Impact of ultrahigh baseline PSA levels on biochemical and clinical outcomes in two Radiation Therapy Oncology Group (RTOG) prostate clinical trials
Bibliographic record
Abstract
5123 Background: Controversy exists regarding the outcomes of prostate cancer patients (PCP) presenting with ultra-high (UH; defined as PSA ≥ 50 ng/ml) PSA levels. The objectives of this study were to assess the outcome of this patient population compared to other high-risk patients and to identify predictors associated with biochemical/clinical outcomes. Methods: PCP from two phase III RTOG PC clinical trials (9202 and 9413) were divided into two groups; high-risk patients with and without UH baseline PSA level. Predictive variables included age, Gleason score, T stage, KPS, and treatment arm. Outcomes included overall survival (OS), distant metastasis (DM), and biochemical failure (BF) by Phoenix definition. A Cox proportional hazards regression model was used for OS, and Fine and Gray's regression model was used for DM and BF to test the hypotheses that a difference in each outcome exists between the two groups. Results: There are 401 PCP in the UH PSA and 1792 in the non-UH PSA cohort. Median age was 70 years and PCP were evenly distributed across the Gleason groups (2–6, 7, 8–10) for the non-UH (median PSA 22.4 ng/ml) and the UH PSA (median PSA 72.8ng/ml) cohort. The UH PSA cohort had a larger proportion of T1-T2 disease (p = 0.01) and a smaller proportion of Gleason 8 disease (p = 0.04) than the non-UH group. PCP with UH PSA was found to have inferior OS (HR 1.19, 95% CI 1.02–1.39), DM rate (HR 1.51, 95% CI 1.19–1.92), and BF rate (HR 1.50, 95% CI 1.29–1.73) when compared to other high-risk PCP in multivariable modeling. In the UH cohort, PSA level was found to model risk of DM (HR 1.01, 95% CI 1.001–1.02) but not OS and BF. Gleason grade 8–10 was found to consistently predict for poor OS, DM, and BF outcomes (with HR estimates ranging from 1.41 to 2.36) in both the overall and UH cohort multivariable analyses. Conclusions: UH PSA levels at diagnosis are related with detrimental changes in OS, DM, and BF. All three outcomes assessed in this investigation can be modeled by various combinations all predictive variables tested. Supported by RTOG U10 CA21661, CCOP U10 CA37422, and Stat U10 CA32115 grants from the NCI. This abstract's contents are the sole responsibility of the authors and do not necessarily represent the official views of the NCI. No significant financial relationships to disclose.
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 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 | 0.046 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".