Prevalence of Biopsy-Proven Clinically Significant Prostate Cancer in Patients with PI-RADS 3 on MRI and Factors Associated with It
Bibliographic record
Abstract
Background: To assess prevalence of prostate cancer and clinically significant prostate cancer in patients with PI-RADS 3 on bpMRI and factors associated with them. Methods: Patients suspicious for prostate cancer following serum Prostate Specific Antigen (PSA) screening, who had PI-RADS 3 on bpMRI, were included. All patients underwent systemic plus MRI targeted biopsy of prostate. Gleason score 3+3 was considered positive for prostate cancer but clinically non-significant one. Higher scores were pertained to as clinically significant prostate cancer. The relationship between patient age, PSA level, PSA density, number of core biopsies, and number of PI-RADS 3 lesions on bpMRI with presence of prostate cancer per se and presence of clinically significant prostate cancer in our patients is assessed. Results: 244 patients were enrolled. 101 patients had prostate cancer (41.4%). Out of these 101 patients, 34 (13.9% of total) had clinically significant prostate cancer. Among different factors, only PSA density was associated with both prostate cancer (OR=1.05, p=0.001) and clinically significant prostate cancer (OR=1.03, p=0.001). According to receiver operating characteristic curve analysis, best cut off value of PSA density which has highest association with clinically significant prostate cancer in PI-RADS 3 patients would be 0.36; with a sensitivity of 0.38 and specificity of 0.93. Conclusion: Considering PSA density with threshold of 0.36 for performing biopsy in patients with PI-RADS 3 on bpMRI might lower the rate of unnecessary biopsies.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".