Predictive factors for negative prostate biopsy in PI-RADS 5 patients: a multivariate analysis of clinical and...

Multiparametric MRI (mpMRI) is crucial for diagnosing prostate cancer, with PI-RADS 5 lesions indicating a high likelihood of clinically significant prostate cancer (csPCa). However, some patients with PI-RADS 5 lesions have negative biopsies, and optimal management remains unclear. This study aims to identify clinical and imaging factors predicting negative biopsy outcomes in these cases.

We conducted a retrospective analysis of a prospectively maintained single-center cohort of patients with PI-RADS 5 lesions who underwent transrectal cognitive fusion-guided prostate biopsy between November 2016 and December 2024. All procedures included systematic 12-core sampling plus targeted biopsies of mpMRI-detected lesions. Clinical and imaging variables-including age, PSA, PSA density, DRE, prostate volume, prior negative biopsy, lesion location, and TRUS findings-were evaluated as potential predictors of negative biopsy outcomes using univariate and multivariate logistic regression, and ROC curve analysis.

A study of 990 patients with PI-RADS 5 lesions found that 113 (11.4%) had negative biopsies. Among the 863 diagnosed with prostate cancer, 92.8% (801) had csPCa. Independent predictors of negative biopsy included prostate volume > 60 cc (22.3%), normal DRE (20.0%), prior negative biopsy (27.9%), transition zone (TZ) lesion location (25.8%), and absence of a suspicious hypoechoic lesion on TRUS (19.9%). The likelihood of a negative biopsy increased with the combination of these factors-rising to 64.3% when all were present. Multivariate analysis confirmed these as independent predictors, with their combination enhancing predictive accuracy (AUC ≥ 0.84).

Despite the high probability of clinically significant prostate cancer in PI-RADS 5 lesions, approximately 10-12% of cases result in negative biopsy findings. Our results indicate that combining prostate volume, lesion location, DRE findings, and TRUS characteristics allows for improved risk stratification. These predictive factors can improve both pre- and post-biopsy decision-making by helping tailor follow-up strategies such as repeat biopsy, mpMRI monitoring, or PSA testing to each individual patient.