Evidence map›Paper›PMID 41708686›Full record

ArticleScientific reports2026

Radiomic profiling of chest CT in a cohort of sarcoidosis cases.

Nichole E Carlson, Wren L Lippitt, Sarah M Ryan, Margaret Mroz, Briana Barkes, Shu-Yi Liao, Lisa A Maier, Tasha E Fingerlin

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

8 authors.

Nichole E CarlsonDepartment of Biostatistics and Informatics, University of Colorado-Anschutz Medical Campus, 13001 E 17th PL, MS B-119, Aurora, CO, 80045, USA. nichole.carlson@cuanschutz.edu.
Wren L LippittDepartment of Biostatistics and Informatics, University of Colorado-Anschutz Medical Campus, 13001 E 17th PL, MS B-119, Aurora, CO, 80045, USA.
Sarah M RyanDepartment of Biostatistics and Informatics, University of Colorado-Anschutz Medical Campus, 13001 E 17th PL, MS B-119, Aurora, CO, 80045, USA.
Margaret MrozDepartment of Medicine, National Jewish Health, Denver, CO, USA.
Briana BarkesDepartment of Medicine, National Jewish Health, Denver, CO, USA.
Shu-Yi LiaoDepartment of Medicine, National Jewish Health, Denver, CO, USA.
Lisa A MaierDepartment of Medicine, National Jewish Health, Denver, CO, USA.
Tasha E FingerlinDepartment of Biostatistics and Informatics, University of Colorado-Anschutz Medical Campus, 13001 E 17th PL, MS B-119, Aurora, CO, 80045, USA.

Funding

Novel integrative approaches for disease phenotyping, utilizing radiomics in SarcoidosisR01HL142049 · NHLBI · NATIONAL JEWISH HEALTH · PI CARLSON, NICHOLE, FINGERLIN, TASHA E. · 2019 to 2022
$2.6M
NHLBI NIH HHS R01 HL142049
6 · The paper itself

Abstract

Chest high resolution computed tomography (HRCT) is increasingly used in clinical practice for sarcoidosis. Visual assessment (VAS) remains standard even with its high inter- and intra-rater variation. Radiomics may be a less variable assessment. We developed radiomic profiles of HRCT and mapped them to radiologic, functional, and patient reported outcomes. Radiomics were calculated on HRCT for both lungs from sarcoidosis cases enrolled in the Genomic Research in Alpha-1 Antitrypsin Deficiency and Sarcoidosis (GRADS) study (N = 320). Cases were clustered using radiomics profiles via robust and sparse K-means. Differences in VAS by cluster were identified using chi-squared tests. Linear regression investigated how pulmonary function tests (PFT) and patient reported outcomes (PRO) differed between clusters with and without adjustment for other radiologic quantification. Sensitivity analyses were used for validation. Clustering produced four patient groups that were associated with, but did not mimic, Scadding stage and Oberstein score (p < 0.001). One cluster had markedly few abnormalities. Another cluster had consistently more abnormalities and fibrosis. Clusters explained 10-15% of PFT variation and significantly predicted PFT even after accounting for other radiologic findings (p < 0.001). Associations with PRO were less consistent. These findings provide early-stage evidence that radiomics may be a useful component in identifying disease phenotypes.

Indexed as

LungSarcoidosisTomography, X-Ray ComputedAdultCluster AnalysisCohort StudiesFemaleHumansMaleMiddle AgedRadiography, ThoracicRadiomicsRespiratory Function TestsPatient outcomesPhenotypingPulmonary functionQuantitative imagingTexture analysis

Identifiers

PMID41708686
PMCPMC13013954

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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.