Evidence map›Paper›PMID 40402291›Full record

ArticleEuropean radiology2025

Influence of content-based image retrieval on the accuracy and inter-reader agreement of usual interstitial pneumonia CT pattern classification.

Sohee Park, Hye Jeon Hwang, Jihye Yun, Eun Jin Chae, Jooae Choe, Sang Min Lee, Han Na Lee, So Youn Shin, Heejun Park, Hana Jeong and 5 more

Abstract read
PubMed Publisher
In one paragraph

Article in European radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 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

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

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. 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

15 authors.

Sohee ParkDepartment of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
Hye Jeon HwangDepartment of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea. umttette@hanmail.net.ORCID http://orcid.org/0000-0003-3508-2870
Jihye YunDepartment of Convergence Medicine, Biomedical Engineering Research Center, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
Eun Jin ChaeDepartment of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
Jooae ChoeDepartment of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
Sang Min LeeDepartment of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
Han Na LeeDepartment of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
So Youn ShinDepartment of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
Heejun ParkDepartment of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
Hana JeongDepartment of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
Min Jee KimDepartment of Pulmonology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
Jang Ho LeeDepartment of Pulmonology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
Kyung-Wook JoDepartment of Pulmonology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
Seunghee BaekDepartment of Clinical Epidemiology and Biostatistics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
Joon Beom SeoDepartment of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo investigate whether a content-based image retrieval (CBIR) of similar chest CT images can help usual interstitial pneumonia (UIP) CT pattern classifications among readers with varying levels of experience. MATERIALS AND

methodsThis retrospective study included patients who underwent high-resolution chest CT between 2013 and 2015 for the initial workup for fibrosing interstitial lung disease. UIP classifications were assigned to CT images by three thoracic radiologists, which served as the ground truth. One hundred patients were selected as queries. The CBIR retrieved the top three similar CT images with UIP classifications using a deep learning algorithm. The diagnostic accuracies and inter-reader agreement of nine readers before and after CBIR were evaluated.

resultsOf 587 patients (mean age, 63 years; 356 men), 100 query cases (26 UIP patterns, 26 probable UIP patterns, 5 indeterminate for UIP, and 43 alternative diagnoses) were selected. After CBIR, the mean accuracy (61.3% to 67.1%; p = 0.011) and inter-reader agreement (Fleiss Kappa, 0.400 to 0.476; p = 0.003) were slightly improved. The accuracies of the radiologist group for all CT patterns except indeterminate for UIP increased after CBIR; however, they did not reach statistical significance. The resident and pulmonologist groups demonstrated mixed results: accuracy decreased for UIP pattern, increased for alternative diagnosis, and varied for others.

conclusionCBIR slightly improved diagnostic accuracy and inter-reader agreement in UIP pattern classifications. However, its impact varied depending on the readers' level of experience, suggesting that the current CBIR system may be beneficial when used to complement the interpretations of experienced readers. KEY POINTS: Question CT pattern classification is important for the standardized assessment and management of idiopathic pulmonary fibrosis, but requires radiologic expertise and shows inter-reader variability. Findings CBIR slightly improved diagnostic accuracy and inter-reader agreement for UIP CT pattern classifications overall. Clinical relevance The proposed CBIR system may guide consistent work-up and treatment strategies by enhancing accuracy and inter-reader agreement in UIP CT pattern classifications by experienced readers whose expertise and experience can effectively interact with CBIR results.

Indexed as

Idiopathic Pulmonary FibrosisInformation Storage and RetrievalLung Diseases, InterstitialRadiographic Image Interpretation, Computer-AssistedTomography, X-Ray ComputedAdultAgedAged, 80 and overAlgorithmsDeep LearningFemaleHumansMaleMiddle AgedObserver VariationReproducibility of ResultsLung diseases (interstitial)Radiographic image interpretation (computer-assisted)Reproducibility of resultsTomography (X-ray computed)

Identifiers

PMID40402291

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.