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POS0886 18F-FDG PET-CT OF INTERSTITIAL LUNG DISEASE IN PATIENTS WITH EARLY SYSTEMIC SCLEROSIS

2023· article· en· W4379510710 on OpenAlexfundno aff
Bo Broens, G. C. J. Zwezerijnen, Esther J. Nossent, Lilian J. Meijboom, Maqsood Yaqub, Julia Spierings, Jeska de Vries‐Bouwstra, Jacob M. van Laar, Conny J. van der Laken, Alexandre E. Voskuyl

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicSystemic Sclerosis and Related Diseases
Canadian institutionsnot available
FundersArthritis SocietyDutch Arthritis Society
KeywordsMedicineInterstitial lung diseaseSystemic diseaseScleroderma (fungus)PET-CTPositron emission tomographyLungPathologyRadiologyNuclear medicineDiseaseInternal medicine

Abstract

fetched live from OpenAlex

Background Patients with systemic sclerosis associated interstitial lung disease (SSc-ILD) have a highly variable disease course, which makes clinical management challenging [1]. Hence, there is a clinical need for new tools to improve patient monitoring and stratification. 18F-Fluorodeoxyglucose (FDG) PET-CT of the lungs has previously shown promising results in patients with SSc-ILD by reflecting ILD activity and contributing to the prediction of lung function decline [2,3]. However, as most studies have been performed in patients with longstanding and severe ILD, there is limited information concerning the value of 18F-FDG PET-CT in the first two years after SSc diagnosis and the recognition of early ILD. Objectives To prospectively investigate the presence and severity of ILD as detected by 18F-FDG PET-CT in patients with early SSc. Methods Included patients fulfilled the 2013 ACR-EULAR classification criteria for SSc, had a disease duration ≤ 2 years (from onset of first non-Raynaud’s symptoms) and diffuse cutaneous disease. All patients underwent a high-resolution CT scan of the lungs to evaluate the presence of ILD, as part of routine clinical care, as well as pulmonary function tests. 18F-FDG PET-CT was performed in 13 patients (ILD n=9; no ILD n=4). For quantitative analysis, six volumes of interest (VOIS) of 2cm were placed in pre-specified dorsobasal lung areas, as described previously [3]. The six standardized uptake values (SUV) were processed and averaged to obtain a total SUV for the dorsobasal lung fields. To correct for inter-individual differences, all uptake values were corrected for 18F-FDG uptake in the mediastinal blood pool [3]. Statistical analysis was performed using a Mann-Whitney U test to compare for between-group differences. Results In the ILD group, 7/9 (78%) of the patients were male with an average age of 53.2 years. In the non-ILD group 2/4 patients were male (50%), with an average age of 41.3 years. All patients received immunosuppressive treatment with either Mycophenolate Mophetil (ILD 100%; no ILD 50%) or Methotrexate (ILD 0%; no ILD 50%) before inclusion. Quantitative analysis of 18F-FDG PET-CT revealed that SUVmax corrected in the dorsobasal lung fields was higher in the patients with ILD (median [range] 0.90 [0.65]) than in patients without ILD (median [range] 0.63 [0.15]; p=0.03). The higher 18F-FDG uptake in patients with ILD compared to those without ILD is visually illustrated in Figure 1. The relationship between the uptake of 18F-FDG, high-resolution CT scan of the lungs and pulmonary function tests at baseline is currently under analysis (and will be presented during the meeting). Conclusion Our results suggest that in early disease stages, within two years of SSc diagnosis, 18F-FDG uptake in the dorsobasal lung fields is higher in patients with ILD compared to patients without ILD. Further analysis is warranted to investigate 18F-FDG uptake in other lung regions, its relationship to conventional tools and with regard to treatment outcomes. As such, we will investigate repeated 18F-FDG PET-CT and clinical outcomes after 1 year of follow-up in patients with SSc-ILD. References [1]Hoffmann-Vold AM, et al. Ann Rheum Dis. 2021 Feb;80(2):219-227. [2]Broens B, et al. Autoimmun Rev. 2022 Dec;21(12):103202. [3]Peelen DM, et al. Rheumatology (Oxford). 2020 Jun 1;59(6):1407-1415. Acknowledgements: NIL. Disclosure of Interests Bo Broens: None declared, Gerben C.J. Zwezerijnen: None declared, Esther Nossent: None declared, Lilian Meijboom: None declared, Maqsood Yaqub: None declared, Julia Spierings: None declared, Jeska de Vries-Bouwstra: None declared, Jacob M. van Laar: None declared, Conny J. van der Laken Grant/research support from: Boehringer Ingelheim and the Dutch Arthritis Society (grant number 21-1-201), Alexandre Voskuyl Grant/research support from: Boehringer Ingelheim and the Dutch Arthritis Society (grant number 21-1-201)

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.012
GPT teacher head0.222
Teacher spread0.210 · 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 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".

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Citations0
Published2023
Admission routes1
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