Evaluating [68Ga]-Ga PSMA PET/CT for Detecting Prostate Cancer Recurrence Post-High-Intensity Focused Ultrasound and Brachytherapy: A Single-Center Retrospective Study
Bibliographic record
Abstract
Focal therapy offers a promising approach for treating localized prostate cancer (PC) with minimal invasiveness and potential cost benefits. High-intensity focused ultrasound (HIFU) and brachytherapy (BT) are among these options but lack long-term efficacy data. Patient follow-ups typically use biopsies and multiparametric MRI (mpMRI), which often miss recurrences. PET/CT with PSMA has emerged as a promising tool for detecting residual disease or recurrence post-treatment, offering higher sensitivity and specificity than traditional imaging. We retrospectively reviewed patients who underwent [68Ga]Ga-PSMA-11 PET/CT for biochemical recurrence (BCR) after HIFU or brachytherapy from 2016 to 2024. Out of 22 patients, 32% had HIFU and 68% had brachytherapy. The median time from treatment to PET scan was 77 months, with a median PSA level of 3 ng/mL. [68Ga]Ga-PSMA-11 PET/CT identified PC recurrence in 63.6% of cases. Of these, 50% showed prostate recurrence, 14% had lymph node involvement, and 28% had metastatic disease. Focal therapies like HIFU and brachytherapy are effective and minimally invasive options for localized PC. [68Ga]Ga-PSMA-11 PET/CT is valuable for detecting recurrence or residual disease, enhancing post-treatment surveillance.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".