Utilization of biopsy-based genomic classifier to predict distant metastasis after definitive radiation and short-course ADT for intermediate and high-risk prostate cancer
Bibliographic record
Abstract
BACKGROUND: We examined the ability of a biopsy-based 22-marker genomic classifier (GC) to predict for distant metastases after radiation and a median of 6 months of androgen deprivation therapy (ADT). METHODS: We studied 100 patients with intermediate-risk (55%) and high-risk (45%) prostate cancer who received definitive radiation plus a median of 6 months of ADT (range 3-39 months) from 2001-2013 at a single center and had available biopsy tissue. Six to ten 4 micron sections of the needle biopsy core with the highest Gleason score and percentage of tumor involvement were macrodissected for RNA extraction. GC scores (range, 0.04-0.92) were determined. The primary end point of the study was time to distant metastasis. Median follow-up was 5.1 years. There were 18 metastases during the study period. RESULTS: On univariable analysis (UVA), each 0.1 unit increase in GC score was significantly associated with time to distant metastasis (hazard ratio: 1.40 (1.10-1.84), P=0.006) and remained significant after adjusting for clinical variables on multivariable analysis (MVA) (adjusted hazard ratio: 1.36 (1.04-1.83), P=0.024). The c-index for 5-year distant metastasis was 0.45 (95% confidence interval: 0.27-0.64) for Cancer of the Prostate Risk Assessment score, 0.63 (0.40-0.78) for National Comprehensive Cancer Network (NCCN) risk groups, and 0.76 (0.57-0.89) for the GC score. Using pre-specified GC risk categories, the cumulative incidence of metastasis for GC>0.6 reached 20% at 5 years after radiation (P=0.02). CONCLUSIONS: We believe this is the first demonstration of the ability of the biopsy-based GC score to predict for distant metastases after definitive radiation and ADT for intermediate- and high-risk prostate cancer. Patients with the highest GC risk (GC>0.6) had high rates of metastasis despite multi-modal therapy suggesting that they could potentially be candidates for treatment intensification and/or enrollment in clinical trials of novel 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".