Validation of the Decipher prostate cancer classifier for predicting 10-year postoperative metastasis from analysis of diagnostic needle biopsy specimens.
Bibliographic record
Abstract
59 Background: Accurate riskstratification after diagnosis of prostate cancer (PCa) is key to optimal treatment decision-making. Decipher is an extensively validated genomic classifier of metastasis after radical prostatectomy (RP). Here, we evaluate its ability to predict metastasis from analysis of prostate needle biopsy diagnostic tumor tissue specimens in a cohort of intermediate risk PCa patients treated with RP. Methods: Fifty-seven patients with available diagnostic biopsy specimens were identified from a previously reported post-RP validation study of Decipher in a cohort of 169 patients treated at Cleveland Clinic. The core with at least 1mm tumor of the highest Gleason grade was sampled and subjected to whole transcriptome analysis. Decipher was calculated based on a locked random forest model. Cox multivariable (MVA) proportional hazards model and survival c-index were used to evaluate the performance of Decipher. Results: 61% of patients had biopsy Gleason score 6 and 67% of patients had NCCN intermediate risk disease. With a median 8 years follow up, 8 patients metastasized and 3 of these patients died of PCa. Decipher had a c-index of 0.80 (95% confidence interval [CI], 0.58-0.95) compared to 0.58 (95% CI, 0.18-0.91) for biopsy Gleason score and 0.57 (0.57; 95% CI, 0.23-0.89) for preoperative PSA at 10 years post-RP for prediction of metastasis. A combined model consisting of Decipher, preoperative PSA, and Gleason score had a c-index of 0.84 (95% CI, 0.68-0.96). On MVA, Decipher was the only significant predictor of metastasis when adjusting for age, preoperative PSA and biopsy Gleason score (Decipher hazard ratio per 10% increase: 1.72; 95% CI, 1.04–2.83; P = 0.02). Conclusions: Decipher was able to predict metastatic outcome from diagnostic biopsy specimens in a cohort of primarily intermediate risk men treated with RP. This additional genomic information may help identify patients who may not be optimal candidates for active surveillance and better identify appropriate first line therapy for men with intermediate risk disease.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 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".