Evidence map›Paper›PMID 26334510›Full record

ArticleEuropean radiology2016

Asbestosis and other pulmonary fibrosis in asbestos-exposed workers: high-resolution CT features with pathological correlations.

Hiroaki Arakawa, Takumi Kishimoto, Kazuto Ashizawa, Katsuya Kato, Kenzo Okamoto, Koichi Honma, Seiji Hayashi, Masanori Akira

Abstract readMulticenter Study
PubMed Publisher
In one paragraph

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 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
2.4field-weighted citation impact, top 11% 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

10 citing papers in PubMed, 1 synthesis or guideline pooled it, 35 citations in OpenAlex.

  1. Brazilian Thoracic Society recommendations for the diagnosis and monitoring of asbestos-exposed individuals.Jornal brasileiro de pneumologia : publicacao oficial da Sociedade Brasileira de Pneumologia e Tisilogia · 2024
    Guideline
  2. Article
  3. Review
  4. Review
  5. Article
  6. Mechanisms of carbon nanotube-induced pulmonary fibrosis: a physicochemical characteristic perspective.Wiley interdisciplinary reviews. Nanomedicine and nanobiotechnology · 2018
    Review
  7. Type 2 immunity in tissue repair and fibrosis.Nature reviews. Immunology · 2018
    Review
  8. Article
  9. Article
  10. Review
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

8 authors at 5 institutions in 1 country.

Hiroaki ArakawaDepartment of Radiology, Dokkyo Medical University, 880, Kita-Kobayashi, Mibu, Tochigi, 321-0293, Japan. arakawa@dokkyomed.ac.jp.
Takumi KishimotoAsbestos Research Center, Okayama Rosai Hospital, 1-10-25 Chikkomidorimachi Ninamiku, Okayama, 702-8055, Japan.
Kazuto AshizawaDepartment of Clinical Oncology, Nagasaki University Graduate School of Biomedical Sciences, 1-7-1 Sakamoto, Nagasaki, 852-8501, Japan.
Katsuya KatoDepartment of Diagnostic Radiology 2, Kawasaki Medical School, 2-1-80, Nakasange, Kita-ku, Okayama, 701-0114, Japan.
Kenzo OkamotoDepartment of Pathology, Hokkaido Chuo Hospital, 4 Joh Higashi 16, Iwamizawa, Hokkaido, 068-0004, Japan.
Koichi HonmaDepartment of Pathology, Dokkyo Medical University, 880, Kita-Kobayashi, Mibu, Tochigi, 321-0293, Japan.
Seiji HayashiNational Hospital Organization Kinki-Chuo Chest Medical Center, 1180 Nagasone-cho, Kita-ku, Sakai-city, Osaka, 591-8555, Japan.
Masanori AkiraDepartment of Radiology, National Hospital Organization Kinki-Chuo Chest Medical Center, Osaka, Japan.
Dokkyo Medical University · JPNational Kinki Chuo Hospital for Chest Disease · JPKawasaki Medical School · JPNagasaki University · JPOkayama Rosai Hospital · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThe purpose was to identify distinguishing CT features of pathologically diagnosed asbestosis, and correlate diagnostic confidence with asbestos body burden.

methodsThirty-three workers (mean age at CT: 73 years) with clinical diagnoses of asbestosis, who were autopsied (n = 30) or underwent lobectomy (n = 3), were collected. Two radiologists independently scored high-resolution CT images for various CT findings and the likelihood of asbestosis was scored. Two pathologists reviewed the pathology specimens and scored the confidence of their diagnoses. Asbestos body count was correlated with CT and pathology scores.

resultsPathologically, 15 cases were diagnosed as asbestosis and 18 cases with various lung fibroses other than asbestosis. On CT, only the score of the subpleural curvilinear lines was significantly higher in asbestosis (p = 0.03). Accuracy of CT diagnosis of asbestosis with a high confidence ranged from 0.73 to 0.79. Asbestos body count positively correlated with CT likelihood of asbestosis (r = 0.503, p = 0.003), and with the confidence level of pathological diagnosis (r = 0.637, p < 0.001).

conclusionsSubpleural curvilinear lines were the only clue for the diagnosis of asbestosis. However, this was complicated by other lung fibrosis, especially at low asbestos body burden. KEY POINTS: • Various patterns of pulmonary fibrosis occurred in asbestos-exposed workers. • The fibre burden in lungs paralleled confident CT diagnosis of asbestosis. • The fibre burden in lungs paralleled confident pathological diagnosis of asbestosis. • Subpleural curvilinear lines were an important CT finding favouring asbestosis.

Indexed as

AgedAsbestosAsbestosisAutopsyBody BurdenEnvironmental ExposureFemaleHumansLungMaleMiddle AgedObserver VariationPulmonary FibrosisRetrospective StudiesTomography, X-Ray ComputedAsbestosAsbestosAsbestosisChronic interstitial pneumoniaComputed tomography, radiographyPulmonary fibrosis

Identifiers

PMID26334510
OpenAlexW1752879629

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.