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Radical prostatectomy (RP) versus radiotherapy (RT) in high-risk prostate cancer (HR-PCa): Emulated randomized comparison with individual patient data (IPD) from two phase III randomized trials (RCTs).

2025· article· en· W4407700218 on OpenAlexaff
Soumyajit Roy, Yilun Sun, James A. Eastham, Martin Gleave, Himisha Beltran, Amar U. Kishan, Angela Y. Jia, Nicholas G. Zaorsky, Jorge A. García, Eric J. Small, Paul L. Nguyen, Gerhardt Attard, Rana R. McKay, Alton Oliver Sartor, Seth A. Rosenthal, Susan Halabi, Felix Y. Feng, Michael J. Morris, Howard M. Sandler, Daniel E. Spratt

Bibliographic record

VenueJournal of Clinical Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineProstatectomyProstate cancerRandomized controlled trialRadiation therapyUrologyProstateOncologyInternal medicineCancer

Abstract

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309 Background: Standard of care (SOC) treatment options for HR-PCa include RT with long-term androgen deprivation (LT-ADT) or RP with selective use of post-operative RT +/- androgen deprivation therapy (ADT). The optimal treatment approach has been assessed in retrospective population-based and multi-center comparisons, which have yielded mixed results with substantial bias. Therefore, we conducted an emulated randomized comparison of RT vs RP in HR-PCa leveraging patients enrolled in RCTs. Methods: We searched Medline for RCTs in HR-PCa with a SOC arm of an RT- or RP-based regimen. Inclusion required similar experimental treatment and contemporaneous enrollment in the same country to reduce bias. This identified 2 trials, NRG/RTOG 0521 (RT+LT-ADT +/- 6 cycles docetaxel [doce]), and CALGB 90203 (RP +/- neoadjuvant 6 cycles doce and ADT). Due to inherent difference in the biochemical recurrence criteria after RT vs RP, we chose inverse probability of treatment weighted (IPTW) cumulative incidence of distant metastasis (DM) as the primary endpoint, considering deaths as competing events. Death after DM was measured to create a harmonized metric of deaths likely attributed to PCa. To assess potential residual selection bias, death without DM to capture non-cancer associated deaths was analyzed. Results: Overall, 1290 patients (RT n=557, RP n=733) were included, with similar median follow-up of 6.4 years. Prior to IPTW, RP patients were significantly younger with lower baseline PSA compared to RT patients. Adjuvant (18%) and salvage therapy (44%) was used in RP cohort. Cumulative incidence of DM was significantly lower in patients who underwent RT compared to RP (8-year DM: 16% vs 23%; p=0.01; subdistribution hazard ratio [sHR] 0.48 [95%CI 0.34-0.69], p<0.001). 8-year rates of death after DM were 10% vs 8% (p=0.72) in the RP and RT patients, respectively. RT patients had significantly greater risk of death without DM (HR 2.09 [1.01-4.34], p=0.048) with early differences measured. On a cross-arm comparison, 8-year cumulative incidence of DM when comparing SOC RT+LT-ADT group versus the doce+ADT+RP group was 18% vs 21%, respectively (sHR 0.75 [0.45-1.24], p=0.26). Conclusions: HR-PCa patients enrolled on RCTs had significantly lower incidence of DM with an RT-based strategy compared to an RP-based approach. Longer follow-up is needed to assess deaths attributed to PCa. Despite the strengths of the comparison (use of cooperative group RCT data, contemporaneous enrollment in the same country, patients fit enough for chemotherapy, and IPTW adjustments) there appears to be residual unmeasured bias, as expected, based on greater early deaths without DM in the RT arm. Utilization of post-operative radiotherapy and ADT+Doce may mitigate differences between RP and SOC RT+LT-ADT.

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)0.077
metaresearch head score (Gemma)0.090
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.077
Threshold uncertainty score0.406

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0770.090
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0080.016
Bibliometrics0.0020.001
Science and technology studies0.0000.002
Scholarly communication0.0020.003
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.148
GPT teacher head0.510
Teacher spread0.362 · 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 designSimulation or modeling
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".

Quick stats

Citations4
Published2025
Admission routes1
Has abstractyes

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