Clinically Significant ISUP Upgrading in the Multiparametric MRI Era: Biopsy Tumor Burden Outperforms Complex Machine Learning Models in a Single-Center Exploratory Cohort

<b>Background/Objectives</b>: Despite multiparametric MRI (mpMRI)-guided biopsy, clinically significant upgrading (CSU) of ISUP Grade Group (GG) at radical prostatectomy (RP) remains common in prostate cancer (PCa). We aimed to identify predictors of CSU (biopsy GG ≤ 2 to RP GG ≥ 3) using routine preoperative variables, and to benchmark a parsimonious logistic model against multiple machine learning (ML) classifiers. <b>Methods</b>: In this single-center exploratory analysis, 96 consecutive PCa patients underwent pre-biopsy mpMRI, systematic ± MRI-targeted biopsy, and RP. Predictive modeling was restricted to biopsy GG 1-2 patients (<i>n</i> = 64). LASSO-guided feature selection and Firth-penalized logistic regression were used to build a locked reference model, evaluated against ML classifiers using cross-validated discrimination, calibration, and decision curve analysis. <b>Results</b>: CSU occurred in 10/64 patients (15.6%). Positive core ratio was the dominant independent predictor (adjusted OR 1.54 per 10% increase, 95% CI 1.10-2.17). PSA density (PSAD) showed a consistent positive association but did not retain independent significance. The locked two-variable model (AUC ≈ 0.75-0.79) outperformed all ML classifiers in discrimination, calibration, and net clinical benefit; however, the limited event count (n = 10) constrains model stability, and these findings require external validation. <b>Conclusions</b>: In a PCa mpMRI-informed diagnostic pathway, CSU is primarily driven by biopsy tumor burden. A simple logistic model based on positive core ratio and PSAD outperformed more complex ML approaches in this exploratory cohort, supporting integration of biopsy tumor burden metrics into preoperative risk stratification pending external validation.