Evidence map›Paper›PMID 31706309›Full record

ArticleBMC pulmonary medicine2019

Quantitative lung morphology: semi-automated measurement of mean linear intercept.

George Crowley, Sophia Kwon, Erin J Caraher, Syed Hissam Haider, Rachel Lam, Prag Batra, Daniel Melles, Mengling Liu, Anna Nolan

Abstract read
In one paragraph

Article in BMC pulmonary medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 81 papers.

0numbers the graph read from it
0cells of the map it votes in
81citing papers in PubMed
–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

81 citing papers in PubMed.

  1. Article
  2. Article
  3. Easily adaptable Fiji macro for mean linear intercept measurement of peripheral respiratory airspace.Developmental dynamics : an official publication of the American Association of Anatomists · 2026
    Article
  4. Article
  5. Age-related changes in respiratory system mechanics and lung morphometry.American journal of physiology. Lung cellular and molecular physiology · 2026
    Article
  6. AlveolEye: rapid and precise lung morphometry guided by computer vision.American journal of physiology. Lung cellular and molecular physiology · 2026
    Article
  7. Article
  8. Article
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  14. Article
  15. Pulmonary injury following exposure to amorphous silicon dioxide nanoparticles inExperimental biology and medicine (Maywood, N.J.) · 2026
    Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article

21 more citing papers are in PubMed but not listed here.

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.

George CrowleyDepartment of Medicine, Division of Pulmonary, Critical Care and Sleep Medicine, New York University School of Medicine, New York, NY, USA.
Sophia KwonDepartment of Medicine, Division of Pulmonary, Critical Care and Sleep Medicine, New York University School of Medicine, New York, NY, USA.
Erin J CaraherDepartment of Medicine, Division of Pulmonary, Critical Care and Sleep Medicine, New York University School of Medicine, New York, NY, USA.
Syed Hissam HaiderDepartment of Medicine, Division of Pulmonary, Critical Care and Sleep Medicine, New York University School of Medicine, New York, NY, USA.
Rachel LamDepartment of Medicine, Division of Pulmonary, Critical Care and Sleep Medicine, New York University School of Medicine, New York, NY, USA.
Prag BatraNew York University School of Medicine, New York, NY, USA.
Daniel MellesUniversity of California, Berkeley, Berkeley, CA, USA.
Mengling LiuDepartment of Environmental Medicine, New York University School of Medicine, New York, NY, USA.
Anna NolanDepartment of Medicine, Division of Pulmonary, Critical Care and Sleep Medicine, New York University School of Medicine, New York, NY, USA. Anna.Nolan@med.nyu.edu.ORCID http://orcid.org/0000-0002-0631-1171

Funding

Metabolomics of World Trade Center-Lung Injury: Biomarker Validation, Longitudinal Assessment and Dietary InterventionU01OH011300 · OH · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI NOLAN, ANNA · 2017 to 2025
$4.8M
RAGE Mediates LPA Induced Pulmonary InflammationR01HL119326 · NHLBI · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI NOLAN, ANNA · 2015 to 2019
$2.1M
ACL HHS U01OH011300NHLBI NIH HHS R01 HL119326NHLBI NIH HHS R01HL119326
6 · The paper itself

Abstract

backgroundQuantifying morphologic changes is critical to our understanding of the pathophysiology of the lung. Mean linear intercept (MLI) measures are important in the assessment of clinically relevant pathology, such as emphysema. However, qualitative measures are prone to error and bias, while quantitative methods such as mean linear intercept (MLI) are manually time consuming. Furthermore, a fully automated, reliable method of assessment is nontrivial and resource-intensive.

methodsWe propose a semi-automated method to quantify MLI that does not require specialized computer knowledge and uses a free, open-source image-processor (Fiji). We tested the method with a computer-generated, idealized dataset, derived an MLI usage guide, and successfully applied this method to a murine model of particulate matter (PM) exposure. Fields of randomly placed, uniform-radius circles were analyzed. Optimal numbers of chords to assess based on MLI were found via receiver-operator-characteristic (ROC)-area under the curve (AUC) analysis. Intraclass correlation coefficient (ICC) measured reliability.

resultsWe demonstrate high accuracy (AUC

conclusionsOur semi-automated method is reliable, equally fast as fully automated methods, and uses free, open-source software. Additionally, we quantified the optimal number of chords that should be measured per lung field.

Indexed as

Image Processing, Computer-AssistedAnimalsDisease Models, AnimalFemaleLungMiceMice, Inbred C57BLPulmonary EmphysemaReproducibility of ResultsROC CurveEmphysemaLung architectureObstructive airways disease

Identifiers

PMID31706309
PMCPMC6842138

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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.