Utility of Incorporating Genetic Variants for the Early Detection of Prostate Cancer
Bibliographic record
Abstract
PURPOSE: Several single nucleotide polymorphisms (SNP) have been associated with the risk of prostate cancer. The clinical utility of using SNPs in the early detection of prostate cancer has not been evaluated. EXPERIMENTAL DESIGN: We examined a panel of 25 SNPs from candidate genes and chromosomal regions in 3,004 unselected men who were screened for prostate cancer using serum prostate-specific antigen (PSA) and digital rectal examination. All underwent a prostate biopsy. We evaluated the ability of these SNPs to help predict the presence of prostate cancer at biopsy. RESULTS: Of the 3,004 patients, 1,389 (46.2%) were found to have prostate cancer. Fifteen of the 25 SNPs studied were significantly associated with prostate cancer (P=0.02-7x10(-8)). We selected a combination of 4 SNPs with the best predictive value for further study. After adjusting for other predictive factors, the odds ratio for patients with all four of the variant genotypes compared with men with no variant genotype was 5.1 (95% confidence interval, 1.6-16.5; P=0.006). When incorporated into a nomogram, genotype status contributed more significantly than PSA, family history, ethnicity, urinary symptoms, and digital rectal examination (area under the curve=0.74). The positive predictive value of the PSA test ranged from 42% to 94% depending on the number of variant genotypes carried (P=1x10(-15)). CONCLUSIONS: SNP genotyping can be used in a clinical setting for the early detection of prostate cancer in a nomogram approach and by improving the positive predictive value of the PSA test.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".