Evidence map›Paper›PMID 30887205›Full record

Observational studyEuropean radiology2019

Adaptive statistical iterative reconstruction (ASIR) affects CT radiomics quantification in primary colorectal cancer.

Davide Prezzi, Katarzyna Owczarczyk, Paul Bassett, Muhammad Siddique, David J Breen, Gary J R Cook, Vicky Goh

Open access · hybridAbstract readObservational Study
In one paragraph

Observational study in European radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
3.9field-weighted citation impact, top 6% of its field
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

15 citing papers in PubMed, 31 citations in OpenAlex.

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  8. Predictive Value ofDiagnostics (Basel, Switzerland) · 2022
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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

7 authors at 2 institutions in 1 country.

Davide PrezziSchool of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, Lambeth Wing, St Thomas' Hospital, London, SE1 7EH, UK. davide.prezzi@kcl.ac.uk.ORCID http://orcid.org/0000-0002-0878-2196
Katarzyna OwczarczykSchool of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, Lambeth Wing, St Thomas' Hospital, London, SE1 7EH, UK.
Paul BassettStatsconsultancy Ltd., 40 Longwood Lane, Amersham, Bucks, HP7 9EN, UK.
Muhammad SiddiqueSchool of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, Lambeth Wing, St Thomas' Hospital, London, SE1 7EH, UK.
David J BreenUniversity Hospital Southampton NHS Foundation Trust, Tremona Road, Southampton, SO16 6YD, UK.
Gary J R CookSchool of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, Lambeth Wing, St Thomas' Hospital, London, SE1 7EH, UK.
Vicky GohSchool of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, Lambeth Wing, St Thomas' Hospital, London, SE1 7EH, UK.
St Thomas' Hospital · GBUniversity Hospital Southampton NHS Foundation Trust · GB

Funding

Cancer Research UK 16463Department of Health 09/22/49Wellcome Trust 203148/Z/16/Z
6 · The paper itself

Abstract

objectivesTo investigate whether adaptive statistical iterative reconstruction (ASIR), a hybrid iterative CT image reconstruction algorithm, affects radiomics feature quantification in primary colorectal cancer compared to filtered back projection. Additionally, to establish whether radiomics from single-slice analysis undergo greater change than those from multi-slice analysis.

methodsFollowing review board approval, contrast-enhanced CT studies from 32 prospective primary colorectal cancer patients were reconstructed with 20% ASIR level increments, from 0 to 100%. Radiomics analysis was applied to single-slice and multi-slice regions of interest outlining the tumour: 70 features, including statistical (first-, second- and high-order) and fractal radiomics, were generated per dataset. The effect of ASIR was calculated by means of multilevel linear regression.

resultsTwenty-eight CT datasets were suitable for analysis. Incremental ASIR levels determined a significant change (p < 0.001) in most statistical radiomics features, best described by a simple linear relationship. First-order statistical features, including mean, standard deviation, skewness, kurtosis, energy and entropy, underwent a relatively small change in both single-slice and multi-slice analysis (median standardised effect size B = 0.08). Second-order statistical features, including grey-level co-occurrence and difference matrices, underwent a greater change in single-slice analysis (median B = 0.36) than in multi-slice analysis (median B = 0.13). Fractal features underwent a significant change only in single-slice analysis (median B = 0.49).

conclusionsIncremental levels of ASIR affect significantly CT radiomics quantification in primary colorectal cancer. Second-order statistical and fractal features derived from single-slice analysis undergo greater change than those from multi-slice analysis. KEY POINTS: • Incremental levels of ASIR determine a significant change in most statistical (first-, second- and high-order) CT radiomics features measured in primary colorectal cancer, best described by a linear relationship. • First-order statistical features undergo a small change, both from single-slice and multi-slice radiomics analyses. • Most second-order statistical features undergo a greater change in single-slice analysis than in multi-slice analysis. Fractal features are only affected in single-slice analysis.

Indexed as

AlgorithmsColorectal NeoplasmsHumansProspective StudiesRadiation DosageRadiographic Image Interpretation, Computer-AssistedStatistics as TopicTomography, X-Ray ComputedColorectal neoplasmsComputer-assistedFractalsImage processingMultidetector computed tomography

Identifiers

PMID30887205
PMCPMC6717179
OpenAlexW2922204311

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.