In vivo monitoring of sorafenib therapy effects on experimental prostate carcinomas using dynamic contrast-enhanced MRI and macromolecular contrast media
Bibliographic record
Abstract
PURPOSE: To investigate dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) with macromolecular contrast media (MMCM) to monitor the effects of the multikinase inhibitor sorafenib on subcutaneous prostate carcinomas in rats with immunohistochemical validation. MATERIALS AND METHODS: Copenhagen rats, implanted with prostate carcinoma allografts, were randomized to the treatment group (n = 8) or the control group (n = 8). DCE-MRI with albumin-(Gd-DTPA)35 was performed at baseline and after 1 week using a clinical 3-Tesla system. The treatment group received sorafenib, 10 mg/kg body weight daily. Kinetic analysis yielded quantitative parameters of tumor endothelial permeability-surface area product (PS; ml/100 ml/min) and fractional blood volume (Vb, %). Tumors were harvested on day 7 for immunohistochemical analysis. RESULTS: In sorafenib-treated tumors, PS (0.62 ± 0.20 vs 0.08 ± 0.09 ml/100 ml/min; P < 0.01) and Vb (5.1 ± 1.0 vs 0.56 ± 0.48%; P < 0.01) decreased significantly from day 0 to day 7. PS showed a highly significant inverse correlation with tumor cell apoptosis (TUNEL; r = -0.85, P < 0.001). Good, significant correlations of PS were also observed with tumor cell proliferation (Ki-67; r = 0.67, P < 0.01) and tumor vascularity (RECA-1; r = 0.72, P < 0.01). MRI-assayed fractional blood volume Vb showed a highly significant correlation with tumor vascularity (RECA-1; r = 0.87, P < 0.001) and tumor cell proliferation (Ki-67; r = 0.82, P < 0.01). CONCLUSION: Results of DCE-MRI with MMCM demonstrated good, significant correlations with the immunohistochemically assessed antiangiogenic, antiproliferative, and proapoptotic effects of a 1-week, daily treatment course of sorafenib on experimental prostate carcinoma allografts.
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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 | 0.000 | 0.000 |
| 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".