Evidence map›Paper›PMID 27031615›Full record

ArticlePloS one2016

Normal Lung Quantification in Usual Interstitial Pneumonia Pattern: The Impact of Threshold-based Volumetric CT Analysis for the Staging of Idiopathic Pulmonary Fibrosis.

Hirotsugu Ohkubo, Yoshihiro Kanemitsu, Takehiro Uemura, Osamu Takakuwa, Masaya Takemura, Ken Maeno, Yutaka Ito, Tetsuya Oguri, Nobutaka Kazawa, Ryuji Mikami and 1 more

Erratum issuedOpen access · goldAbstract read
In one paragraph

Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 31 papers.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed
3.6field-weighted citation impact, top 7% 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

31 citing papers in PubMed, 63 citations in OpenAlex.

  1. Trial
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  13. Quantitative Analysis for Lung Disease on Thin-Section CT.Diagnostics (Basel, Switzerland) · 2023
    Review
  14. Demonstration of ameliorating effect of papaverine in sepsis-induced acute lung injury on rat model through radiology and histology.Ulusal travma ve acil cerrahi dergisi = Turkish journal of trauma & emergency surgery : TJTES · 2023
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors at 2 institutions in 1 country.

Hirotsugu OhkuboDepartment of Respiratory Medicine, Allergy and Clinical Immunology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan.
Yoshihiro KanemitsuDepartment of Respiratory Medicine, Allergy and Clinical Immunology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan.
Takehiro UemuraDepartment of Respiratory Medicine, Allergy and Clinical Immunology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan.
Osamu TakakuwaDepartment of Respiratory Medicine, Allergy and Clinical Immunology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan.
Masaya TakemuraDepartment of Respiratory Medicine, Allergy and Clinical Immunology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan.
Ken MaenoDepartment of Respiratory Medicine, Allergy and Clinical Immunology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan.
Yutaka ItoDepartment of Respiratory Medicine, Allergy and Clinical Immunology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan.
Tetsuya OguriDepartment of Respiratory Medicine, Allergy and Clinical Immunology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan.
Nobutaka KazawaDepartment of Radiology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan.
Ryuji MikamiDepartment of Radiology, Tokyo Medical University, Tokyo, Japan.
Akio NiimiDepartment of Respiratory Medicine, Allergy and Clinical Immunology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan.
Nagoya City University · JPTokyo Medical University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAlthough several computer-aided computed tomography (CT) analysis methods have been reported to objectively assess the disease severity and progression of idiopathic pulmonary fibrosis (IPF), it is unclear which method is most practical. A universal severity classification system has not yet been adopted for IPF.

objectiveThe purpose of this study was to test the correlation between quantitative-CT indices and lung physiology variables and to determine the ability of such indices to predict disease severity in IPF.

methodsA total of 27 IPF patients showing radiological UIP pattern on high-resolution (HR) CT were retrospectively enrolled. Staging of IPF was performed according to two classification systems: the Japanese and GAP (gender, age, and physiology) staging systems. CT images were assessed using a commercially available CT imaging analysis workstation, and the whole-lung mean CT value (MCT), the normally attenuated lung volume as defined from -950 HU to -701 Hounsfield unit (NL), the volume of the whole lung (WL), and the percentage of NL to WL (NL%), were calculated.

resultsCT indices (MCT, WL, and NL) closely correlated with lung physiology variables. Among them, NL strongly correlated with forced vital capacity (FVC) (r = 0.92, P <0.0001). NL% showed a large area under the receiver operating characteristic curve for detecting patients in the moderate or advanced stages of IPF. Multivariable logistic regression analyses showed that NL% is significantly more useful than the percentages of predicted FVC and predicted diffusing capacity of the lungs for carbon monoxide (Japanese stage II/III/IV [odds ratio, 0.73; 95% confidence intervals (CI), 0.48 to 0.92; P < 0.01]; III/IV [odds ratio. 0.80; 95% CI 0.59 to 0.96; P < 0.01]; GAP stage II/III [odds ratio, 0.79; 95% CI, 0.56 to 0.97; P < 0.05]).

conclusionThe measurement of NL% by threshold-based volumetric CT analysis may help improve IPF staging.

Indexed as

AgedArea Under CurveCone-Beam Computed TomographyFemaleHumansIdiopathic Pulmonary FibrosisJapanLogistic ModelsLungMaleMiddle AgedRespiratory Function TestsRetrospective StudiesROC CurveSeverity of Illness Index

Identifiers

PMID27031615
PMCPMC4816297
OpenAlexW2318967274

What OpenQuestion holds

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LicenceCC BY
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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.