Frontiers in oncology · 2026
Role of urinary leukocytes in the risk stratification of prostate cancer using nonlinear stacking learning strategy: a bi-cohort diagnostic study.
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Xia Shou, Ran Zhenchun, Cheng Mengzhe, Zhu Hao, Yang Chunguang, Wu Xinglong
Аннотация
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.
METHODS: We retrospectively included 492 men with elevated PSA levels from Tongji Hospital (n = 415) and Xiangyang Central Hospital (n = 77). All patients underwent transrectal ultrasound-guided prostate biopsy and were classified into low-, intermediate-, and high-risk PCa according to the Gleason score. Clinical variables, including age, BMI, tPSA, f/tPSA, p2PSA, PHI, PHID, and PSAD, were collected and standardized. Feature selection was performed using least absolute shrinkage and selection operator (LASSO) regression combined with bootstrap-based stability analysis. Based on the selected features, we constructed a non-linear stacking ensemble comprising decision tree, logistic regression, support vector machine (SVM), k-nearest neighbors (KNN), and gradient boosting as base learners. Three-class risk stratification models were trained under two scenarios: with and without incorporation of UL. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), macro-F1 score, and the DeLong test. Calibration curves and decision curve analysis (DCA) were applied to quantify the incremental net clinical benefit associated with UL. Predefined subgroup analyses across PSA strata (<10, 10-20, >20 ng/mL) were conducted to examine the context-dependent contribution of UL.
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 center-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.
METHODS: We retrospectively included 492 men with elevated PSA levels from Tongji Hospital (n = 415) and Xiangyang Central Hospital (n = 77). All patients underwent transrectal ultrasound-guided prostate biopsy and were classified into low-, intermediate-, and high-risk PCa according to the Gleason score. Clinical variables, including age, BMI, tPSA, f/tPSA, p2PSA, PHI, PHID, and PSAD, were collected and standardized. Feature selection was performed using least absolute shrinkage and selection operator (LASSO) regression combined with bootstrap-based stability analysis. Based on the selected features, we constructed a non-linear stacking ensemble comprising decision tree, logistic regression, support vector machine (SVM), k-nearest neighbors (KNN), and gradient boosting as base learners. Three-class risk stratification models were trained under two scenarios: with and without incorporation of UL. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), macro-F1 score, and the DeLong test. Calibration curves and decision curve analysis (DCA) were applied to quantify the incremental net clinical benefit associated with UL. Predefined subgroup analyses across PSA strata (<10, 10-20, >20 ng/mL) were conducted to examine the context-dependent contribution of UL.
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 center-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.