Evidence map›Paper›PMID 37962032›Full record

ArticleJournal of applied clinical medical physics2024

Investigating the role of imaging factors in the variability of CT-based texture analysis metrics.

Bino Abel Varghese, Steven Yong Cen, Kristin Jensen, Joshua Levy, Hilde Kjernlie Andersen, Anselm Schulz, Xiaomeng Lei, Vinay Anant Duddalwar, David John Goodenough

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of applied clinical medical physics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
  2. Radiomics and Back Pain.Global spine journal · 2026
    Review
  3. Article
  4. Influence of scan mode, tilt, and radiation dose on CT radiomic metrics.Journal of applied clinical medical physics · 2026
    Article
  5. Article
  6. Article
  7. 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

9 authors.

Bino Abel VargheseKeck Medical Center, Department of Radiology, University of Southern California, Los Angeles, California, USA.ORCID https://orcid.org/0000-0002-0508-2899
Steven Yong CenKeck Medical Center, Department of Radiology, University of Southern California, Los Angeles, California, USA.
Kristin JensenDepartment of Physics and Computational Radiology, Oslo, Norway.
Joshua LevyThe Phantom Laboratory, Greenwich, New York, USA.
Hilde Kjernlie AndersenDepartment of Physics and Computational Radiology, Oslo, Norway.
Anselm SchulzDepartment of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway.
Xiaomeng LeiKeck Medical Center, Department of Radiology, University of Southern California, Los Angeles, California, USA.
Vinay Anant DuddalwarKeck Medical Center, Department of Radiology, University of Southern California, Los Angeles, California, USA.
David John GoodenoughDepartment of Radiology, George Washington University, Washington, District of Columbia, USA.

Funding

Integrated radiomic and liquid biopsy monitoring in SWOG S1802: A phase 3 therapeutic trial for metastatic prostate cancerR01CA257610 · NCI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI DUDDALWAR, VINAY, GOLDKORN, AMIR · 2021 to 2025
$3.4M
NCI NIH HHS R01 CA257610
6 · The paper itself

Abstract

objectiveThis study assesses the robustness of first-order radiomic texture features namely interquartile range (IQR), coefficient of variation (CV) and standard deviation (SD) derived from computed tomography (CT) images by varying dose, reconstruction algorithms and slice thickness using scans of a uniform water phantom, a commercial anthropomorphic liver phantom, and a human liver in-vivo. MATERIALS AND

methodsScans were acquired on a 16 cm detector GE Revolution Apex Edition CT scanner with variations across three different nominal slice thicknesses: 0.625, 1.25, and 2.5 mm, three different dose levels: CTDIvol of 13.86 mGy for the standard dose, 40% reduced dose and 60% reduced dose and two different reconstruction algorithms: a deep learning image reconstruction (DLIR-high) algorithm and a hybrid iterative reconstruction (IR) algorithm ASiR-V50% (AV50) were explored, varying one at a time. To assess the effect of non-linear modifications of images by AV50 and DLIR-high, images of the water phantom were also reconstructed using filtered back projection (FBP). Quantitative measures of IQR, CV and SD were extracted from twelve pre-selected, circular (1 cm diameter) regions of interest (ROIs) capturing different texture patterns across all scans.

resultsAcross all scans, imaging, and reconstruction settings, CV, IQR and SD were observed to increase with reduction in dose and slice thickness. An exception to this observation was found when using FBP reconstruction. Lower values of CV, IQR and SD were observed in DLIR-high reconstructions compared to AV50 and FBP. The Poisson statistics were more stringently noted in FBP than DLIR-high and AV50, due to the non-linear nature of the latter two algorithms.

conclusionVariation in image noise due to dose reduction algorithms, tube current, and slice thickness show a consistent trend across phantom and patient scans. Prospective evaluation across multiple centers, scanners and imaging protocols is needed for establishing quality assurance standards of radiomics.

Indexed as

AlgorithmsTomography, X-Ray ComputedHumansImage Processing, Computer-AssistedPhantoms, ImagingRadiation DosageRadiographic Image Interpretation, Computer-AssistedWaterWatercomputed tomographyimagingphantom studyquality assuranceradiomics

Identifiers

PMID37962032
PMCPMC11005980

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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.