Negative multiparametric magnetic resonance imaging for prostate cancer: further outcome and consequences
Bibliographic record
Abstract
Abstract Purpose EAU guidelines recommend multiparametric MRI of the prostate (mpMRI) prior to biopsy to increase accuracy and reduce biopsies. Whether biopsy can be avoided in case of negative mpMRI remains unclear. Aim of this study is to evaluate predictors of overall prostate cancer (PCa) in negative mpMRI. Methods A total of 216 patients from 2018 to 2020 with suspicion of PCa and negative mpMRI (PI-RADS ≤ 2) were interviewed by telephone about outcome and further follow-up. Clinically significant PCa (csPCa) was defined as ISUP ≥ 2. Patients with vs. without biopsy and with vs. without PCa were compared. Univariate and multivariate analyses were performed to evaluate predictors of PCa occurrence in patients with negative mpMRI. Results 15.7% and 5.1% of patients with PI-RADS ≤ 2 on mpMRI showed PCa and csPCa, respectively. PCa patients had higher PSAD (0.14 vs. 0.09 ng/ml2; p = 0.001) and lower prostate volume (50.5 vs. 74.0 ml; p = 0.003). Patients without biopsy (25%) after MRI were older (69 vs. 65.5 years; p = 0.027), showed lower PSA (5.7 vs. 6.73 ng/ml; p = 0.033) and lower PSA density (0.09 vs. 0.1 ng/ml2; p = 0.027). Multivariate analysis revealed age (OR 1.09 [1.02–1.16]; p = 0.011), prostate volume (OR 0.982 [0.065; 0.997]; p = 0.027), total PSA level (OR 1.22 [1.01–1.47], p = 0.033), free PSA (OR 0.431 [0.177; 0.927]; p = 0.049) and no PI-RADS lesion vs PI-RADS 1–2 lesion (OR 0.38 [0.15–0.91], p = 0.032.) as predictive factors for the endpoint presence of PCa. Conclusions Biopsy for selected patient groups (higher age, prostate volume and free PSA as well as lower PSA-Density) with negative mpMRI can be avoided, if sufficient follow-up care is guaranteed. Detailed counseling regarding residual risk for undetected prostate cancer should be mandatory.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".