PSMA-PET/CT Registry for Recurrent Prostate Cancer (PREP): Initial findings from a single center.
Bibliographic record
Abstract
5064 Background: Several lesion-targeted therapies exist for locally recurrent or limited stage metastatic prostate cancer (PCa) post-radiotherapy (RT) and radical prostatectomy (RP). However, detection of disease sites is limited using conventional imaging (CI) including computed tomography (CT) and bone scan. Prostate specific membrane antigen (PSMA) targeting PET radiopharmaceuticals like [18F]DCFPyL may help detect disease not seen on CI. Our objective was to assess the ability of PSMA targeted PET/CT to detect sites of disease recurrence and impact on patient management. Methods: This multi-center prospective registry study included six Ontario centers. Eligible patients in 1 of 7 clinical cohorts (Table) were identified and approved by Cancer Care Ontario (CCO) to have restaging with PSMA targeted PET/CT. Referring physicians were asked to complete a form indicating whether a change in management strategy would occur based on the PET/CT results. At 6 months post-PET/CT, actual patient management will be confirmed via provincial registries. These interim results are from a single center. Results: 253 patients were enrolled and had a PSMA targeted PET/CT. At baseline, median age was 71 years (range 50-102 years) and median PSA was 2.7 ng/mL (range 0.04-134.0 ng/mL). The majority of patients (n=59; 23.3%) were in cohort 2 (biochemical failure post-RP). In patients with negative CI, PSMA targeted PET/CT detected disease sites in 68.5% (170/248), resulting in a change in management for 67.8% (137/202) overall and 72.1% and 64.3% post-RT and post-RP, respectively. Conclusions: PSMA targeted PET/CT detected occult lesions on CI in the majority of patients enrolled, leading to a high rate of change in management. Our institutional results are in keeping with preliminary results reported for the provincial cohort. Clinical trial information: NCT03718260. [Table: see text]
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".