Background/Objectives: ISUP grade group discordance between prostate biopsy and radical prostatectomy (RP) impacts treatment decisions in over a third (~25–40%) of prostate cancer (PCa) patients. We aimed to identify ISUP grade migration predictors and assess the impact of preoperative imaging (MRI) in a contemporary Romanian PCa cohort.Methods: We retrospectively analyzed 142 PCa patients undergoing RP following biopsy between January 2021 and December 2024 at Pius Brinzeu County Hospital, Timișoara: 90 without and 52 with preoperative MRI. Clinical parameters, MRI findings (PI-RADS), and biopsy characteristics were evaluated. Machine learning models (gradient boosting, random forest) were developed with SHAP analysis for interpretability.Results: Grade migration occurred in 69/142 patients (48.6%): upstaging in 55 (38.7%) and downstaging in 14 (9.9%). In the non-MRI cohort, 37/90 (41.1%) were upstaged and 9/90 (10.0%) were downstaged, versus 18/52 (34.6%) upstaged and 5/52 (9.6%) downstaged in the MRI cohort. The MRI group showed a 6.5% absolute reduction in upstaging (34.6% vs. 41.1%), a promising non-significant trend (p= 0.469) that requires further investigation. Grade 1 patients showed the highest upstaging (69.4%), while Grades 3–4 showed the highest downstaging (11/43, 25.6%). PI-RADS 4 lesions had the highest upstaging (43.5%). PSA density > 0.20 ng/mL2emerged as the strongest predictor. Gradient boosting achieved superior performance (AUC = 0.812) versus logistic regression (AUC = 0.721), representing a 13% improvement in discrimination. SHAP analysis revealed PSA density as the most influential (importance: 0.287). Grade migration associated with adverse pathology: extracapsular extension (52.7% vs. 28.7%,p= 0.008) and positive margins (38.2% vs. 21.8%,p= 0.045).Conclusions: ISUP grade migration affects 48.6% of Romanian patients, with 38.7% upstaged and 9.9% downstaged. The 69.4% upstaging in Grade 1 patients emphasizes the need for enhanced risk stratification tools, while 10% downstaging suggests potential overtreatment. Machine learning with SHAP analysis provides superior predictive performance (13% AUC improvement) while offering clinically interpretable risk assessments. PSA density dominates risk assessment, while PI-RADS 4 lesions warrant closer scrutiny than previously recognized.
**背景/目的：** 前列腺穿刺活检与根治性前列腺切除术（RP）之间的国际泌尿病理学会（ISUP）分级分组不一致，影响了超过三分之一（约25–40%）前列腺癌（PCa）患者的治疗决策。本研究旨在识别ISUP分级迁移的预测因素，并评估术前影像学（MRI）在当代罗马尼亚PCa队列中的影响。 **方法：** 我们回顾性分析了2021年1月至2024年12月期间在蒂米什瓦拉Pius Brinzeu县医院接受活检后行RP的142例PCa患者：其中90例未行术前MRI，52例行术前MRI。评估了临床参数、MRI发现（PI-RADS评分）和活检特征。开发了机器学习模型（梯度提升、随机森林），并采用SHAP分析以提高可解释性。 **结果：** 142例患者中有69例（48.6%）发生分级迁移：其中55例（38.7%）升级，14例（9.9%）降级。在非MRI队列中，37/90例（41.1%）升级，9/90例（10.0%）降级；而在MRI队列中，18/52例（34.6%）升级，5/52例（9.6%）降级。MRI组显示出升级率绝对降低了6.5%（34.6% vs. 41.1%），这是一个有前景但不显著的差异趋势（p=0.469），需要进一步研究。1级患者升级率最高（69.4%），而3-4级患者降级率最高（11/43，25.6%）。PI-RADS 4级病灶升级率最高（43.5%）。PSA密度 > 0.20 ng/mL²成为最强的预测因子。梯度提升模型（AUC = 0.812）相较于逻辑回归（AUC = 0.721）表现出更优的性能，区分度提高了13%。SHAP分析显示PSA密度最具影响力（重要性：0.287）。分级迁移与不良病理特征相关：包膜外侵犯（52.7% vs. 28.7%，p=0.008）和切缘阳性（38.2% vs. 21.8%，p=0.045）。 **结论：** ISUP分级迁移影响了48.6%的罗马尼亚患者，其中38.7%升级，9.9%降级。1级患者高达69.4%的升级率强调了需要增强风险分层工具，而10%的降级率则提示可能存在过度治疗。结合SHAP分析的机器学习提供了更优的预测性能（AUC提高13%），同时提供了临床可解释的风险评估。PSA密度在风险评估中占主导地位，而PI-RADS 4级病灶需要比以往认识更密切的关注。
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