Bibliographic record
Abstract
PURPOSE OF REVIEW: This review discusses the feasibility, recent advances and current status of in-bore MRI-guided interventional techniques for diagnosis and treatment of focal prostate cancer (PCa) and also explores the future applications, highlighting the emerging strategies for the treatment of PCa. RECENT FINDINGS: Multiparametric MRI has opened up opportunities for diagnosis and targeted therapeutics to the site of disease within the organ wherein minimizing the incidence of treatment-related toxicity of whole gland therapy. MRI-guided targeted biopsy has a higher detection rate for significant cancer and lower rate of detection of insignificant cancer. In comparison to ultrasound-guided focal therapy, in-bore treatment provides the advantage of real time thermal monitoring during treatment and assessment of treatment coverage by an enhanced scan immediately post-treatment. Preliminary results of ongoing phase I and II in-bore focal PCa treatment trials via transperineal, transrectal and transurethral routes, using different energy modalities for the ablation, have shown promising results. SUMMARY: Advances in multiparametric-MRI has opened up opportunities for in-bore targeted focal treatment of PCa in the correctly selected patient.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".