Building a predictive model for outcomes with [177Lu]Lu-PSMA-617 in patients with metastatic castration-resistant prostate cancer using VISION data: Preliminary results.
Bibliographic record
Abstract
208 Background: 177Lu-PSMA-617 is approved in adults with PSMA+ metastatic castration-resistant prostate cancer (mCRPC). This analysis of VISION data sought to build predictive models for clinical outcomes after 177Lu-PSMA-617. Methods: In VISION, adults with PSMA+ mCRPC received 177Lu-PSMA-617 (7.4 GBq every 6 weeks, ≤6 cycles) plus protocol-permitted standard of care (SoC) or SoC alone. In this post hoc analysis, 29 baseline parameters were assessed for prognostic and predictive value on overall survival (OS), radiographic progression-free survival (rPFS) and PSA response (≥50% reduction; PSA50). Parameters associated with outcome regardless of treatment were prognostic, and those associated with outcome after treatment with 177Lu-PSMA-617+SoC vs SoC were predictive. Cox proportional hazards (OS and rPFS) or logistic regression (PSA50) models were used in univariate analyses for parameter selection and to build statistical models. Here, univariate analyses are presented. Multiplicity was corrected by use of q values (α = 0.05). Results: Data from 831 adults were analyzed. In initial univariate analyses, 76%, 69% and 38% of parameters assessed were prognostic for OS, rPFS and PSA50, respectively; fewer were predictive of better outcomes after 177Lu-PSMA-617+SoC vs SoC (subset shown). Conclusions: Baseline prognostic and predictive parameters were identified for OS, as well as for rPFS and PSA50, in adults with mCRPC in VISION receiving 177Lu-PSMA-617+SoC or SoC alone. For clinical application, select parameters will be reassessed categorically, and multivariate predictive models will be developed using identified parameters and parameter shrinkage. [Table: see text]
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".