Brandner ED, Wu A, Chen H, et al. Abdominal organ motion measured using 4D CT. Int J Radiat Oncol Biol Phys. 2006; 65(2): 554‐ 560. doi: 10.1016/j.ijrobp.2005.12.042 16690437 · DOI
  31. [33]Yonghua Y, Limin L, Baosheng L, et al. Establishment of a mathematical model for the influence of respiratory movement upon the position of an intrahepatic space‐occupying lesion. Chin J Radiat Oncol. 2002; 11(4): 245‐ 247. Accessed September 4, 2025. https://inis.iaea.org/records/50jx3‐w5y46
  32. [34]Xi M, Liu M‐Z, Li Q‐Q, Cai L, Zhang L, Hu Y‐H. Analysis of abdominal organ motion using four‐dimensional CT. Chin J Cancer. 2009; 28(9): 989‐ 993. doi: 10.5732/cjc.009.10193 19728920 · DOI
  33. [35]Varghese BA, Cen SY, Jensen K, et al. Investigating the role of imaging factors in the variability of CT‐based texture analysis metrics. J Appl Clin Med Phys. 2023; 25(4): e14192. doi: 10.1002/acm2.14192 37962032 PMC11005980 · DOI
  34. [36]Zhao B. Understanding sources of variation to improve the reproducibility of radiomics. Front Oncol. 2021; 11: 633176. doi: 10.3389/fonc.2021.633176 33854969 PMC8039446 · DOI · Paper page
  35. [37]Teng X, Wang Y, Nicol AJ, et al. Enhancing the clinical utility of radiomics: addressing the challenges of repeatability and reproducibility in CT and MRI. Diagnostics. 2024; 14(16): 1835. doi: 10.3390/diagnostics14161835 39202322 PMC11353986 · DOI
  36. [38]Hoye J, Smith T, Abadi E, Solomon JB, Samei E. Correction for systematic bias in radiomics measurements due to variation in imaging protocols. Acad Radiol. 2022; 29(4): e61‐ e72. doi: 10.1016/j.acra.2021.04.012 34130922 PMC8665935 · DOI

Cited by

Sorted by citations

Authors
Yadav, N., Lei, X., Cen, S. Y., Levy, J., Jensen, K., & Varghese, B. A.
Affiliations
  • University of Southern California (US)
  • The Phantom Laboratory Greenwich New York USA
  • Oslo University Hospital (NO)
Corresponding author
Yadav, Neha
ORCID
Venue
Journal of Applied Clinical Medical Physics
Publisher
Wiley
Published
2026-01
Volume
27
Issue
1
Pages
e70462-e70462
ISSN
1526-9914 (ISSN-L)
DOI
10.1002/acm2.70462
PMC
PMC12800920
OpenAlex
W7123639209
Cited by
1
References
1
Citations by year
2026: 1
FWCI
4.70
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Applied Clinical Medical Physics (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • PubMed Central (publishedVersion, cc-by)
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
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
Keywords
CT radiomics, gantry tilt, scanning mode, radiation dose, feature calibration, texture analysis, liver phantom
MeSH terms
Radiomics, Humans, Image Processing, Computer-Assisted, Liver, Radiation Dosage, Radiotherapy Planning, Computer-Assisted, Tomography, X-Ray Computed, Phantoms, Imaging, Radiotherapy, Intensity-Modulated

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review