Quantitative Prostate MRI Analysis Following Fluvastatin Therapy for Localized Prostate Cancer - A Pilot Study
Bibliographic record
Abstract
Purpose: To assess the role of multi-parametric MRI (mpMRI) in assessment of tumor response to fluvastatin administered prior to radical prostatectomy. Methods: Men with MRI-visible, clinically significant prostate cancer and due to be treated with radical prostatectomy were prospectively enrolled. mpMRI was performed at baseline and following 6-7 week of neoadjuvant oral statin therapy (40 mg fluvastatin, twice daily), prior to prostatectomy. MRI assessment included tumor size, T2 relaxation time, ADC value, K-trans (volume transfer constant), Kep (reflux constant), and Ve (fractional volume) parameters at the 2 time points. Initial prostate needle biopsy cores, prior to starting oral statin therapy, corresponding to site of tumor on radical prostatectomy specimens were selected for analysis. The effect of fluvastatin on tumor proliferation (marker Ki67) and on tumor cell apoptosis (marker cleaved Caspase-3, CC3) were analyzed and correlated with MRI findings. Results: Nine men with paired MRI studies were included in the study. Binary histopathological data was available for 6 of the participants. No significant change in tumor size ( P = 0.898), T2 relaxation time ( P = 0.213), ADC value ( P = 0.455), K-trans ( P = 0.613), Kep ( P = 0.547) or Ve ( P = 0.883) between the time of biopsy and prostatectomy were observed. No significant change in tumor proliferation (%Ki67-positive cells, P = 0.766) was observed by immunohistochemistry analysis. However, there was a significant increase in tumor cell apoptosis (%CC3-positive cells, P = 0.047). Conclusion: mpMRI techniques may not be sufficiently sensitive to detect the types (or magnitude) of tumor cell changes observed following 6-7 weeks of fluvastatin therapy for prostate cancer.
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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 | 0.000 |
| 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".