Multiparametric MRI combined with PSA density as a noninvasive rule‐out strategy in active surveillance for prostate cancer
Bibliographic record
Abstract
Abstract Objective To evaluate the diagnostic performance of multiparametric MRI (mpMRI), mpMRI combined with PSA density (PSAd) and combined biopsy (CBx) in detecting clinically significant prostate cancer (csPCa) in men undergoing active surveillance, using radical prostatectomy (RP) specimens as the reference standard. Patients and Methods In this prospective single‐centre study, 91 patients with low‐risk prostate cancer under active surveillance underwent mpMRI, PSAd measurement, CBx and ultimately RP. mpMRI was reported using PI‐RADS v2.0, and PSAd was dichotomised at 0.12 ng/ml/cm3. Diagnostic accuracy was compared using ISUP grade ≥2 and ≥3 thresholds. Radical prostatectomy pathology served as the reference standard. Results For detecting ISUP ≥3 cancer, mpMRI combined with PSAd achieved the highest sensitivity (93.3%) and negative predictive value (94.4%). CBx demonstrated the highest specificity (88.2%) and overall diagnostic balance (Youden index = 0.348). mpMRI alone showed intermediate performance. Differences in classification between strategies were statistically significant (McNemar p < 0.001). Conclusions mpMRI combined with PSAd provides high sensitivity and negative predictive value for ruling out aggressive prostate cancer, supporting its use as a non‐invasive triage tool in active surveillance. CBx remains the most specific method for histological confirmation. These strategies should be used complementarily to optimise decision‐making in active surveillance protocols.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".