Non-invasive quantification of bone (re)modeling dynamics in adults with osteogenesis imperfecta treated with setrusumab using timelapse high-resolution peripheral-quantitative computed tomography
Bibliographic record
Abstract
Timelapse imaging using high-resolution peripheral quantitative computed tomography has emerged as a non-invasive method to quantify bone (re)modeling. However, there is no consensus on how to perform the procedure. As part of the ASTEROID phase-2b multicenter trial, we used 29 same-day repeated scans from adults with OI to identify a method that minimized measurement error. We evaluated input image type, registration method, segmentation mask, and for grayscale images various values for the voxel density difference considered formed or resorbed, minimum formation/resorption cluster size, and Gaussian smoothing sigma. We verified the accuracy of our method and then used it on longitudinal scans (baseline, 6, 12, 18, and 24 mo) from 78 participants to assess bone formation and resorption induced by an anabolic (setrusumab) and anti-catabolic (zoledronic acid) treatments as part of the ASTEROID trial. Regardless of image registration method, binary input images resulted in large errors ~13% and ~8% for first- and second-generation scanners, respectively. For the grayscale input images, errors were smaller for 3D compared to matched angle registration. For both scanner generations, a density threshold of 200 mgHA/cm3 combined with Gaussian noise reduction resulted in errors <1%. We verified the method was accurate by showing that similar regions of bone formation and resorption were identified when comparing each scan from the same-day repeated scans with a scan from another timepoint. Timelapse analysis revealed a dose-dependent increase in bone formation and resorption with setrusumab treatment. Zoledronic acid altered bone changes in favor of formation, although no changes reached statistical significance. This study identifies a timelapse method that minimizes measurement error, which can be used in future studies to improve the uniformity of results. This non-invasive imaging biomarker revealed dose dependent bone (re)modeling outcomes from 1 year of setrusumab treatment in adults with OI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".