Evidence map›Paper›PMID 30915563›Full record

Observational studyEuropean radiology2019

Preoperative assessment of parietal pleural invasion/adhesion of subpleural lung cancer: advantage of software-assisted analysis of 4-dimensional dynamic-ventilation computed tomography.

Tsuneo Yamashiro, Hiroshi Moriya, Maho Tsubakimoto, Yukihiro Nagatani, Tatsuya Kimoto, Sadayuki Murayama, ACTIve Study Group investigators

Abstract readObservational Study
PubMed Publisher
In one paragraph

Observational study in European radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
1.2field-weighted citation impact, top 19% of its field
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

12 citing papers in PubMed, 18 citations in OpenAlex.

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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 at 3 institutions in 1 country.

Tsuneo YamashiroDepartment of Radiology, Graduate School of Medical Science, University of the Ryukyus, 207 Uehara, Nishihara, Okinawa, 903-0215, Japan. clatsune@yahoo.co.jp.ORCID http://orcid.org/0000-0002-1904-2548
Hiroshi MoriyaDepartment of Radiology, Ohara General Hospital, Fukushima City, Fukushima, Japan.
Maho TsubakimotoDepartment of Radiology, Graduate School of Medical Science, University of the Ryukyus, 207 Uehara, Nishihara, Okinawa, 903-0215, Japan.
Yukihiro NagataniDepartment of Radiology, Shiga University of Medical Science, Otsu, Shiga, Japan.
Tatsuya KimotoHealthcare IT Development Center, Canon Medical Systems Corporation, Otawara, Tochigi, Japan.
Sadayuki MurayamaDepartment of Radiology, Graduate School of Medical Science, University of the Ryukyus, 207 Uehara, Nishihara, Okinawa, 903-0215, Japan.
ACTIve Study Group investigators
University of the Ryukyus · JPCanon (Japan) · JPShiga University of Medical Science · JP

Funding

Japan Society for the Promotion of Science Kakenhi-16K19837
6 · The paper itself

Abstract

objectiveTo evaluate the accuracy of four-dimensional (4D) dynamic-ventilation computed tomography (CT) scanning coupled with our novel image analysis software to diagnose parietal pleural invasion/adhesion of peripheral (subpleural) lung cancer.

methodsEighteen patients with subpleural lung cancer underwent both 4D dynamic-ventilation CT during free breathing and conventional (static) chest CT during preoperative assessment. The absence of parietal pleural invasion/adhesion was surgically confirmed in 13 patients, while the presence of parietal pleural invasion/adhesion was confirmed in 5 patients. Two chest radiologists, who were blinded to patient status, cooperatively evaluated the presence of pleural invasion/adhesion using two different imaging modalities: (i) conventional high-resolution CT images, reconstructed in the axial, coronal, and sagittal directions, and (ii) 4D dynamic-ventilation CT images combined with a color map created by image analysis software to visualize movement differences between the lung surface and chest wall. Parameters of diagnostic accuracy were assessed, including a receiver operating characteristic analysis.

resultsSoftware-assisted 4D dynamic-ventilation CT images achieved perfect diagnostic accuracy for pleural invasion/adhesion (sensitivity, 100%; specificity, 100%; area under the curve [AUC], 1.000) compared to conventional chest CT (sensitivity, 60%; specificity, 77%; AUC, 0.846).

conclusionSoftware-assisted 4D dynamic-ventilation CT can be considered as a novel imaging approach for accurate preoperative analysis of pleural invasion/adhesion of peripheral lung cancer. KEY POINTS: • 4D dynamic-ventilation CT can correctly assess parietal pleural invasion/adhesion of peripheral lung cancer. • A unique color map clearly demonstrates parietal pleural invasion/adhesion. • Our technique can be expanded to diagnose "benign" pleural adhesions for safer thoracoscopic surgery.

Indexed as

AgedAged, 80 and overFemaleFour-Dimensional Computed TomographyHumansLungLung NeoplasmsMaleMiddle AgedNeoplasm InvasivenessPleural NeoplasmsPreoperative CareRespirationRetrospective StudiesROC CurveSensitivity and SpecificityComputer-assisted image analysisFour-dimensional computed tomographyLung cancer

Identifiers

PMID30915563
OpenAlexW2923368451

What OpenQuestion holds

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Registered trials

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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.