Hepatic perfusion in a tumor model using DCE-CT: an accuracy and precision study
Bibliographic record
Abstract
In the current study we investigate the accuracy and precision of hepatic perfusion measurements based on the Johnson and Wilson model with the adiabatic approximation. VX2 carcinoma cells were implanted into the livers of New Zealand white rabbits. Simultaneous dynamic contrast-enhanced computed tomography (DCE-CT) and radiolabeled microsphere studies were performed under steady-state normo-, hyper- and hypo-capnia. The hepatic arterial blood flows (H(A)BF) obtained using both techniques were compared with ANOVA. The precision was assessed by the coefficient of variation (CV). Under normo-capnia the microsphere H(A)BF were 51.9 +/- 4.2, 40.7 +/- 4.9 and 99.7 +/- 6.0 ml min(-1) (100 g)(-1) while DCE-CT H(A)BF were 50.0 +/- 5.7, 37.1 +/- 4.5 and 99.8 +/- 6.8 ml min(-1) (100 g)(-1) in normal tissue, tumor core and rim, respectively. There were no significant differences between H(A)BF measurements obtained with both techniques (P > 0.05). Furthermore, a strong correlation was observed between H(A)BF values from both techniques: slope of 0.92 +/- 0.05, intercept of 4.62 +/- 2.69 ml min(-1) (100 g)(-1) and R(2) = 0.81 +/- 0.05 (P < 0.05). The Bland-Altman plot comparing DCE-CT and microsphere H(A)BF measurements gives a mean difference of -0.13 ml min(-1) (100 g)(-1), which is not significantly different from zero. DCE-CT H(A)BF is precise, with CV of 5.7, 24.9 and 1.4% in the normal tissue, tumor core and rim, respectively. Non-invasive measurement of H(A)BF with DCE-CT is accurate and precise. DCE-CT can be an important extension of CT to assess hepatic function besides morphology in liver diseases.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".