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Record W4415430075 · doi:10.1302/1358-992x.2025.10.055

QUANTIFYING OSTEOSARCOPENIA IN A PRECLINICAL MODEL OF METASTATIC CANCER USING IMAGING BIOMARKERS

2025· article· en· W4415430075 on OpenAlexaff
Christine Huang, Margarete K. Akens, Michael Hardisty, Cari Whyne

Bibliographic record

VenueOrthopaedic Proceedings · 2025
Typearticle
Languageen
FieldMedicine
TopicCancer Diagnosis and Treatment
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSarcopeniaIn vivoOsteopeniaEx vivoOsteoporosisCancerBone mineral

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.676
Threshold uncertainty score0.666

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.106
GPT teacher head0.408
Teacher spread0.303 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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