Evidence map›Paper›PMID 33778635›Full record

ArticleRadiology. Cardiothoracic imaging2020

Undetected Lung Cancer at Posteroanterior Chest Radiography: Potential Role of a Deep Learning-based Detection Algorithm.

Ju Gang Nam, Eui Jin Hwang, Da Som Kim, Seung-Jin Yoo, Hyewon Choi, Jin Mo Goo, Chang Min Park

Registry-linked trialAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Radiology. Cardiothoracic imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05489471 (A Prospective Study to Assess the Impact of an Artificial Intelligence System on Reporting of Chest X-rays, Evaluate the Ability of AI Driven Worklists to Improve Reporting Times and Improve Same Day CT Pathway for Suspected Lung Cancer), which is not on this map. Cited by 20 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing 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.

NCT05489471 unknown statusnot on this mapstarted 2023, after this paper: background citation

A Prospective Study to Assess the Impact of an Artificial Intelligence System on Reporting of Chest X-rays, Evaluate the Ability of AI Driven Worklists to Improve Reporting Times and Improve Same Day CT Pathway for Suspected Lung Cancer

TypeobservationalSponsorHull University Teaching Hospitals NHS TrustRan2023 to 2023Enrolled20,000ConditionsArtificial IntelligenceArmsArtificial intelligence review
3 · Its place in the literature

Who cites it

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

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  16. Normal blindness: when we Look But Fail To See.Trends in cognitive sciences · 2022
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Ju Gang NamDepartment of Radiology, Seoul National University Hospital and College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea (J.G.N., E.J.H., D.S.K., S.J.Y., H.C., J.M.G., C.M.P.); and Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea (J.M.G., C.M.P.).ORCID 0000-0003-3991-4523
Eui Jin HwangDepartment of Radiology, Seoul National University Hospital and College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea (J.G.N., E.J.H., D.S.K., S.J.Y., H.C., J.M.G., C.M.P.); and Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea (J.M.G., C.M.P.).ORCID 0000-0002-3697-5542
Da Som KimDepartment of Radiology, Seoul National University Hospital and College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea (J.G.N., E.J.H., D.S.K., S.J.Y., H.C., J.M.G., C.M.P.); and Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea (J.M.G., C.M.P.).ORCID 0000-0002-8501-9389
Seung-Jin YooDepartment of Radiology, Seoul National University Hospital and College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea (J.G.N., E.J.H., D.S.K., S.J.Y., H.C., J.M.G., C.M.P.); and Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea (J.M.G., C.M.P.).ORCID 0000-0002-0779-3889
Hyewon ChoiDepartment of Radiology, Seoul National University Hospital and College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea (J.G.N., E.J.H., D.S.K., S.J.Y., H.C., J.M.G., C.M.P.); and Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea (J.M.G., C.M.P.).ORCID 0000-0003-3735-6791
Jin Mo GooDepartment of Radiology, Seoul National University Hospital and College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea (J.G.N., E.J.H., D.S.K., S.J.Y., H.C., J.M.G., C.M.P.); and Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea (J.M.G., C.M.P.).ORCID 0000-0003-1791-7942
Chang Min ParkDepartment of Radiology, Seoul National University Hospital and College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea (J.G.N., E.J.H., D.S.K., S.J.Y., H.C., J.M.G., C.M.P.); and Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea (J.M.G., C.M.P.).

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo evaluate the performance of a deep learning-based algorithm in detecting lung cancers not reported on posteroanterior chest radiographs during routine practice. MATERIALS AND

methodsThe retrospective test dataset included 168 posteroanterior chest radiographs acquired between March 2017 and December 2018 (168 patients; mean age, 71.9 years ± 9.5 [standard deviation]; age range, 42-91 years) with 187 lung cancers (mean size, 2.3 cm ± 1.2) undetected during initial clinical evaluation, and 50 normal chest radiographs. CT served as the reference standard for ground truth. Four thoracic radiologists independently reevaluated the chest radiographs for lung nodules both without and with the aid of the algorithm. The performances of the algorithm and the radiologists were evaluated and compared on a per-chest radiograph basis and a per-lesion basis, according to the area under the receiver operating characteristic curve (AUROC) and area under the jackknife free-response ROC curve (AUFROC).

resultsThe algorithm showed excellent diagnostic performances both in terms of per-chest radiograph classification (AUROC, 0.899) and per-lesion localization (AUFROC, 0.744); both of these values were significantly higher than those of the radiologists (AUROC, 0.634-0.663; AUFROC, 0.619-0.651;

conclusionA deep learning-based nodule detection algorithm showed excellent detection performance of lung cancers that were not reported on chest radiographs during routine practice and significantly reduced reading errors when used as a second reader.

Identifiers

PMID33778635
PMCPMC7977930

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