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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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".