Biomarkers in prostate cancer diagnosis and prognosis: beyond prostate-specific antigen
Bibliographic record
Abstract
PURPOSE OF REVIEW: To review the most recent advances in genetic testing for prostate cancer risk and of new molecular diagnostic assays to improve diagnostic accuracy and treatment decision beyond prostate-specific antigen (PSA) testing. RECENT FINDINGS: Multiple independent studies had demonstrated evidence that genetic variations in three regions of chromosome 8q24 and one each at 17q12 and 17q24.3 are independent predictors of prostate cancer risk in addition to family history and serum PSA levels. The small percentage of individuals with several anomalies can have up to 10 times the risk of prostate cancer. Novel molecular urine tests have been studied, and the prostate cancer antigen 3 RNA detection has been studied most extensively and is now commercially available. It provides an independent and synergistic information to predict a higher or lower risk of prostate cancer at given PSA level and can further help predict the tumor volume and Gleason grade found on the prostatectomy specimen. Sensitivity of the prostate cancer antigen 3 test could be improved by the detection of the fusion gene transcripts transmembrane protease serine 2-E26 transformation specific-related gene and serine peptidase inhibitor Kazal type 1 who may in addition allow the identification of prostate cancer patients at higher risk of life-threatening disease. SUMMARY: The challenge in the years to come will be to introduce these new gene-based diagnostic and prognostic tests in algorithms integrating the other known risk factors of age, ethnicity, family history and PSA level to better tailor diagnostic and therapeutic strategies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".