Evidence map›Paper›PMID 32940850›Full record

ArticleJapanese journal of radiology2021

Incorporation of a computer-aided vessel-suppression system to detect lung nodules in CT images: effect on sensitivity and reading time in routine clinical settings.

Taku Takaishi, Yoshiyuki Ozawa, Yuya Bando, Akiko Yamamoto, Sachiko Okochi, Hirochika Suzuki, Yuta Shibamoto

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Article in Japanese journal of radiology, 2021. 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
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  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

7 authors.

Taku TakaishiKonan Kosei Hospital, Takayacho-Omatsubara 137, Konan, Aichi, Japan. jomo.oze@gmail.com.ORCID http://orcid.org/0000-0002-2105-5268
Yoshiyuki OzawaDepartment of Radiology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan.
Yuya BandoKonan Kosei Hospital, Takayacho-Omatsubara 137, Konan, Aichi, Japan.
Akiko YamamotoKonan Kosei Hospital, Takayacho-Omatsubara 137, Konan, Aichi, Japan.
Sachiko OkochiKonan Kosei Hospital, Takayacho-Omatsubara 137, Konan, Aichi, Japan.
Hirochika SuzukiKonan Kosei Hospital, Takayacho-Omatsubara 137, Konan, Aichi, Japan.
Yuta ShibamotoDepartment of Radiology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo evaluate whether a computer-aided vessel-suppression system improves lung nodule detection in routine clinical settings. MATERIALS AND

methodsWe used computer software that automatically suppresses pulmonary vessels on chest CT while preserving pulmonary nodules. Sixty-one chest CT images were included in our study. Three radiologists independently read either standard CT images alone or both computer-aided CT and standard CT images randomly to detect a pulmonary nodule ≥ 4 mm in diameter. After an interval of at least 15 days to avoid recall bias, the three radiologists interpreted the counterpart images of the same patients. The reference standard was decided by an expert panel. The primary endpoint was sensitivity. The secondary endpoint was interpretation time.

resultsThe average sensitivity improved with computer-aided CT (72% for standard CT vs. 84% for computer-aided CT, p = 0.02). There was no difference in the false-positive rate (21% for both standard CT and computer-aided CT, p = 0.98). Although the average reading time was 9.5% longer for computer-aided plus standard CT compared with standard CT alone, the difference was not significant (p = 0.11).

conclusionVessel-suppressed CT images helped radiologists to improve the sensitivity of pulmonary nodule detection without compromising the false-positive rate.

Indexed as

Radiographic Image Interpretation, Computer-AssistedTomography, X-Ray ComputedAdolescentAdultAgedAged, 80 and overFemaleHumansLungLung NeoplasmsMaleMiddle AgedMultiple Pulmonary NodulesSensitivity and SpecificitySoftwareSolitary Pulmonary NoduleComputer softwareLungRadiologistsTomographyWorkflow

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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.