A direct comparison of the diagnostic accuracy of three prostate cancer nomograms designed to predict the likelihood of a positive initial transrectal biopsy
Bibliographic record
Abstract
BACKGROUND: Several tools have been developed to predict the outcome of prostate biopsies performed to diagnosis prostate cancer (PCa). However, few studies have focused on the comparative accuracy of these predictive tools. We aim to establish the predictive accuracy of three commonly used nomograms by comparing their prostate biopsy outcome predictions with actual pathological results. METHODS: From January 2008 to December 2010, 708 consecutive patients with an elevated serum PSA level and/or abnormal DRE were referred to our institution. All data were collected prospectively. All patients underwent a TRUS 12-core biopsy. Probability of a positive biopsy was predicted using three online risk calculation nomograms. The discriminative ability of the nomograms was assessed via AUC and the most accurate model was calibrated and compared to actual biopsy results. RESULTS: Of 667 patients fulfilling all three nomograms criteria, 384 (57.5%) had PCa and 283 (42.5%) did not. AUC for the PCPT-CRC, SWOP-PRI, and Montreal nomograms was 0.68 (95% CI, 0.63-0.72), 0.72 (95% CI, 0.68-0.76), and 0.79 (95% CI, 0.76-0.82), respectively. A comparison of the three models' performance showed that the Montreal model provided the greatest predictive accuracy (P = 0.03). CONCLUSIONS: External validation of three commonly used nomograms designed to predict the likelihood of a positive prostate biopsy reveals the Montreal model was more accurate than either the PCPT-CRC or SWOP-PRI models. The Montreal nomogram achieves a diagnostic accuracy of 79% and is superior to PSA alone though we await further research to define the probability (of cancer) threshold above which a prostate biopsy would be advised.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".