Independent validation of a genomic classifier in an at-risk population of men conservatively managed after radical prostatectomy.
Bibliographic record
Abstract
16 Background: Patients with locally advanced prostate cancer after radical prostatectomy (RP) are at risk for clinical progression and by current guidelines are candidates for adjuvant radiation. While clinical trials comparing adjuvant radiation to observation demonstrated benefits for many of these patients about 50% men on the control observation arm did not progress. This study evaluated whether a validated genomic classifier ([GC], Decipher) for predicting metastasis can be used to identify high-risk patients that may be spared unnecessary secondary therapy. The objective was to validate GC predictions in an at risk population conservatively managed after RP. Methods: A case-cohort design was used to sample patients with either preop PSA>20 ng/mL, pT3, positive surgical margin or Gleason score 8 or more disease, treated at Cleveland Clinic with RP from 1987 to 2008. Patients with lymph node metastasis or neo-adjuvant or adjuvant treatment were excluded. Cases were defined as local recurrence and/or regional/distant metastasis confirmed by biopsy or positive CT/bone scan. Random sampling of the cohort, including all cases, yielded 220 patients. Tissue was available for 196 and GC scores were generated for 184 patients. AUC for survival data, weighted Cox regression analysis and cumulative incidence adjusting for competing risk were used to assess GC performance for predicting distant metastasis (DM) in comparison to the 2005 Stephenson nomogram. Results: GC had an AUC of 0.89 (95% CI 0.62-0.97) for predicting DM at 5 years post-RP. A combined GC-Stephenson model yielded an AUC of 0.81 (95% CI 0.62-0.96). GC was the predominant predictor in multivariable Cox analysis with an HR of 1.55 (95% CI 1.25-1.90) for a 10% increase in score. Patients with high (21.7%) and low GC scores (58.7%) had a 3.97 fold higher and 3.88 fold lower incidence of DM at 5 years than the intermediate group (19.6%), respectively. Conclusions: We present results of a second, blinded independent validation study of GC performance in a conservatively managed RP cohort. In an at risk population use of GC may further allow identification of men that may be safely spared adjuvant radiation therapy.
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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 | 0.009 |
| 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 | 0.001 |
| 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".