Evidence map›Paper›PMID 35080646›Full record

ArticleEuropean radiology2022

Role of hepatic metastatic lesion size on inter-reader reproducibility of CT-based radiomics features.

Linda C Kelahan, Donald Kim, Moataz Soliman, Ryan J Avery, Hatice Savas, Rishi Agrawal, Michael Magnetta, Benjamin P Liu, Yuri S Velichko

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Article in European radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
1.9field-weighted citation impact, top 16% 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

9 citing papers in PubMed, 13 citations in OpenAlex.

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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

9 authors at 1 institution in 1 country.

Linda C KelahanDepartment of Radiology, Northwestern University - Feinberg School of Medicine, 676 North St. Clair Street, Chicago, IL, 60611, USA.
Donald KimDepartment of Radiology, Northwestern University - Feinberg School of Medicine, 676 North St. Clair Street, Chicago, IL, 60611, USA.
Moataz SolimanDepartment of Radiology, Northwestern University - Feinberg School of Medicine, 676 North St. Clair Street, Chicago, IL, 60611, USA.
Ryan J AveryDepartment of Radiology, Northwestern University - Feinberg School of Medicine, 676 North St. Clair Street, Chicago, IL, 60611, USA.
Hatice SavasDepartment of Radiology, Northwestern University - Feinberg School of Medicine, 676 North St. Clair Street, Chicago, IL, 60611, USA.
Rishi AgrawalDepartment of Radiology, Northwestern University - Feinberg School of Medicine, 676 North St. Clair Street, Chicago, IL, 60611, USA.
Michael MagnettaDepartment of Radiology, Northwestern University - Feinberg School of Medicine, 676 North St. Clair Street, Chicago, IL, 60611, USA.
Benjamin P LiuDepartment of Radiology, Northwestern University - Feinberg School of Medicine, 676 North St. Clair Street, Chicago, IL, 60611, USA.
Yuri S VelichkoDepartment of Radiology, Northwestern University - Feinberg School of Medicine, 676 North St. Clair Street, Chicago, IL, 60611, USA. y-velichko@northwestern.edu.
Northwestern University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo evaluate the effect of hepatic metastatic lesion size on inter-reader reproducibility of CT-based 2D radiomics imaging features.

methodsComputerized tomography (CT) scans of 59 liver metastases from 34 patients with colorectal cancer were evaluated. Image segmentation was performed manually by three readers blinded to each other's results. For each radiomics feature, we created two datasets by sorting measurements according to size, i.e., (i) from the smallest to the largest lesion and (ii) from the largest to the smallest lesion. The Lin concordance correlation coefficient (CCC) was employed to analyze the reproducibility of radiomics features. In particular, the CCC was computed as a function of a number of elements in the dataset, by gradually adding lesions from each sorted dataset. To evaluate the effect of lesion size, we analyzed the difference between these two functions thus assessing the contribution of small and large lesions into the reproducibility of radiomics features.

resultsInter-reader reproducibility of CT-based 2D radiomics features assessed using Lin's CCC demonstrates tumor-size dependence. For example, the Lin CCC for GLCM contrast equals 0.88 (95% C.I. 0.84 to 0.92, p < 0.003) and could change by an additional + / - 0.06 depending on the presence of large or small lesions.

conclusionsGroups of "large" and "small" lesions show different inter-reader reproducibility. The inter-reader reproducibility from the mixed group consisting of "large" and "small" lesions depends on the lesion-size distribution and can vary widely. This finding could partially explain variability in reproducibility of radiomics features in the literature. KEY POINTS: • Groups of "large" and "small" lesions show different inter-reader reproducibility. • The inter-reader reproducibility from the mixed group consisting of "large" and "small" lesions depends on the lesion-size distribution.

Indexed as

Liver NeoplasmsFemaleHumansReproducibility of ResultsTomography, X-Ray ComputedComputer tomographyLiver metastasisRadiomicsReproducibility

Identifiers

PMID35080646
OpenAlexW4210692884

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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.