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Evaluation of post radiotherapy PSA as a prognostic and predictive biomarker in high risk prostate cancer: A secondary analysis of RTOG 0521.

2025· article· en· W4407698914 on OpenAlexaff
P.P. Koffer, Christina Raker, Thomas A. DiPetrillo, Benedito A. Carneiro, Anthony Mega, Howard M. Sandler, George Rodrigues, Amit B. Shah, Jason A. Efstathiou, Susan Chafe, Alexander G. Balogh, Scott Williams, Deborah A. Kuban, Elizabeth Gore, J.K. Wong, Marie Duclos, Paul L. Nguyen, Felix Y. Feng

Bibliographic record

VenueJournal of Clinical Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsMcGill University Health CentreLondon Health Sciences Centre
Fundersnot available
KeywordsMedicineProstate cancerRadiation therapyOncologyInternal medicineBiomarkerCancer

Abstract

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384 Background: RTOG 0521 was a randomized trial of radiotherapy (RT) and 24 months of androgen deprivation (ADT) with and without docetaxel (D) in high risk prostate cancer. We sought to evaluate whether post RT PSA was prognostic of outcomes and predictive of the benefit of D. We hypothesized that patients with a higher post RT PSA derive a benefit from D while those with a lower post RT PSA would derive no benefit. Methods: Patients treated on RTOG 0521 received 72-75.6 Gy in 40-42 fractions 8 weeks after starting ADT. In the experimental arm, D was started 28 days after RT. Per protocol, a PSA was to be drawn within 28 days after completion of RT (PRT-PSA). Hazard ratios (HRs) for PRT-PSA (>/≤ median level) were estimated by Cox proportional hazards regression for overall survival (OS) and Fine-Gray competing risks regression for prostate specific mortality (PCSM) and distant metastasis (DM), adjusting for baseline characteristics. As a sensitivity analysis for non-proportional hazards, HRs were estimated with follow up censored at 10 years. Results: PRT-PSA was available in 276/563 patients (114/281 in ADT alone and 162/282 in the ADT+D arm). PRT-PSA was drawn at a median of 15 days from the completion of RT and 120 days from randomization. 25% of patients had a PRT-PSA >0.1 ng/L. Patients with PSA >0.1 ng/mL had worse OS (HR 2.39, 95% confidence interval [CI] 1.51-3.79), PCSM (HR 3.78, 95% CI 1.87-7.62), and DM (HR 3.54, 95% CI 1.99-6.31) (all p<0.001). Baseline characteristics including Gleason score, T-stage, pretreatment PSA, performance status, and age were similar between patients with a PRT-PSA >0.1 and ≤ 0.1 ng/mL. In patients with PRT-PSA >0.1 ng/mL, there was no benefit seen with the addition of D to ADT alone in terms of OS (HR 1.06, p=0.88), PCSM (HR 0.97, p=0.96), or DM (HR 1.16, p=0.75). In patients with PRT-PSA ≤0.1 ng/mL, there was a benefit from the addition of D to ADT alone in terms of OS (HR 0.55, p=0.03) and PCSM (HR 0.36, p=0.02) but no significant benefit in terms of DM (HR 0.74, p=0.40). Results were similar in the sensitivity analysis for patients with PRT-PSA >0.1 ng/mL (OS HR 1.36, p=0.42; PCSM HR 1.71, p=0.39) and patients with PRT-PSA ≤0.1 ng/mL (OS HR 0.41, p=0.003; PCSM HR 0.26, p=0.006). Conclusions: PRT-PSA was prognostic of OS, DMFS, and DM in patients with high risk prostate cancer treated with RT and long term ADT +/- D. Despite having a worse prognosis, patients with PRT-PSA >0.1 ng/mL did not benefit from the addition of D while those with PRT-PSA ≤ 0.1 ng/mL had an OS and PCSM benefit from D. Clinical trial information: NCT00288080 . PRT-PSA (ng/mL) ADT, event/total ADT+D, event/total Adjusted HR (95% CI) Adjusted HR (95% CI)-10 y OS ≤0.1 28/79 31/127 0.55 (0.32-0.95) 0.41 (0.23-0.73) >0.1 21/35 18/35 1.06 (0.51-2.19) 1.36 (0.64-2.88) PCSM ≤0.1 13/79 7/127 0.36 (0.15-0.87) 0.26 (0.10-0.67) >0.1 15/35 11/35 0.97 (0.29-3.21) 1.71 (0.51-5.70) DM ≤0.1 14/79 16/127 0.74 (0.37-1.50) 0.75 (0.37-1.50) >0.1 16/35 16/35 1.16 (0.47-2.85) 1.09 (0.43-2.75)

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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)0.006
metaresearch head score (Gemma)0.006
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.073
GPT teacher head0.505
Teacher spread0.432 · 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 routes1
Has abstractyes

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