Diagnostic utility of serum prostate-specific antigen and circulating inflammatory markers for differentiating...

To assess the diagnostic performance of serum prostate-specific antigen (PSA), the Prostate Health Index (PHI), and peripheral blood inflammatory markers (neutrophil-lymphocyte ratio (NLR), lymphocyte-monocyte ratio (LMR), neutrophil-apolipoprotein A1 ratio (NAR) apolipoprotein A1 (ApoA1)) in differentiating prostate cancer (PCa) from biopsy-negative benign prostatic hyperplasia (BPH), and to construct an optimized machine learning diagnostic model.

A retrospective analysis was conducted on 701 patients referred for prostate biopsy between March 2018 and January 2024, including 421 PCa and 280 BPH cases. Patients were divided into training (60%; n=421), validation (20%; n=140), and test (20%; n=140) cohorts. LASSO regression identified key predictors, which were used to develop five machine learning models-logistic regression, decision tree, random forest, support vector machine, and XGBoost. model performance was evaluated using ROC and precision-recall curves, calibration plots, Brier Scores, and decision curve analysis (DCA). AUCs were compared using the DeLong test.

PCa patients exhibited higher PSA, Neu, MONO, NLR, NAR, and PHI but lower ApoA1 and LMR than BPH patients (all P<0.05). XGBoost achieved the best performance (AUC: training 0.994; validation 0.953; test 0.979), significantly surpassing PSA (AUC difference: 0.055-0.118, P<0.001) and PHI (AUC difference: 0.077-0.084, P<0.007). Calibration curves indicated low Brier Scores (0.0326-0.0751) and excellent model fit. DCA confirmed superior clinical benefit. NLR and NAR were major contributors to PCa risk prediction.

The XGBoost model integrating NLR, LMR, and NAR demonstrates superior diagnostic accuracy and clinical utility compared with PSA and PHI, potentially improving pre-biopsy risk stratification and reducing unnecessary invasive procedures.