Noninvasive Quantification of Tumor Volume in Preclinical Liver Metastasis Models Using Contrast-Enhanced X-Ray Computed Tomography
Bibliographic record
Abstract
OBJECTIVES: To determine a timepoint after contrast injection that yields equal liver parenchymal and vascular enhancement in micro-computed tomography images. To evaluate the utility of images acquired during this time period for the noninvasive measurement of liver-tumor volume. MATERIALS AND METHODS: The imaging timepoint was determined by quantifying the enhancement kinetics of Fenestra VC (0.015 mL/g) in NIH III mice. In respiratory-gated images of tumor bearing mice, the ability to measure tumor volume was evaluated with a measurement variability study, and by comparing in vivo and histologically measured tumor volume. RESULTS: Eight hours after contrast injection the liver parenchyma and vasculature were equally enhanced allowing for clear delineation of the unenhanced tumors. The smallest tumor detected in this study was 1.1 mm in diameter. The coefficient of variation for tumor-volume measurement ranged from 3.6% to 12.9% and from 6.3% to 25.8% for intra and interobserver variability, respectively. In vivo and histologic tumor-volume measurements were closely correlated (r = 0.98, P < 0.0001). CONCLUSIONS: Imaging at a time period of equal liver parenchyma and vascular enhancement after contrast injection allows for clear delineation of liver-tumor borders, thereby enabling quantitative tumor-volume monitoring.
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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 | 0.000 | 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".