Design and Implementation of a Multi‐Center Trial of <scp> <sup>129</sup> Xe </scp> Gas Exchange <scp>MRI</scp> and <scp>MRS</scp> to Evaluate Longitudinal Progression of <scp>COPD</scp>
Bibliographic record
Abstract
ABSTRACT MR imaging holds the potential to enhance drug development efficiency by de‐risking early phase studies and increasing confidence in results. It can improve patient selection, increase repeatability, and provide greater sensitivity to change, thereby enabling smaller, faster clinical trials. For trials in the pulmonary space, hyperpolarized 129 Xe MRI is appealing because it provides 3‐dimensional imaging of pulmonary ventilation and gas exchange in a brief, non‐invasive exam. Metrics derived from 129 Xe MRI may be more sensitive to disease progression than conventional lung function assessments and may thus provide a valuable means to evaluate numerous novel pharmacologic and biologic therapies now in development. However, despite the acute need for better patient selection and for prognostic and monitoring biomarkers, 129 Xe MR imaging is not yet widely utilized in pulmonary drug development, partly because such trials must be conducted at multiple centers to enroll enough participants. Thus, incorporating 129 Xe MRI requires broader dissemination, harmonized image acquisition protocols, standardized dose delivery, visualization, and quantification. Multi‐site trials must also be able to operate across all major MRI vendor platforms and diverse software/hardware revisions. To this end, the 129 Xe MRI Clinical trials consortium has published a harmonized protocol describing four recommended acquisitions. Here we report on the first industry‐sponsored study to deploy this 129 Xe MRI/MRS protocol in a multi‐center, multi‐platform, multi‐national study to evaluate longitudinal progression of chronic obstructive pulmonary disease (COPD). We demonstrate the steps necessary to implement standardized 129 Xe‐MRI acquisition techniques across multiple sites and discuss the practices implemented, quality control approaches, and lessons learned for facilitating and accelerating the implementation of future trials that incorporate this technology. Level of Evidence: 5.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".