Relevance of the POS-1 Orthotopic Model as an “Imaging Model” for <i>In Vivo</i> and Simultaneous Monitoring of Tumor Proliferation and Bone Remodeling in Osteosarcoma
Bibliographic record
Abstract
INTRODUCTION: Osteosarcoma (OS) management requires a better understanding of tumor/bone interactions in vivo during disease progression. Using [(18)F]-FDG and [(99m)Tc]-HMDP imaging, we assessed a methodology for an in vivo quantitative characterization of an orthotopic model of osteolytic OS on the basis of (1) tumor proliferation, (2) tumor and bone metabolic activities, and (3) bone remodeling. METHODS: POS-1 tumor bearing mice were monitored in vivo over a 26-day period, with tumor and bone metabolic volumes (TMV and BMV, respectively) being determined from [(18)F]-FDG, bone remodeling from [(99m)Tc]-HMDP, and tumoral volume from micro- computed tomography scans. RESULTS: From day 10, [(18)F]-FDG strongly accumulated within POS-1 tumor, with a tumor/muscle ratio of 3.7 ± 0.8. TMV and BMV increased as pathology progressed: TMV increased at early stage of pathology (from 56%) whereas BMV strongly increased (from 113%) during late stage. From [(99m)Tc]-HMDP imaging, bone remodeling features were evidenced within the distal region of tibia bearing the tumor, with a mean scintigraphic ratio of 1.36 ± 0.11 at day 12, that reached value of 2.53 ± 0.19 at day 26. CONCLUSIONS: Our results validated the POS-1 orthotopic model as "OS imaging model," that could serve for evaluating in vivo therapies targeting tumor proliferation and/or bone remodeling in OS.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".