Addition of MRI or DRE to clinical data and risk prediction for clinically significant prostate cancer.
Bibliographic record
Abstract
e17105 Background: In patients suspected of prostate cancer, the decision to perform a biopsy hinges on available clinical data. Magnetic resonance imaging (MRI) and digital rectal exam (DRE) data are informative but may not be available. We created accurate models for clinically significant prostate cancer (csPCa), flexible to MRI and DRE data availability. Methods: Optimized ensembles of calibrated random forest models predicting csPCa (Grade Group ≥2) used total PSA, free PSA, prior negative biopsy status, and age, with or without DRE and MRI data (prostate volume and PI-RADS score). Risk models were derived (training cohorts n=1257 to 2191) and validated (validation cohorts n=317 to 1257) from different clinical sites. Models were evaluated by the area under the receiver operating characteristic curve (ROC AUC), sensitivity, specificity, positive predictive value, and negative predictive value, using thresholds providing ~ 95% sensitivity. Feature importance was determined by SHAP analysis. Results: All models had an AUC of at least 0.80, showing that predicting csPCa can be accurate without MRI or DRE data. Including MRI data significantly increased the AUC in the validation cohort (ClarityDX Prostate 0.80 vs ClarityDX Prostate +MRI 0.87). DRE had moderate value for models without MRI data (ClarityDX Prostate 0.80 vs ClarityDX Prostate +DRE 0.82) and minor value with MRI data (ClarityDX Prostate +MRI vs ClarityDX Prostate +DRE+MRI; AUC 0.87 vs 0.87; specificity 45% vs 47%, Table). Mean absolute SHAP values were highest for PI-RADS and prostate volume. Conclusions: These optimized risk models provide high accuracy for predicting csPCa in various clinical settings. Including MRI data greatly increases model accuracy, while DRE has a smaller effect on model accuracy. [Table: see text]
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.001 |
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".