[18F]-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography of LAPC4-CR Castration-Resistant Prostate Cancer Xenograft Model in Soft Tissue Compartments
Bibliographic record
Abstract
Preclinical xenograft models have contributed to advancing our understanding of the molecular basis of prostate cancer and to the development of targeted therapy. However, traditional preclinical in vivo techniques using caliper measurements and survival analysis evaluate the macroscopic tumor behavior, whereas tissue sampling disrupts the microenvironment and cannot be used for longitudinal studies in the same animal. Herein, we present an in vivo study of [(18)F]-fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) designed to evaluate the metabolism within the microenvironment of LAPC4-CR, a unique murine model of castration-resistant prostate cancer. Mice bearing LAPC4-CR subcutaneous tumors were administered [(18)F]-FDG via intravenous injection. After a 60-minute distribution phase, the mice were imaged on a PET/CT scanner with submillimeter resolution; and the fused PET/CT images were analyzed to evaluate tumor size, location, and metabolism across the cohort of mice. The xenograft tumors showed [(18)F]-FDG uptake that was independent of tumor size and was significantly greater than uptake in skeletal muscle and liver in mice (Wilcoxon signed-rank P values of .0002 and .0002, respectively). [(18)F]-FDG metabolism of the LAPC4-CR tumors was 2.1 ± 0.8 ID/cm(3)*wt, with tumor to muscle ratio of 7.4 ± 4.7 and tumor to liver background ratio of 6.7 ± 2.3. Noninvasive molecular imaging techniques such as PET/CT can be used to probe the microenvironment of tumors in vivo. This study showed that [(18)F]-FDG-PET/CT could be used to image and assess glucose metabolism of LAPC4-CR xenografts in vivo. Further work can investigate the use of PET/CT to quantify the metabolic response of LAPC4-CR to novel agents and combination therapies using soft tissue and possibly bone compartment xenograft models.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".