Validation of a genomic classifier for predicting biochemical failure following postoperative radiation therapy in high-risk prostate cancer.
Bibliographic record
Abstract
10 Background: Radiation therapy (RT) is commonly offered in the post radical prostatectomy (RP) setting, however response varies. We hypothesized that the genomic classifier ([GC] Decipher) score would predict biochemical failure (BF) and distant metastasis (DM) in men receiving post−RP RT. Methods: Under an institutional review board approved protocol, 223 men who underwent post−RP RT at the Kimmel Cancer Center of Thomas Jefferson University for pT3 or margin positive disease from 1990 to 2009 were identified. RNA was extracted from 143 patients with paraffin−embedded specimens and expression quantified from the highest Gleason grade tumor focus using a high−density oligonucleotide microarray. Excluding men who received neo−adjuvant therapy, 139 patients remained for GC calculation. Area under the receiver operating curve (AUC), decision curves, cumulative incidence accounting for competing risks, and multivariable Cox regression analyses were used to assess GC for predicting BF and DM after RT in comparison to nomograms. Results: The AUC of CAPRA-S was 0.67 (95% CI 0.58−0.77) and 0.65 (95% CI 0.44−0.86) for BF and DM, respectively. Integration of GC improved AUC to 0.75 (95% CI 0.66−0.84) and 0.77 (95% CI 0.64−0.91) for BF and DM, respectively. Cumulative incidence of BF at 8 years post-RT was 21%, 48%, and 81% for low (less than 0.4), intermediate (0.4 to 0.6), and high (more than 0.6) GC, respectively (p<0.00001). In multivariable analysis, patients who received RT early (pre−RT prostate-specific antigen [PSA] less than 1 ng/mL) had a BF benefit with a significantly reduced hazard ratio (HR) of 0.32 (95% CI 0.11−0.96, p<0.042). Patients with high GC had an HR of 14.73 for BF (95% CI 4.90−44.31, p<0.00001). Earlier PSA recurrence was observed in patients with high GC score that received salvage compared to adjuvant RT with median BF survival post-RT of 4.67 versus 8.78 years (p<0.04). This held true after adjusting for CAPRA-S score. Conclusions: This is the first validation of the GC in the post−RP RT setting. GC improved risk stratification above clinical classifiers. Patients with high GC received significant benefit from early RT intervention. For those patients with high pre-RT PSA and high GC, exploration of intensified therapy is warranted.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".