Evidence map›Paper›PMID 37552259›Full record

ArticleEuropean radiology2024

Machine learning slice-wise whole-lung CT emphysema score correlates with airway obstruction.

Mats Lidén, Antoine Spahr, Ola Hjelmgren, Simone Bendazzoli, Josefin Sundh, Magnus Sköld, Göran Bergström, Chunliang Wang, Per Thunberg

Open access · hybridAbstract read
In one paragraph

Article in European radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.8field-weighted citation impact, top 24% 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

0 citing papers in PubMed, 3 citations in OpenAlex.

No citing paper in PubMed yet.

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 5 institutions in 1 country.

Mats LidénDepartment of Radiology and Medical Physics, Faculty of Medicine and Health, Örebro University, 701 82, Örebro, Sweden. mats.liden@regionorebrolan.se.ORCID http://orcid.org/0000-0002-1346-1450
Antoine SpahrDepartment of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology School of Technology and Health, Stockholm, Sweden.
Ola HjelmgrenDepartment of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Simone BendazzoliDepartment of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology School of Technology and Health, Stockholm, Sweden.
Josefin SundhDepartment of Respiratory Medicine, Faculty of Medicine and Health, Örebro University, Örebro, Sweden.
Magnus SköldDepartment of Medicine Solna, Karolinska Institutet, Stockholm, Sweden.
Göran BergströmDepartment of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Chunliang WangDepartment of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology School of Technology and Health, Stockholm, Sweden.
Per ThunbergDepartment of Radiology and Medical Physics, Faculty of Medicine and Health, Örebro University, 701 82, Örebro, Sweden.
Örebro University · SEKTH Royal Institute of Technology · SESahlgrenska University Hospital · SEKarolinska Institutet · SEKarolinska University Hospital · SE

Funding

Analytic Imaging Diagnostics Arena, Linköping, Sweden 2104_LidenNyckelfonden, Örebro OLL-881491Region Örebro län OLL-959996
6 · The paper itself

Abstract

objectivesQuantitative CT imaging is an important emphysema biomarker, especially in smoking cohorts, but does not always correlate to radiologists' visual CT assessments. The objectives were to develop and validate a neural network-based slice-wise whole-lung emphysema score (SWES) for chest CT, to validate SWES on unseen CT data, and to compare SWES with a conventional quantitative CT method. MATERIALS AND

methodsSeparate cohorts were used for algorithm development and validation. For validation, thin-slice CT stacks from 474 participants in the prospective cross-sectional Swedish CArdioPulmonary bioImage Study (SCAPIS) were included, 395 randomly selected and 79 from an emphysema cohort. Spirometry (FEV1/FVC) and radiologists' visual emphysema scores (sum-visual) obtained at inclusion in SCAPIS were used as reference tests. SWES was compared with a commercially available quantitative emphysema scoring method (LAV950) using Pearson's correlation coefficients and receiver operating characteristics (ROC) analysis.

resultsSWES correlated more strongly with the visual scores than LAV950 (r = 0.78 vs. r = 0.41, p < 0.001). The area under the ROC curve for the prediction of airway obstruction was larger for SWES than for LAV950 (0.76 vs. 0.61, p = 0.007). SWES correlated more strongly with FEV1/FVC than either LAV950 or sum-visual in the full cohort (r =  - 0.69 vs. r =  - 0.49/r =  - 0.64, p < 0.001/p = 0.007), in the emphysema cohort (r =  - 0.77 vs. r =  - 0.69/r =  - 0.65, p = 0.03/p = 0.002), and in the random sample (r =  - 0.39 vs. r =  - 0.26/r =  - 0.25, p = 0.001/p = 0.007).

conclusionThe slice-wise whole-lung emphysema score (SWES) correlates better than LAV950 with radiologists' visual emphysema scores and correlates better with airway obstruction than do LAV950 and radiologists' visual scores. CLINICAL RELEVANCE STATEMENT: The slice-wise whole-lung emphysema score provides quantitative emphysema information for CT imaging that avoids the disadvantages of threshold-based scores and is correlated more strongly with reference tests than LAV950 and reader visual scores. KEY POINTS: • A slice-wise whole-lung emphysema score (SWES) was developed to quantify emphysema in chest CT images. • SWES identified visual emphysema and spirometric airflow limitation significantly better than threshold-based score (LAV950). • SWES improved emphysema quantification in CT images, which is especially useful in large-scale research.

Indexed as

Airway ObstructionEmphysemaPulmonary Disease, Chronic ObstructivePulmonary EmphysemaCross-Sectional StudiesHumansLungProspective StudiesTomography, X-Ray ComputedDeep learningLungPulmonary disease, chronic obstructivePulmonary emphysemaTomography, X-ray computed

Identifiers

PMID37552259
PMCPMC10791709
OpenAlexW4385659570

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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.