Assessing the Role of High-resolution Microultrasound Among Naïve Patients with Negative Multiparametric Magnetic Resonance Imaging and a Persistently High Suspicion of Prostate Cancer
Bibliographic record
Abstract
Background: Multiparametric magnetic resonance imaging (mpMRI) is an invaluable diagnostic tool in the decision-making for prostate biopsies (PBx). However, a non-negligible proportion of patients with negative MRI (nMRI) may still harbour prostate cancer (PCa). Objective: To assess whether microultrasound (micro-US) can help in substratifying the presence of PCa and clinically significant PCa (csPCa; ie, any Gleason score ≥7 PCa) in patients with nMRI despite a persistently high clinical suspicion of PCa. Design setting and participants: A total of 125 biopsy-naïve patients who underwent micro-US-guided PBx with the ExactVu system for a persistently high suspicion of PCa despite nMRI were prospectively enrolled. Intervention: The Prostate Risk Identification using micro-US (PRI-MUS) protocol was used to identify suspicious areas; PBx included targeted sampling of PRI-MUS ≥3 areas and systematic sampling. Outcome measurements and statistical analysis: The primary endpoint was the assessment of micro-US diagnostic accuracy in detecting csPCa. Secondary endpoints included determining the proportion of patients with nMRI who may avoid PBx after micro-US or transrectal US, presence of cribriform and intraductal patterns on biopsy core examination, predictors of csPCa in patients presenting with nMRI, and comparing micro-US-targeted and systematic PBx in identifying csPCa. Results and limitations: Considering csPCa detection rate, micro-US showed optimal sensitivity and negative predictive value (respectively, 97.1% and 96.4%), while specificity and positive predictive value were 29.7% and 34.0%, respectively. Twenty-eight (22.4%) patients with a negative micro-US examination could have avoided PBx with one (2.9%) missed csPCa. Cribriform and intraductal patterns were found in 14 (41.2%) and four (11.8%) of csPCa patients, respectively. In multivariable logistic regression models, positive micro-US, age, digital rectal examination, and prostate-specific antigen density ≥0.15 emerged as independent predictors of PCa. Targeted and systematic sampling identified 33 (97.1%) and 26 (76.5%) csPCa cases, respectively. The main limitation of the current study is represented by its retrospective single-centre nature on an operator-dependent technology. Conclusions: Micro-US represents a valuable tool to rule out the presence of csPCa among patients with a persistent clinical suspicion despite nMRI. Patient summary: According to our results, microultrasound (micro-US) may represent an effective tool for the diagnosis of clinically significant prostate cancer in patients with negative magnetic resonance imaging (nMRI), providing high sensitivity and negative predictive value. Further randomised studies are needed to confirm the potential role of micro-US in the diagnostic pathway of patients with a persistent suspicion of prostate cancer despite nMRI.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".