Evidence map›Paper›PMID 38454921›Full record

ArticleF1000Research2023

Impact of slice thickness on reproducibility of CT radiomic features of lung tumors.

Sanat Gupta, Kaushik Nayak, Saikiran Pendem

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In one paragraph

Article in F1000Research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 2 pooled it
–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

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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

6 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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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

3 authors.

Sanat GuptaManipal College of Health Professions, Department of Medical Imaging Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.
Kaushik NayakManipal College of Health Professions, Department of Medical Imaging Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.
Saikiran PendemManipal College of Health Professions, Department of Medical Imaging Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.ORCID 0000-0001-7933-1192

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Radiomics posits that quantified characteristics from radiographic images reflect underlying pathophysiology. Lung cancer (LC) is one of the prevalent forms of cancer, causing mortality. Slice thickness (ST) of computed tomography (CT) images is a crucial factor influencing the generalizability of radiomic features (RF) in oncology. There is scarcity of research that how ST affects variability of RF in LC. The present study helps in identifying the specific RF categories affected by variations in ST and provides valuable insights for researchers and clinicians working with RF in the field of LC.Hence, aim of the study is to evaluate influence of ST on reproducibility of CT-RF for lung tumors. Methods: This is a prospective study, 32 patients with confirmed histopathological diagnosis of lung tumors were included. Contrast Enhanced CT (CECT) thorax was performed using a 128- Incisive CT (Philips Health Care). The image acquisition was performed with 5-mm and 2 mm STwas reconstructed retrospectively. RF were extracted from the CECT thorax images of both ST. We conducted a paired t-test to evaluate the disparity in RF between the two thicknesses. Lin's Concordance Correlation Coefficient (CCC) was performed to identify the reproducibility of RF between the two thicknesses. Results: Out of 107 RF, 66 (61.6%) exhibited a statistically significant distinction (p<0.05) when comparing two ST and while 41 (38.3%) RF did not show significant distinction (p>0.05) between the two ST measurements. 29 features (CCC ≥ 0.90) showed excellent to moderate reproducibility, and 78 features (CCC ≤ 0.90) showed poor reproducibility. Among the 7 RF categories, the shape-based features (57.1%) showed the maximum reproducibility whereas NGTDM-based features showed negligible reproducibility. Conclusions: The ST had a notable impact on the majority of CT-RF of lung tumors. Shape based features (57.1%). First order (44.4%) features showed highest reproducibility compared to other RF categories.

Indexed as

Lung NeoplasmsRadiomicsHumansProspective StudiesReproducibility of ResultsRetrospective StudiesTomography, X-Ray ComputedComputed TomographyCT ParametersLung CancerRadiomicsSlice Thickness

Identifiers

PMID38454921
PMCPMC10918310

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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.