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Record W2488715786 · doi:10.21037/jtd.2016.s021

AB021. Validation of real-world, non-research thoracic CT scans for quantitative analysis of COPD

2016· article· en· W2488715786 on OpenAlexaff
Ronald J. Dandurand, Myriam Dandurand, Raúl San Jośe Estépar, Jean Bourbeau, David H. Eidelman

Bibliographic record

VenueJournal of Thoracic Disease · 2016
Typearticle
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsMedicineCOPDMedical physicsRadiologyInternal medicine

Abstract

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Background: Quantitative CT (QCT) imaging plays an important role in phenotyping COPD and uses the voxel density histogram to measure total lung volume (TLV) and emphysema surrogates: low attenuation area (LAA) and lung density (LD). LD is often volume corrected using the predicted total lung capacity (TLC) to compensate for submaximal inspiration prior to image acquisition. QCT is carried out with careful attention to quality control including scanner make/model, calibration frequency, lung volume, acquisition protocol, and the use of contrast, and bears a financial and radiation cost. We wished to determine if: (I) thoracic CT scans acquired for clinical indications on a variety of scanners from different centres with varying calibration frequency, acquisition protocols and only simple breath holding instructions could yield reproducible data; (II) volume correcting LAA and LD using the pulmonary function test (PFT) measured TLC would compensate for submaximal inspiration better than using the predicted TLC; and (III) contrast infusion causes predictable changes in the QCT metrics TLV, LAA and LD. Methods: A total of 82 subjects (67 COPD, 15 non-COPD) from a community respirology practice had at least 2 CT scans judged free of significant infiltrates, performed on 10 different models of scanner in 7 different community hospitals or radiology centres for clinical indications within a 13-month period and had pulmonary function tests performed respecting ATS criteria within 14 months of at least 1 CT scan. Images were analysed with Airway Inspector in ITALIC FONT (airwayinspector.acil-bwh.org) for LAA [<-950 Hounsfield Unit (HU)], LD (at 15th percentile + 1,000 HU) and TLV. 46 paired non-contrast scans (NC/NC) and 42 paired contrast/non-contrast scans (C/NC, 23 CT angio with early infusion, 19 routine contrast with late infusion) were used to construct identity plots for TLV, LAA, LD, and LAA and LD corrected for both predicted TLC and PFT measured TLC. LAA was volume corrected (VC) using the formula LAAVC = LAA × TLC/TLV and LD using LDVC = LD × TLV/TLC, where TLC was either the predicted TLC (PTLC) or PFT measured TLC (MTLC). Regression line slopes and Pearson’s r and P values were calculated for identity plots. Paired Student t-tests were used to detect differences in group mean TLV, LAA and LD, natural log transformed if necessary, between CT scan pairs. Significance was set at P<0.05 after Bonferroni correction. The study had local IRB approval. Results: NC/NC inter-scan and CT-PFT intervals were 5.5 months ± 3.7 SD and 4.6±3.9 respectively, and C/NC 5.9±3.9 and 5.2±4.9 respectively. NC/NC identity plot slope, r and P values, and CT scan 1 vs. 2 mean/median and Student t-test p values respectively were; TLV: 0.93, 0.97, P<0.001, 5.32±1.42 vs. 5.38L±1.48 L, 0.22; LAA: 1.01, 0.95, P<0.001, 0.03 (0.01‒0.09 IQR) vs. 0.03 (0.01‒0.08), 0.81; LAAVC-PTLC: 1.01, 0.95, P<0.001, 0.03 (0.01‒0.08) vs. 0.03 (0.01‒0.07), 0.95; LAAVC-MTLC: 1.02, 0.95, P<0.001, 0.03 (0.01‒0.10) vs. 0.04 (0.01‒0.09), 0.99; LD: 0.98, 0.96, P<0.001, 80±27 vs. 82±28 g/L, 0.15; LDVC-PTLC: 1.00, 0.97, P<0.001, 82±26 vs. 83±27, 0.30; LDVC-MTLC: 1.03, 0.97, P<0.001, 68±23 vs. 69±25, 0.38; and for C/NC; TLV: 0.90, 0.91, P<0.001, 5.04±1.41 vs. 5.52±1.41 L, P<0.001; LAA: 0.67, 0.94, P<0.001, 0.03 (0.01‒0.06) vs. 0.05 (0.01‒0.10), P<0.001; LAAVC-PTLC: 0.74, 0.94, P<0.001, 0.02 (0.01‒0.05) vs. 0.04 (0.01‒0.08), P<0.001; LAAVC-MTLC: 0.75, 0.94, P<0.001, 0.03 (0.01‒0.07) vs. 0.05 (0.01‒0.11), P<0.001; LD: 1.21, 0.96, P<0.001, 98±40 vs. 79±31, P<0.001; LDVC-PTLC: 1.12, 0.96, P<0.001, 92±27 vs. 81±23, P<0.001; LDVC-MTLC: 1.10, 0.98, P<0.001, 79±30 vs. 70±26, P<0.001. For NC/NC, there is a high degree of reproducibility between scans for both LAA and LD with regression line slopes close to unity, Pearson’s r values between 0.95 and 0.97 and P<0.001 for all. The means of the lnLAA and LD do not differ and appear to become more similar with a step-wise increase in the Student t-test p values as lung volume correction proceeds from not correcting, to correcting using PTLC, to correcting using MTLC. On the other hand, while the regression line slopes of the C/NC correlate very strongly with Pearson’s r values between 0.94 and 0.98 and P<0.001 for all, they progressively move toward unity as volume correction proceeds from not correcting, to correcting using PTLC to correcting using MTLC. The means of the lnLAA and LD remain significantly different between C/NC (P<0.001 for all) regardless of the lung volume correction method. Conclusions: Real-world, non-contrast thoracic CT scans can provide reproducible QCT data. LAA and LD seem more reproducible when volume corrected using the PFT measured TLC than when using the predicted TLC and suggests the former better compensates for submaximal inspiration prior to image acquisition. Contrast infusion has predictable effects on QCT metrics decreasing LAA by 33% and increasing and LD by 21%. Volume correcting using the PFT measured TLC reduces the contrast effect on LAA to 25% and LD to 10%. If validated by other centres, these findings suggest the pool of observational QCT data could be vastly expanded at little dollar and no radiation cost.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.002

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 head0.080
GPT teacher head0.471
Teacher spread0.391 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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".

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Citations0
Published2016
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