Evidence map›Paper›PMID 40297437›Full record

ArticlemedRxiv : the preprint server for health sciences2025

Quantitative CT Scoring for Local COPD Severity.

Wassim W Labaki, Sundaresh Ram, Ali Namvar, Alexander J Bell, Benjamin A Hoff, Ella A Kazerooni, Stefanie Galban, Fernando J Martinez, Charles R Hatt, Susan Murray and 5 more

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

15 authors.

Wassim W LabakiDepartment of Internal Medicine, Division of Pulmonary and Critical Care Medicine, University of Michigan, Ann Arbor, MI, United States.
Sundaresh RamDepartment of Radiology and Imaging Sciences, Emory University and Georgia Institute of Technology, Atlanta, GA, United States.
Ali NamvarDepartment of Radiology, University of Michigan, Ann Arbor, MI, United States.
Alexander J BellDepartment of Radiology, University of Michigan, Ann Arbor, MI, United States.
Benjamin A HoffDepartment of Radiology, University of Michigan, Ann Arbor, MI, United States.
Ella A KazerooniDepartment of Radiology, University of Michigan, Ann Arbor, MI, United States.
Stefanie GalbanDepartment of Radiology, University of Michigan, Ann Arbor, MI, United States.
Fernando J MartinezWeill Cornell Medical College, New York, NY, United States.
Charles R Hatt4D Medical Inc, Woodland Hills, CA, United States.
Susan MurraySchool of Public Health, University of Michigan, Ann Arbor, MI, United States.
Evgeny M MirkesSchool of Computing and Mathematical Sciences, University of Leicester, Leicester, Leicestershire, United Kingdom.
Alexander N GorbanSchool of Computing and Mathematical Sciences, University of Leicester, Leicester, Leicestershire, United Kingdom.
Andrei ZinovyevIn Silico R&D, Evotec, Toulouse, France.
MeiLan K HanDepartment of Internal Medicine, Division of Pulmonary and Critical Care Medicine, University of Michigan, Ann Arbor, MI, United States.
Craig J GalbanDepartment of Radiology, University of Michigan, Ann Arbor, MI, United States.

Funding

Genetic Epidemiology of COPDU01HL089897 · NHLBI · NATIONAL JEWISH HEALTH · PI CRAPO, JAMES D · 2007 to 2021
$56.9M
GENETIC EPIDEMIOLOGY OF COPD (COPD GENE) TASK A: STUDY VISIT 4, COLLECTION OF COPDGENE STUDY DATA ANDBIOSPECIMENS AND OVERSIGHT OF THE COPDGENE STUDY75N92023D00011 · NHLBI · NATIONAL JEWISH HEALTH · PI NEWMAN, LEE S · 2023 to 2025
$29.6M
Genetic Epidemiology of COPDU01HL089856 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI SILVERMAN, EDWIN K · 2007 to 2021
$20.7M
Prediction of COPD Progression by PRMR01HL150023 · NHLBI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI GALBAN, CRAIG J, HAN, MEILAN K · 2020 to 2023
$2.8M
NHLBI NIH HHS 75N92023D00011NHLBI NIH HHS R01 HL150023NHLBI NIH HHS U01 HL089856NHLBI NIH HHS U01 HL089897
6 · The paper itself

Abstract

Chronic obstructive pulmonary disease (COPD) is complex, and its course is difficult to predict due to its diverse pathophysiology. Small airway disease (SAD), a key component of COPD and potential target for emerging therapeutics, may be reversible in mild COPD, but left unchecked, may worsen, leading to airway loss and emphysema. The dual nature of SAD complicates clinical management of COPD patients, necessitating more accurate monitoring methods. To meet this need, we developed elastic Parametric Response Mapping (ePRM), a tiered scoring system that classifies local lung volumes by the degree of PRM-derived SAD, normal, and emphysematous tissue. In individuals with or at risk for COPD, we demonstrate that chest CT ePRM can categorize local lung tissue into distinct tiers of disease severity that distinguish between tissue characterized by early reversible SAD and progressive destruction. This level of characterization is crucial to developing personalized treatment strategies for COPD.

Indexed as

chronic obstructive pulmonary diseasecomputed tomography of the chestelastic principal graphemphysemamachine learningparametric response mappingsmall airways disease

Identifiers

PMID40297437
PMCPMC12036413

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.