Predicting Clinically Significant Prostate Cancer with or Without Digital Rectal Exam and MRI Data Using Claritydx Prostate Models
Bibliographic record
Abstract
This prognostic study created optimized ensembles of calibrated random forest models to predict clinically significant prostate cancer (csPCa, grade group ≥2 PCa) using total prostate-specific antigen (PSA), free PSA, negative biopsy status, and age, with or without DRE and MRI data. Observational data were aggregated from cohorts in six organizations in Canada, the USA, and Czechia. Prostate biopsies were performed between 2009 and 2024. Risk models (ClarityDX Prostate + DRE, ClarityDX Prostate + MRI, and ClarityDX Prostate + MRI + DRE) were derived (training cohorts n = 1626 to 2191) and validated (validation cohorts n = 378 to 1318) from different clinical sites. The models had ROC AUC values ≥ 0.80. Adding DRE improved the ROC AUC to 0.82 while models using MRI features had ROC AUC values of 0.87 (without DRE) and 0.88 (with DRE) in the validation cohort. These four ClarityDX Prostate models offer high accuracy in predicting csPCa in individuals in variable clinical settings.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".