Focal Therapy for Localized Prostate Cancer: A Case Series with Cost Analysis
Bibliographic record
Abstract
Focal therapy for prostate cancer (PCa) provides approaches to treat PCa patients in a less invasive manner than traditional whole-gland surgical or radiation modalities. This manuscript provides a case series of high-intensity focused ultrasound (HIFU), cryoablation, and irreversible electroporation (IRE) for PCa at a single institution and cost analysis with a review of the literature. All patients who underwent HIFU, cryoablation, or IRE for localized PCa were retrospectively reviewed, excluding patients who received whole-gland therapy. Functional outcomes were erectile dysfunction and lower urinary tract symptoms. Cost data were collected. A total of 45 patients were included in the study with focal therapy ranging from 2023 to 2025 (4 HIFU, 20 cryoablation, 21 IRE). A total of 30 patients had focally treated lesions, and 15 patients had hemi-gland treatment. The mean preoperative PSA was 7.7 ng/mL. On the paired sample t-test, there was no significant difference between pre-focal and post-focal therapy PSA. Three patients experienced biochemical recurrence requiring prostate biopsy after focal treatment. Mean cost was USD 3804.50 and not significantly different by focal treatment. No metastatic events occurred nor deaths at a median follow-up of 6 months. Patients in this series had largely unaltered functional outcomes. Cost analysis in contemporary publications is lacking. Although follow-up was short, cancer control was adequate.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".