Advances in focal therapy for prostate cancer: current modalities, outcomes, and future directions
Bibliographic record
Abstract
Focal therapy (FT) has emerged as a promising treatment option for localized prostate cancer (PCa), offering oncologic control with reduced adverse effects compared to radical therapies. Various energy modalities, such as high-intensity focused ultrasound, cryotherapy, irreversible electroporation, and focal laser ablation, among others, selectively target malignant prostate tissue while sparing surrounding structures. Advances in imaging techniques, particularly multiparametric magnetic resonance imaging and prostate-specific membrane antigen-positron emission tomography/computed tomography, have improved lesion localization, patient selection, and treatment monitoring. The incorporation of artificial intelligence is enhancing tumor detection and predicting treatment outcomes. Although significant advancements have been made, challenges such as the lack of long-term data, treatment protocol standardization, and regulatory hurdles still limit widespread adoption. This review explores the current state of focal therapy for prostate cancer, highlighting its mechanisms, technological innovations, clinical outcomes, and future directions.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".