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Individual patient data (IPD) analysis of early PSA nadir in ARASENS, LATITUDE, and TITAN: Training and validation of a novel model.

2025· article· en· W4407699241 on OpenAlexaff
Soumyajit Roy, Yilun Sun, Maha Hussain, Kim N., Simon Chowdhury, Pedro C. Barata, Christopher J.D. Wallis, Bertrand Tombal, Neeraj Agarwal, Amar U. Kishan, Shawn Malone, Scott C. Morgan, Umang Swami, Angela Y. Jia, Nicholas G. Zaorsky, Michael Ong, Karim Fizazi, Fred Saad, Neal D. Shore, Daniel E. Spratt

Bibliographic record

VenueJournal of Clinical Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsUniversité de MontréalUniversity of OttawaOttawa HospitalPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health NetworkUniversity of British Columbia
FundersProstate Cancer Foundation
KeywordsNadirMedicineTitan (rocket family)TECLatitudeGeodesyAstrobiologySatelliteGeologyAstronomyIonosphere

Abstract

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192 Background: Early PSA nadir after systemic therapy in mHSPC is a predictor of overall survival. There are no prediction models that have been validated in randomized clinical trials (RCTs) to help identify patients who experience early PSA response. Using IPD from three phase III randomized trials, ARASENS, LATITUDE, and TITAN, we trained and validated a model to predict early PSA nadir in mHSPC patients. Methods: Eligible trials that randomized mHSPC patients to receive an androgen receptor pathway inhibitor (ARPI) were identified through Medline and clinicaltrials.gov. Three trials were identified that had available IPD through online data-sharing portals, and a pre-specified analysis plan was approved. Early PSA nadir was defined as ≤0.2 ng/mL by 6 months of random allocation. Patients who received androgen deprivation (ADT) (+/- docetaxel [doce]) with ARPI, were split randomly 60:40 into a training and a testing cohort. Patients who received ADT monotherapy (+/- doce) as standard of care (SOC) were used as a validation cohort. A random forest classifier model was constructed in the training cohort with 10-fold cross-validation. Included variables were age, performance status, body mass index (BMI), Gleason score, metastatic stage at diagnosis, visceral metastasis, PSA and hemoglobin at baseline, use of docetaxel, and receipt of prior local therapy (RP and/or RT). After internal validation in the testing cohort, the locked model was applied to the validation cohort, and performance was assessed with area under curve (AUC) and Brier score. Results: Data was available for 3434 patients. Overall, 1718 patients received SOC plus ARPI and 1716 patients received SOC alone. The training cohort consisted of 1030 patients while the testing cohort and validation cohort consisted of 688 and 1716 patients, respectively. The top 5 variables in order of importance were baseline PSA, hemoglobin, BMI, age, and ECOG performance status. The AUC for the testing and validation cohort was 0.75 and 0.77, respectively. When stratified by tertile of predicted probability, the proportion of PSA nadir was 32%, 52%, and 83% in the testing cohort and 7%, 15%, and 41% in the validation cohort, respectively. The Brier scores for the testing and validation cohort were 0.20 and 0.26, respectively. The modest calibration (i.e., higher Brier score) in the validation (SOC alone) cohort could be attributed to slight overprediction of early PSA nadir probability by a model trained in SOC plus ARPI group. Conclusions: To our knowledge, this is the first trained and validated model to predict early PSA nadir by 6 months of treatment initiation in mHSPC using data from multiple phase III RCTs. This model could provide clinical utility to guide treatment and monitoring strategies, as well as conducting clinical trials to enrich patient accrual for those clinically impacted by early PSA nadir.

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)0.015
metaresearch head score (Gemma)0.017
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 score0.015
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.017
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.288
GPT teacher head0.493
Teacher spread0.204 · 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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