Evidence map›Paper›PMID 39665633›Full record

Observational studyRadiology. Cardiothoracic imaging2024

Evaluating the Cumulative Benefit of Inspiratory CT, Expiratory CT, and Clinical Data for COPD Diagnosis and Staging through Deep Learning.

Amanda N Lee, Albert Hsiao, Kyle A Hasenstab

Registry-linked trialAbstract readObservational Study
PubMed Full text at PMC Publisher
In one paragraph

Observational study in Radiology. Cardiothoracic imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT00608764 (Genetic Epidemiology of Chronic Obstructive Pulmonary Disease), which is not on this map. Cited by 3 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 2 pooled it
–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.

NCT00608764 active not recruitingnot on this map

Genetic Epidemiology of Chronic Obstructive Pulmonary Disease (COPDGene)

TypeobservationalSponsorBrigham and Women's HospitalRan2007 to 2028Enrolled10,718ConditionsPulmonary Disease, Chronic Obstructive, Emphysema, Bronchitis, Chronic
3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
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

3 authors.

Amanda N LeeFrom the Computational Science Research Center, San Diego State University, San Diego, Calif (A.N.L.); Department of Radiology, University of California San Diego, La Jolla, Calif (A.H.); and Department of Mathematics and Statistics, San Diego State University, 5500 Campanile Dr, San Diego, CA 92182 (K.A.H.).ORCID 0000-0003-2509-128X
Albert HsiaoFrom the Computational Science Research Center, San Diego State University, San Diego, Calif (A.N.L.); Department of Radiology, University of California San Diego, La Jolla, Calif (A.H.); and Department of Mathematics and Statistics, San Diego State University, 5500 Campanile Dr, San Diego, CA 92182 (K.A.H.).
Kyle A HasenstabFrom the Computational Science Research Center, San Diego State University, San Diego, Calif (A.N.L.); Department of Radiology, University of California San Diego, La Jolla, Calif (A.H.); and Department of Mathematics and Statistics, San Diego State University, 5500 Campanile Dr, San Diego, CA 92182 (K.A.H.).ORCID 0000-0002-4687-960X

Funding

Genetic Epidemiology of COPDU01HL089897 · NHLBI · NATIONAL JEWISH HEALTH · PI CRAPO, JAMES D · 2007 to 2021
$56.9M
Metabolomic signatures of empysema and COPD progression in the COPDGene cohortR01HL089897 · NHLBI · NATIONAL JEWISH HEALTH · PI CRAPO, JAMES D · 2012 to 2016
$31.1M
Genetic Epidemiology of COPDU01HL089856 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI SILVERMAN, EDWIN K · 2007 to 2021
$20.7M
NHLBI NIH HHS R01 HL089897NHLBI NIH HHS U01 HL089856NHLBI NIH HHS U01 HL089897
6 · The paper itself

Abstract

Purpose To measure the benefit of single-phase CT, inspiratory-expiratory CT, and clinical data for convolutional neural network (CNN)-based chronic obstructive pulmonary disease (COPD) staging. Materials and Methods This retrospective study included inspiratory and expiratory lung CT images and spirometry measurements acquired between November 2007 and April 2011 from 8893 participants (mean age, 59.6 years ± 9.0 [SD]; 53.3% [4738 of 8893] male) in the COPDGene phase I cohort (ClinicalTrials.gov: NCT00608764). CNNs were trained to predict spirometry measurements (forced expiratory volume in 1 second [FEV

Indexed as

Deep LearningInhalationPulmonary Disease, Chronic ObstructiveTomography, X-Ray ComputedAgedExhalationFemaleHumansLungMaleMiddle AgedRetrospective StudiesSpirometryAttention MapChronic Obstructive Pulmonary DiseaseConvolutional Neural NetworkCTSeverity Staging

Identifiers

PMID39665633
PMCPMC11683208

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

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.