Quantification of the effect of osteolytic metastases on bone strain within whole vertebrae using image registration
Bibliographic record
Abstract
The vertebral column is the most frequent site of metastatic involvement of the skeleton with up to 1/3 of all cancer patients developing spinal metastases. Longer survival times for patients, particularly secondary to breast cancer, have increased the need for better understanding the impact of skeletal metastases on structural stability. This study aims to apply image registration to calculate strain distributions in metastatically involved rodent vertebrae utilizing µCT imaging. Osteolytic vertebral lesions were developed in five rnu/rnu rats 2-3 weeks post intracardiac injection with MT-1 human breast cancer cells. An image registration algorithm was used to calculate and compare strain fields due to axial compressive loading in metastatically involved and control vertebrae. Tumor-bearing vertebrae had greatly increased compressive strains, double the magnitude of strain compared to control vertebrae (p=0.01). Qualitatively strain concentrated within the growth plates in both tumor bearing and control vertebrae. Most interesting was the presence of strain concentrations at the dorsal wall in metastatically involved vertebrae, suggesting structural instability. Strain distributions, quantified by image registration were consistent with known consequences of lytic involvement. Metastatically involved vertebrae had greater strain magnitude than control vertebrae. Strain concentrations at the dorsal wall in only the metastatic vertebrae, were consistent with higher incidence of burst fracture secondary to this pathology. Future use of image registration of whole vertebrae will allow focused examination of the efficacy of targeted and systemic treatments in reducing strains and the related risk of fracture in pathologic bones under simple and complex loading.
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 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 | 0.009 | 0.004 |
| 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".