Evidence map›Paper›PMID 26645864›Full record

ArticleEuropean radiology2016

Diagnostic accuracy of low-mA chest CT reconstructed with Model Based Iterative Reconstruction in the detection of early pleuro-pulmonary complications following a lung transplantation.

Marie-Pierre Debray, Gaëlle Dauriat, Antoine Khalil, Sebastien Leygnac, Sarah Tubiana, Albane Grandjean, Elisabeth Schouman-Claeys, Jean-Pierre Laissy, Phalla Ou

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Article in European radiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.2field-weighted citation impact, top 20% 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

7 citing papers in PubMed, 17 citations in OpenAlex.

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  6. Assessment of chest CT at CTDIThe British journal of radiology · 2017
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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

9 authors at 7 institutions in 1 country.

Marie-Pierre DebrayDepartment of Radiology, AP-HP, Bichat-Claude Bernard Hospital, Paris, France. marie-pierre.debray@aphp.fr.
Gaëlle DauriatDepartment of Pneumology B, AP-HP, Bichat-Claude Bernard Hospital, Paris, France.
Antoine KhalilDepartment of Radiology, AP-HP, Bichat-Claude Bernard Hospital, Paris, France.
Sebastien LeygnacDepartment of Radiology, AP-HP, Bichat-Claude Bernard Hospital, Paris, France.
Sarah TubianaAP-HP, Centre de Pharmaco-épidémiologie, Pôle Santé Publique, Recherche Clinique et Information Médicale, Hôpitaux Universitaires Paris-Nord Val de Seine, Site Bichat, Paris, France.
Albane GrandjeanGeneral Electric Medical Systems, Buc, France.
Elisabeth Schouman-ClaeysDepartment of Radiology, AP-HP, Bichat-Claude Bernard Hospital, Paris, France.
Jean-Pierre LaissyDepartment of Radiology, AP-HP, Bichat-Claude Bernard Hospital, Paris, France.
Phalla OuDepartment of Radiology, AP-HP, Bichat-Claude Bernard Hospital, Paris, France.
Université Claude Bernard Lyon 1 · FRDélégation Paris 7 · FRGeneral Electric (France) · FRHôpital Bichat-Claude-Bernard · FRHôpitaux Universitaires Paris-Ouest · FRSorbonne Paris Cité · FRUniversité Paris Cité · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo assess the accuracy of reduced-dose, low-mA chest CT (RD-CT) reconstructed with model-based iterative reconstruction (MBIR) in detecting usual early complications following pulmonary transplantation, as compared to standard-dose chest CT (SD-CT) reconstructed with adaptative statistical iterative reconstruction (ASIR).

methodsOur institutional review board approved this prospective study and patients provided written informed consent. Two thoracic radiologists independently evaluated 47 examinations performed routinely in 20 patients during 6 months following lung transplantation for the detection and/or evolution of usual pleuropulmonary complications and for subjective image quality. Each examination consisted of successive acquisition of unenhanced SD-CT (100-120 kV, noise index 45, ASIR) and RD-CT (100 kV, 16-24mAs/slice, MBIR).

resultsMean CTDIvol was 4.12 ± 0.88 and 0.65 ± 0.09 mGy for SD-CT and RD-CT, respectively. Complications were found in 40/47 (85 %) examinations. Sensitivity and negative predictive value of RD-CT were 92-100 % for the detection of pneumonia, fungal infection, pleural effusion, pneumothorax, and bronchial dehiscence or stenosis, as compared to SD-CT. Image quality of RD-CT was graded good for 81 % of examinations.

conclusionsMBIR-RD-CT is accurate, as compared to SD-CT, for delineating most usual pleuropulmonary complications during the 6 months following pulmonary transplantation and might be used routinely for the early monitoring of pulmonary allografts. KEY POINTS: • Early chest complications are frequent following a pulmonary transplantation • CT has a key role for their detection and follow-up • Low-mAMBIR CT is accurate for monitoring most lung allograft early pleuropulmonary complications • MBIR chest CT allows a six-fold dose reduction compared to standard CT.

Indexed as

AdultAlgorithmsFemaleHumansLungLung DiseasesLung TransplantationMaleMiddle AgedModels, TheoreticalPleuraPleural DiseasesProspective StudiesRadiation DosageRadiography, ThoracicTomography, X-Ray ComputedAdaptative statistical iterative reconstructionComputed tomographyDoseLung transplantationModel-based iterative reconstruction

Identifiers

PMID26645864
OpenAlexW2296028877

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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.