Impact of Genomic Classifiers on Risk Stratification and Treatment Intensity in Patients With Localized Prostate Cancer
Bibliographic record
Abstract
BACKGROUND: Tissue-based genomic classifiers (GCs) have been developed to improve prostate cancer (PCa) risk assessment and treatment recommendations. PURPOSE: To summarize the impact of the Decipher, Oncotype DX Genomic Prostate Score (GPS), and Prolaris GCs on risk stratification and patient-clinician decisions on treatment choice among patients with localized PCa considering first-line treatment. DATA SOURCES: MEDLINE, EMBASE, and Web of Science published from January 2010 to August 2024. STUDY SELECTION: Two investigators independently identified studies on risk classification and treatment choice after GC testing for patients with localized PCa considering first-line treatment. DATA EXTRACTION: Relevant data extracted by 1 researcher and overread by a second. Risk of bias (ROB) was assessed in duplicate. DATA SYNTHESIS: Ten studies reported risk reclassification after GC testing. In low ROB observational studies, very low- or low-risk patients with PCa were more likely to have their risk levels classified as the same or lower (GPS, 100% to 88.1%; Decipher, 87.2% to 82.9%; Prolaris, 76.9%). However, 1 randomized trial found that GC testing with GPS reclassified 34.5% of very low-risk and 29.4% of low-risk patients to a higher risk category. Twelve observational studies indicated that treatment decisions after GC testing either remained unchanged or slightly favored active surveillance. In contrast, analyses from a single randomized trial found fewer choices for active surveillance after GPS testing. LIMITATIONS: Heterogeneity in screening patterns, risk-determination cutoffs, pathology, and clinical practices. Studies on treatment choice were moderate to high ROB. CONCLUSION: Although GC tests do not consistently influence risk classification or treatment decisions, the differences observed between observational and randomized studies highlight a need for well-designed trials to explore the role of GC tests in patients with newly diagnosed PCa considering first-line treatment. PRIMARY FUNDING SOURCE: U.S. Department of Veterans Affairs. (PROSPERO: CRD42022347950).
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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 | 0.017 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".