Evidence map›Paper›PMID 33827699›Full record

ArticleCancer imaging : the official publication of the International Cancer Imaging Society2021

Are radiomics features universally applicable to different organs?

Seung-Hak Lee, Hwan-Ho Cho, Junmo Kwon, Ho Yun Lee, Hyunjin Park

Abstract read
In one paragraph

Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. [Molecular cancer · 2026
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  7. Biodistribution Assessment of a NovelLife (Basel, Switzerland) · 2024
    Article
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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

5 authors.

Seung-Hak LeeDepartement of Electronic Electrical and Computer Engineering, Sungkyunkwan University, Suwon, 16419, South Korea.
Hwan-Ho ChoDepartement of Electronic Electrical and Computer Engineering, Sungkyunkwan University, Suwon, 16419, South Korea.
Junmo KwonDepartement of Electronic Electrical and Computer Engineering, Sungkyunkwan University, Suwon, 16419, South Korea.
Ho Yun LeeDepartment of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-Ro, Gangnam-Gu, Seoul, 06351, South Korea. hoyunlee96@gmail.com.
Hyunjin ParkCenter for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon, 16419, South Korea. hyunjinp@skku.edu.ORCID http://orcid.org/0000-0001-5681-8918

Funding

National Research Foundation NRF-2020M3E5D2A01084892
6 · The paper itself

Abstract

backgroundMany studies have successfully identified radiomics features reflecting macroscale tumor features and tumor microenvironment for various organs. There is an increased interest in applying these radiomics features found in a given organ to other organs. Here, we explored whether common radiomics features could be identified over target organs in vastly different environments.

methodsFour datasets of three organs were analyzed. One radiomics model was constructed from the training set (lungs, n = 401), and was further evaluated in three independent test sets spanning three organs (lungs, n = 59; kidneys, n = 48; and brains, n = 43). Intensity histograms derived from the whole organ were compared to establish organ-level differences. We constructed a radiomics score based on selected features using training lung data over the tumor region. A total of 143 features were computed for each tumor. We adopted a feature selection approach that favored stable features, which can also capture survival. The radiomics score was applied to three independent test data from lung, kidney, and brain tumors, and whether the score could be used to separate high- and low-risk groups, was evaluated.

resultsEach organ showed a distinct pattern in the histogram and the derived parameters (mean and median) at the organ-level. The radiomics score trained from the lung data of the tumor region included seven features, and the score was only effective in stratifying survival for other lung data, not in other organs such as the kidney and brain. Eliminating the lung-specific feature (2.5 percentile) from the radiomics score led to similar results. There were no common features between training and test sets, but a common category of features (texture category) was identified.

conclusionAlthough the possibility of a generally applicable model cannot be excluded, we suggest that radiomics score models for survival were mostly specific for a given organ; applying them to other organs would require careful consideration of organ-specific properties.

Indexed as

AdultAgedFemaleHumansMaleMiddle AgedOrgans at RiskRadiometryComputed tomographyMacroscale tumor featuresMagnetic resonance imagingRadiomicsSurvival analysisTumor microenvironment

Identifiers

PMID33827699
PMCPMC8028225

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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.