Evidence map›Paper›PMID 40134084›Full record

ArticleYonsei medical journal2025

Deep Learning-Based Computer-Aided Diagnosis in Coronary Artery Calcium-Scoring CT for Pulmonary Nodule Detection: A Preliminary Study.

Seung Yun Lee, Ji Weon Lee, Jung Im Jung, Kyunghwa Han, Suyon Chang

Abstract readEvaluation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Yonsei medical journal, 2025. 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

5 authors.

Seung Yun LeeDepartment of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea.ORCID https://orcid.org/0000-0001-7091-2441
Ji Weon LeeDepartment of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea.ORCID https://orcid.org/0009-0004-2047-2272
Jung Im JungDepartment of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea.ORCID https://orcid.org/0000-0001-8264-9388
Kyunghwa HanDepartment of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0002-5687-7237
Suyon ChangDepartment of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea. ohyes723@gmail.com.ORCID https://orcid.org/0000-0002-9221-8116

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo evaluate the feasibility and utility of deep learning-based computer-aided diagnosis (DL-CAD) for the detection of pulmonary nodules on coronary artery calcium (CAC)-scoring computed tomography (CT). MATERIALS AND

methodsThis retrospective study included 273 patients (aged 63.9±13.2 years; 129 men) who underwent CAC-scoring CT. A DL-CAD system based on thin-section images was used for pulmonary nodule detection, and two independent junior readers reviewed the standard CAC-scoring CT scans with and without referencing DL-CAD results. A reference standard was established through the consensus of two experienced radiologists. Sensitivity, positive predictive value, and F1-score were assessed on a per-nodule and per-patient basis. The patients' medical records were monitored until November 2023.

resultsA total of 269 nodules were identified in 129 patients. With DL-CAD assistance, the readers' sensitivity significantly improved (65% vs. 80% for reader 1; 82% vs. 86% for reader 2; all

conclusionDL-CAD based on thin-section images can assist less experienced readers in detecting pulmonary nodules on CAC-scoring CT scans, improving detection sensitivity without significantly increasing false-positives.

Indexed as

Deep LearningImage Interpretation, Computer-AssistedMultiple Pulmonary NodulesSolitary Pulmonary NoduleTomography, X-Ray ComputedAgedCalciumCoronary VesselsFeasibility StudiesFemaleHumansMaleMiddle AgedRetrospective StudiesSensitivity and SpecificityCalciumComputer-aided diagnosisdeep learningdiagnostic performancelungtomographyX-ray computed

Identifiers

PMID40134084
PMCPMC11955396

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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.