Micro-CT evaluation of bone defects: Applications to osteolytic bone metastases, bone cysts, and fracture
Bibliographic record
Abstract
Bone defects can occur in various forms and present challenges to performing a standard micro-CT evaluation of bone quality because most measures are suited to homogeneous structures rather than ones with spatially focal abnormalities. Such defects are commonly associated with pain and fragility. Research involving bone defects requires quantitative approaches to be developed if micro-CT is to be employed. In this study, we demonstrate that measures of inter-microarchitectural bone spacing are sensitive to the presence of focal defects in the proximal tibia of two distinctly different mouse models: a burr-hole model for fracture healing research, and a model of osteolytic bone metastases. In these models, the cortical and trabecular bone compartments were both affected by the defect and were, therefore, evaluated as a single unit to avoid splitting the defects into multiple analysis regions. The burr-hole defect increased mean spacing (Sp) by 27.6%, spacing standard deviation (SpSD) by 113%, and maximum spacing (Spmax) by 72.8%. Regression modeling revealed SpSD (β=0.974, p<0.0001) to be a significant predictor of the defect volume (R(2)=0.949) and Spmax (β=0.712, p<0.0001) and SpSD (β=0.271, p=0.022) to be significant predictors of the defect diameter (R(2)=0.954). In the mice with osteolytic bone metastases, spacing parameters followed similar patterns of change as reflected by other imaging technologies, specifically bioluminescence data which is indicative of tumor burden. These data highlight the sensitivity of spacing measurements to bone architectural abnormalities from 3D micro-CT data and provide a tool for quantitative evaluation of defects within a bone.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".