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Hyperpolarized 129Xe MRI Captures Elevated Regional Ventilation Heterogeneity in Patients With Bronchiolitis Obliterans Syndrome Following Hematopoietic Stem-cell Transplantation in Children

2025· article· en· W4410272994 on OpenAlexaff
Faiyza Alam, Brandon Zanette, D. Li, Sharon Braganza, Moaath K. Mustafa Ali, Félix Ratjen, David Manson, Tal Schechter‐Finkelstein, Joerg Krueger, G. Santyr

Bibliographic record

VenueAmerican Journal of Respiratory and Critical Care Medicine · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAtomic and Subatomic Physics Research
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsBronchiolitis obliteransMedicineHematopoietic stem cell transplantationBronchiolitisTransplantationStem cellHematopoietic cellHaematopoiesisPathologyRespiratory systemInternal medicineLung transplantationGenetics

Abstract

fetched live from OpenAlex

Abstract Rationale: Bronchiolitis Obliterans Syndrome (BOS) is a lung complication of Hematopoietic Stem-Cell Transplantation (HSCT), often with delayed diagnosis as early stages are typically asymptomatic. Once symptoms meet National Institute of Health (NIH) criteria, airway obstruction is typically irreversible. Single-breath Hyperpolarized Xenon MRI (Xe-MRI) detects early pulmonary decline in cystic fibrosis (CF) and was feasible in pediatric HSCT patients. Additionally, it is sensitive to ventilation defects even in asymptomatic post-HSCT subjects with normal FEV1 and those unable to perform spirometry. Xe-MRI can also be done in a multiple-breath washout (MBW) fashion which, while shown to be feasible and repeatable in CF, has not been tested in HSCT or BOS patients. Both methods offer promising non-invasive ways to monitor obstructive changes in lung ventilation due to disease and their regional information on ventilation may be more sensitive to earlier symptoms compared to pulmonary function tests (PFTs). This study aims to evaluate single-breath and MBW Xe-MRI for detecting BOS in pediatric HSCT patients and compare with PFTs and against NIH criteria. Methods: Patients were recruited ≥6 months post-HSCT with institutional approval; some with confirmed BOS per NIH criteria, others with only BOS-like symptoms. Patients performed spirometry (FEV1%) and N2 MBW (lung clearance index, LCI) by ATS/ERS standards. Single-breath Xe-MRI (ventilation defect percent, VDP) and MBW Xe-MRI (fractional ventilation, FV) were performed as previously described. For VDP and FV, their coefficient of variation (CoV, a measure of regional heterogeneity) was calculated using the standard deviation/mean using a 3x3 image kernel. Mann-Whitney U-test assessed group differences. Pearson's correlation coefficient was used to correlate MRI with PFTs. Results: 11 children [mean age 12±3 years, 5 BOS, 6 non-BOS] were recruited. Xe-MRI was well-tolerated by all participants with no adverse events. Significant differences between BOS/non-BOS were observed by both measures of regional ventilation heterogeneity, CoVVDP (P=0.017) and CoVFV (P=0.016), but not by either measure of regional ventilation, VDP or FV (Figure 1). Both PFTs (FEV1%, LCI) were also able to distinguish between groups, as expected. CoVVDP correlated significantly with LCI (R=0.74, P=0.014) and FEV1% (R=-0.65, P=0.030). No other significant correlations between Xe-MRI and PFTs were observed. Conclusion: Measures of regional ventilation heterogeneity (CoVVDP and CoVFV) can distinguish BOS and non-BOS, while VDP, FV cannot. Changes in ventilation heterogeneity (either spatial, as reflected by imaging, or temporal, as reflected by LCI) may be an important marker for detecting BOS. Absolute advantage of MRI over PFTs requires further study.

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)0.000
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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

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 head0.007
GPT teacher head0.270
Teacher spread0.263 · 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
Published2025
Admission routes1
Has abstractyes

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