Early favorable prostate-specific antigen response prediction in metastatic hormone sensitive prostate cancer
Bibliographic record
Abstract
There is an unmet need for a tool that could predict early favorable prostate-specific antigen (PSA) response in metastatic hormone sensitive prostate cancer (mHSPC) patients receiving androgen receptor pathway inhibitor (ARPI). Here, we train and validate a multivariable logistic regression model to predict early favorable PSA response (≤0.2 ng/mL by 6 months) in these patients. Patients randomly allocated to the ARPI arms of the LATITUDE (abiraterone), TITAN (apalutamide), and ARASENS (darolutamide) trials, are split 60:40 into training (n = 1030) and internal validation (n = 688) cohorts. The locked model is validated in an independent external validation cohort - the enzalutamide arm of the ENZAMET trial (n = 540). The area under curve and Brier score for the locked model in the external validation cohort are 0.82 (95% confidence interval [CI] = 0.78–0.85) and 0.16, respectively. Stratification by predicted probability tertiles show PSA response rates of 92% (95% CI = 88–96), 74% (95% CI = 68–81), and 39% (95% CI = 32–47), respectively. Pending prospective validation, our model predicts early favorable PSA response supporting its potential role in guiding treatment decisions. There is a need for an easy-to-use clinical tool, that could predict favorable early PSA response and subsequently enhance early risk stratification, as well as guide treatment planning. Here, the authors show that based on patient data from four phase III randomized trials, Nadir androgen receptor pathway inhibitor (APRI)- Derived Integrative Response (NADIR) model predicts favorable early PSA response to ≤0.2 ng/mL by 6 months in metastatic hormone sensitive prostate cancer (mHSPC) patients initiating treatment with an APRI.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".