QUANTIFYING OSTEOSARCOPENIA IN A PRECLINICAL MODEL OF METASTATIC CANCER USING IMAGING BIOMARKERS
Bibliographic record
Abstract
Growing evidence shows a strong relationship between sarcopenia (generalized loss of skeletal muscle mass, strength, and function) and osteopenia (low bone mineral density) in many diseases, however to date the integrated study of osteosarcopenia has been limited. This study aimed to quantify osteosarcopenia and the relationships between bone and muscle-based imaging biomarkers in a preclinical model of osteolytic metastasis. Seven six-week-old athymic female rats (Hsd:RH-Foxn1rnu, Envigo) were studied. Institutional approval was obtained and ARRIVE guidelines were followed. Four animals were inoculated with luciferase-transfected HeLa human cervical cancer cells via intracardiac injection (day 0) and three rats served as healthy controls. Animals were imaged using in vivo μMR (NanoScan PET/MR, Mediso, T1 GRE 3D axial with Gd contrast, 0.18\0.18\1mm voxels) and μCT (NanoScan SPECT/CT/PET, Mediso, 69μm isotropic voxels) at one day prior to and 21 days post cell injection. In vivo bioluminescence imaging (day 15, day 21), in vivo μCT, animal body weight, and general animal observations were used to assess tumour burden. Animals were euthanized on day 21. Excised vertebrae were μCT imaged (μCT100, Scanco, 34.4μm isotropic voxels). In vivo μCT and μMR images were cropped at L2/L3 and L4/L5 intervertebral disc midpoints and fused using manual and automatic registration (BRAINS registration module, 3D Slicer 4.11.20210226). Psoas muscles were manually segmented from fused μCT/μMR images and psoas muscle volume (normalized by L2 vertebral volume) and mean attenuation (as an indicator of muscle composition1) measured. Bone mineral density (BMD) of the L2 vertebrae was measured from ex vivo μCT images. T-tests compared biomarkers of non-metastatic and metastatic cohorts and Pearson's correlation was evaluated between the biomarkers. Three of four injected rats developed osteolytic bone tumours. Non-metastatic animals had higher BMD (p=0.00081) and change in normalized psoas volume (p=0.0051) compared to metastatic animals (figure 1a,b). Non-metastatic animals had lower change in normalized psoas attenuation (p=0.042) (figure 1c). A strong positive relationship was found between change in normalized psoas volume and BMD (R=0.95, p=0.00085) (figure 2a). Significant negative correlations were found between change in psoas attenuation and BMD (R=−0.87, p=0.011) and change in normalized psoas volume and change in psoas attenuation (R=−0.88, p=0.0086) (figure 2b,c). The presence of osteosarcopenia was indicated by the relative loss of bone and muscle mass in metastatic animals. The rat which did not develop cancer despite inoculation had bone and muscle biomarkers reflecting its healthy status. Expected differences in BMD and change in normalized psoas volume between non-metastatic and metastatic animals were accompanied by differences in change in psoas attenuation (suggestive of fatty infiltration of muscle). However, surprisingly, our results suggest that non-metastatic animals had more fatty infiltration than metastatic animals. Additional samples and histology will confirm changes in muscle composition and better link the current imaging biomarkers to physical changes in muscle. These imaging-based biomarkers quantifying osteosarcopenia in preclinical models of skeletal metastases can ultimately be used to study disease progression and treatment response. For any figures or tables, please contact the authors directly.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".