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Record W3007250251 · doi:10.1148/radiol.2020191735

Pulmonary Imaging Phenotypes of Chronic Obstructive Pulmonary Disease Using Multiparametric Response Maps

2020· article· en· W3007250251 on OpenAlexafffund
Jonathan L. MacNeil, Dante P. I. Capaldi, Andrew Westcott, Rachel L. Eddy, Andrea L. Barker, David G. McCormack, Miranda Kirby, Grace Párraga

Bibliographic record

VenueRadiology · 2020
Typearticle
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsToronto Metropolitan UniversityWestern University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsMedicineCOPDPulmonary diseaseGold standard (test)Pulmonary function testingNuclear medicineObstructive lung diseaseVoxelProspective cohort studyEffective diffusion coefficientLung volumesInternal medicineLungRadiologyCardiologyMagnetic resonance imaging

Abstract

fetched live from OpenAlex

Background Pulmonary imaging of chronic obstructive pulmonary disease (COPD) has focused on CT or MRI measurements, but these have not been evaluated in combination. Purpose To generate multiparametric response map (mPRM) measurements in ex-smokers with or without COPD by using volume-matched CT and hyperpolarized helium 3 (3He) MRI. Materials and Methods In this prospective study (https://clinicaltrials.gov, NCT02279329), participants underwent MRI and CT and completed pulmonary function tests, questionnaires, and the 6-minute walk test between December 2010 and January 2019. Disease status was determined by using Global initiative for chronic Obstructive Lung Disease (GOLD) criteria. The mPRM voxel values were generated by using co-registered MRI and CT labels. Kruskal-Wallis and Bonferroni tests were used to determine differences across disease severity, and correlations were determined by using Spearman coefficients. Results A total of 175 ex-smokers (mean age, 69 years ± 9 [standard deviation], 108 men) with or without COPD were evaluated. Ex-smokers without COPD had a larger fraction of normal mPRM voxels (60% vs 37%, 20%, and 7% for GOLD I, II, and III/IV disease, respectively; all P ≤ .001) and a smaller fraction of abnormal voxels, including small airways disease (normal CT, not ventilated: 5% vs 6% [not significant], 11%, and 19% [P ≤ .001 for both] for GOLD I, II, and III/IV disease, respectively) and mild emphysema (normal CT, abnormal apparent diffusion coefficient [ADC]: 33% vs 54%, 56%, and 54% for GOLD I, II, and III/IV disease respectively; all P ≤ .001). Normal mPRM measurements were positively correlated with forced expiratory volume in 1 second (FEV1) (r = 0.65, P < .001), the FEV1-to–forced vital capacity ratio (r = 0.81, P < .001), and diffusing capacity (r = 0.75, P < .001) and were negatively correlated with worse quality of life (r = −0.48, P < .001). Abnormal mPRM measurements of small airways disease (normal CT, not ventilated) and mild emphysema (normal CT, abnormal ADC) were negatively correlated with FEV1 (r = −0.65 and −0.42, respectively; P < .001) and diffusing capacity (r = −0.53 and −0.60, respectively; P < .001) and were positively correlated with worse quality of life (r = 0.45 and r = 0.33, respectively; P < .001), both of which were present in ex-smokers without COPD. Conclusion Multiparametric response maps revealed two abnormal structure-function results related to emphysema and small airways disease, both of which were unexpectedly present in ex-smokers with normal spirometry and CT findings. © RSNA, 2020 Online supplemental material is available for this article.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.374
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.027
GPT teacher head0.299
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations31
Published2020
Admission routes2
Has abstractyes

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