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A prospective provincial registry of PSMA PET CT for recurrent prostate cancer (PREP): Results for 4135 men.

2025· article· en· W4407701397 on OpenAlexaffabout
Glenn Bauman, Mohammed Rashid, Deanna L. Langer, Pamela MacCrostie, Bo Green, Victor Mak, Girish S. Kulkarni, Bobby Shayegan, Laurence Klotz, Stephen E. Pautler, Antonio Finelli, Marlon Hagerty, William Luke, Andrés Kohan, Vivian Tan, Luke T. Lavallée, Katherine Zukotynski, Joseph Chin, Ur Metser

Bibliographic record

VenueJournal of Clinical Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsUniversity of OttawaThunder Bay Regional Health Sciences CentreLondon Health Sciences CentreCancer Care OntarioMcMaster UniversityPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health NetworkWestern University
Fundersnot available
KeywordsMedicineProstate cancerOncologyInternal medicineCancerProstateCancer registryGynecology

Abstract

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35 Background: PREP was initiated in Ontario to provide access and characterize performance of PSMA PET CT among men with recurrent prostate cancer after primary definitive treatment (RP or RT). Methods: Between 03/18 and 09/22, 4135 men were accrued. Men were enrolled and imaged with 18F-DCFPyL at 1 of 6 participating sites within 1 of 6 clinical cohorts. Standardized reports delineated sites of recurrence and post PET management changes. Linkage to provincial databases allowed estimation of overall survival and utilization of salvage radiotherapy after PET. Results: Median follow-up was 1.8 years; key findings are in Table 1. Significant predictors of a positive PET scan on multi-variable analysis included: higher PSA at time of PET and clinical cohort (highest for cohort 4). Significant predictors of change in management were type of recurrence (highest for loco-regional) and higher PSA. Significant predictors of worse overall survival included clinical cohort (worst for cohort 4), extent and type of metastases (worst for mixed bone/lymph/visceral or extensive metastases). A change in management post PET was a significant predictor of improved survival. Conclusions: The PREP registry facilitated access to PSMA PET/CT with high rates of disease detection and impact on management. Significant factors associated with survival were extent and sites of disease detected and management change after PET. Clinical trial information: NCT03718260 . PREP registry: Key findings. Total (n=4135) Cohort 1 (n=255): BF within 3 months from RP and pN+ or PSA >0.1 Cohort 2 (n=1500): BF following RP Cohort 3 (n=1040): BF post RPand adjuvant or salvage RT Cohort 4 (n=263): BF while on salvage hormone therapy Cohort 5 (n=176): BF following Therapy for Oligo-metastases on prior PET Cohort 6 (n=901): BF following primary RT Median Age at scan (IQR) 71.0 (66.0–76.0) 66.0 (61.0–70.0) 70.0 (65.0–74.0) 72.0 (67.0–76.0) 74.0 (69.0–79.0) 73.0 (66.0–77.0) 75.0 (70.0–79.0) Median PSA (IQR) at scan (ng/mL) 1.3 (0.3–4.0) 0.7 (0.2–2.5) 0.3 (0.2–1.0) 1.1 (0.5–2.7) 3.5 (1.6–7.2) 2.4 (1.0–5.3) 4.4 (3.1–7.4) PET: Negative Findings 1216 (29.4) 84 - 88 (32.9 - 34.5)* 749 (49.9) 258 (24.8) 19 (7.2) 18 - 22 (10.2 - 12.5)* 84 (9.3) PET: Locoregional Recurrence 1377 (33.3) 95 (37.3) 471 (31.4) 295 (28.4) 63 (24.0) 34 (19.3) 419 (46.5) PET: Oligo-Metastatic (≤5 metastases) 1021 (24.7) 45 (17.6) 224 (14.9) 354 (34.0) 111 (42.2) 70 (39.8) 217 (24.1) PET: Extensive Metastases 521 (12.6) 29 (11.4) 56 (3.7) 133 (12.8) 70 (26.6) 52 (29.5) 181 (20.1) Change in management post PET 2070 (50.1) 135 (52.9) 585 (39.0) 552 (53.1) 152 (57.8) 99 (56.3) 547 (60.7) Radiotherapy within 6 months 1729 (41.8) 165 (64.7) 866 (57.7) 346 (33.3) 88 (33.5) 49 (27.8) 215 (23.9) Died during follow-up period 138 (3.3) 1 - 5 (0.4 - 2.0)* 18 (1.2) 30 (2.9) 35 (13.3) 8 -12 (4.5 - 6.8)* 44 (4.9) BF: Biochemical Failure; RP: R

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.720
Threshold uncertainty score0.564

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.004
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.002

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.

Opus teacher head0.114
GPT teacher head0.535
Teacher spread0.421 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes2
Has abstractyes

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