Evidence map›Paper›PMID 34999920›Full record

ArticleEuropean radiology2022

Clinical impact of a deep learning system for automated detection of missed pulmonary nodules on routine body computed tomography including the chest region.

Kueian Chen, Ying-Chieh Lai, Balamuralidhar Vanniarajan, Pieh-Hsu Wang, Shao-Chung Wang, Yu-Chun Lin, Shu-Hang Ng, Pelu Tran, Gigin Lin

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Article in European radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

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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.

Kueian ChenDepartment of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Taoyuan, 33382, Guishan, Taiwan.
Ying-Chieh LaiDepartment of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Taoyuan, 33382, Guishan, Taiwan.
Balamuralidhar VanniarajanFerrumFerrum Health, Santa Clara, CA, USA.
Pieh-Hsu WangDepartment of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Taoyuan, 33382, Guishan, Taiwan.
Shao-Chung WangDepartment of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Taoyuan, 33382, Guishan, Taiwan.
Yu-Chun LinImaging Core Laboratory, Institute for Radiological Research, Chang Gung Memorial Hospital at Linkou and Chang Gung University, 5 Fuhsing St., Taoyuan, 33382, Guishan, Taiwan.
Shu-Hang NgDepartment of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Taoyuan, 33382, Guishan, Taiwan.
Pelu TranFerrumFerrum Health, Santa Clara, CA, USA.
Gigin LinDepartment of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Taoyuan, 33382, Guishan, Taiwan. giginlin@cgmh.org.tw.ORCID http://orcid.org/0000-0001-7246-1058

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo evaluate the clinical impact of a deep learning system (DLS) for automated detection of pulmonary nodules on computed tomography (CT) images as a second reader.

methodsThis single-centre retrospective study screened 21,150 consecutive body CT studies from September 2018 to February 2019. Pulmonary nodules detected by the DLS on axial CT images but not mentioned in initial radiology reports were flagged. Flagged images were scored by four board-certificated radiologists each with at least 5 years of experience. Nodules with scores of 2 (understandable miss) or 3 (should not be missed) were then categorised as unlikely to be clinically significant (2a or 3a) or likely to be clinically significant (2b or 3b) according to the 2017 Fleischner guidelines for pulmonary nodules. The miss rate was defined as the total number of studies receiving scores of 2 or 3 divided by total screened studies.

resultsAmong 172 nodules flagged by the DLS, 60 (35%) missed nodules were confirmed by the radiologists. The nodules were further categorised as 2a, 2b, 3a, and 3b in 24, 14, 10, and 12 studies, respectively, with an overall positive predictive value of 35%. Missed pulmonary nodules were identified in 0.3% of all CT images, and one-third of these lesions were considered clinically significant.

conclusionsUse of DLS-assisted automated detection as a second reader can identify missed pulmonary nodules, some of which may be clinically significant. CLINICAL RELEVANCE/APPLICATION: Use of DLS to help radiologists detect pulmonary lesions may improve patient care. KEY POINTS: • DLS-assisted automated detection as a second reader is feasible in a large consecutive cohort. • Performance of combined radiologists and DLS was better than DLS or radiologists alone. • Pulmonary nodules were missed more frequently in abdomino-pelvis CT than the thoracic CT.

Indexed as

Deep LearningLung NeoplasmsMultiple Pulmonary NodulesSolitary Pulmonary NoduleHumansRadiographic Image Interpretation, Computer-AssistedRetrospective StudiesSensitivity and SpecificityTomography, X-Ray ComputedDeep learningMissed diagnosisMultidetector computed tomographyMultiple pulmonary nodules

Identifiers

PMID34999920

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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.