Contrast-Enhanced Microcomputed Tomography Using Intraperitoneal Contrast Injection for the Assessment of Tumor-Burden in Liver Metastasis Models
Bibliographic record
Abstract
OBJECTIVES: To determine if intraperitoneally (IP) administered contrast (iohexol), used in conjunction with a liver-specific agent (Fenestra), can improve measurement precision and accuracy when quantifying tumor volume from micro-CT images of a liver metastasis model. MATERIALS AND METHODS: We compared images acquired with Fenestra alone to images acquired with the combination of Fenestra and IP iohexol. The variability in tumor volume and tumor-burden measurement was evaluated for both techniques. The tumor-burden measurement accuracy of both in vivo techniques was determined by comparison with tumor-burden quantified from ex vivo images. RESULTS: : The addition of IP iohexol decreased measurement variability for individual tumors and overall tumor-burden by 4-8 fold and 2-3 fold, respectively. IP iohexol significantly improved the accuracy of tumor-burden measurement for both low and high tumor-burdened animals. CONCLUSIONS: The combination of IP iohexol with Fenestra provides superior delineation of liver tumors, in comparison to Fenestra alone. The complete tumor delineation provided by this imaging strategy allows for noninvasive quantification of liver tumor-burden.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".