Limitations of single slice dynamic contrast enhanced MR in pharmacokinetic modeling of bone sarcomas
Bibliographic record
Abstract
BACKGROUND: Single slice dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) appears to provide perfusion data about sarcomas in vivo that correlate with tumor necrosis on equivalent pathological sections. However, sarcomas are heterogeneous and therefore single slice DCE-MRI may not correlate with total tumor necrosis. PURPOSE: To determine whether changes in pharmacokinetic modeling of DCE-MRI, during chemotherapy for primary bone sarcomas correlated with histological measures of total tumor necrosis. MATERIAL AND METHODS: Twelve patients with appendicular primary bone sarcomas were included in the study. Each patient had DCE-MRI before, and after completion, of pre-operative chemotherapy. The mean arterial slope (A), endothelial permeability coefficient (K(trans)), and extravascular extracellular volume (V(e)) were derived from each data set using a modified two compartment pharmacokinetic model. Total tumor necrosis rates were compared with changes in A, K(trans), and V(e). RESULTS: Six patients had total tumor necrosis of >or=90% and six had a measure of <90%. The median percentage changes in A, K(trans), and V(e) for the >or=90% necrosis group were -52.5% (-83 to 6), -66% (-82 to 26), and 23.5% (-26 to 40), respectively. For the <90% necrosis group, A = - 35% (-75 to 132), K(trans)= - 53 (-66 to 149) and V(e)= - 14.5% (-42 to 40). One patient with >90% necrosis had increases in all three measures. Comparison of the two groups generated P-values of 0.699 for A, 0.18 for K(trans), and 0.31 for V(e). CONCLUSION: There was no statistically significant correlation between changes in pharmacokinetic perfusion parameters and total tumor necrosis. When using single slice DCE-MRI heterogeneous histology of primary bone sarcomas and repair mediated angiogenesis might both be confounding factors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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 teacher head, 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".