Role of Urinary leukocytes in the risk stratification of prostate cancer using Nonlinear Stacking Learning Strategy: a bi-cohort diagnostic study

Mengzhe Cheng¹ZhenChun Ran²Hao Zhu²Chunguang Yang³Xinglong Wu^2*

• ¹Aerospace Nanhu Electronic Information Technology Co., Ltd.，Jingzhou，434007，China, Jingzhou, China
• ²Wuhan Institute of Technology, Wuhan, China
• ³Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China, Wuhan, China

Objective This study aimed to develop a non-linear stacking ensemble learning framework to evaluate the incremental diagnostic contribution of urinary leukocytes (UL) in prostate cancer (PCa) risk stratification, with a primary focus on predictive performance and clinical utility. Results In the baseline setting without UL, the non-linear stacking model achieved AUCs of 0.962 and 0.928 in the internal and external cohorts, respectively, indicating robust discriminative performance. After incorporating UL, several base learners—particularly decision tree, KNN, and gradient boosting—demonstrated centre-specific AUC improvements ranging from 0.003 to 0.02 (p < 0.05), accompanied by consistently increased net clinical benefit on DCA. Subgroup analyses showed that the incremental value of UL was most evident in patients with intermediate PSA levels (4–10 ng/mL) and in those with clinical features suggestive of benign prostatic hyperplasia. Post hoc SHapley Additive exPlanations (SHAP) analyses performed on a representative base learner indicated that UL exerted a modest but directionally consistent influence on high-risk predictions, complementing established PSA-derived indices rather than acting as a dominant independent driver. Conclusion Within a stacking ensemble–based risk stratification framework primarily optimized for predictive performance, urinary leukocytes provide a clinically meaningful auxiliary signal that improves discrimination and net benefit in specific PSA-defined subgroups. These findings support the use of UL as a complementary inflammation-related marker in PCa risk assessment, while interpretability is best understood at the level of base learners and original clinical features rather than the full ensemble model.