A Multimarker Model for Prostate Cancer Risk Assessment: Improving Diagnostic Accuracy Beyond PSA.

Abstract

Objective: This study aimed to evaluate the association between biochemical markers and prostate cancer (PCa) risk by analyzing patients with benign prostatic hyperplasia (BPH) and PCa. Additionally, the study sought to assess the diagnostic accuracy of a multimarker model compared to prostate-specific antigen (PSA) alone.

Methods: A cross-sectional study was conducted with data from 2931 patients (1374 with BPH and 1557 with PCa) from the Prostate Cancer Data Set of the National Population Health Data Center. Biochemical markers, including PSA, apolipoproteins, lipid profiles, and metabolic markers (calcium and phosphate), were analyzed. Univariate and multivariate logistic regression analyses were performed to assess the associations with PCa risk. The diagnostic performance of the multimarker model was evaluated using receiver operating characteristic (ROC) curve analysis.

Results: Total PSA levels were significantly higher in PCa patients, and the free/total PSA ratio was lower (p < 0.001). Apolipoprotein A1, LDL cholesterol, calcium, and phosphate were also significantly associated with PCa risk (p < 0.001). The multivariate logistic regression model, incorporating multiple markers, showed improved diagnostic accuracy (AUC 0.731, 95% CI: 0.713-0.749), with sensitivity of 68.4% and specificity of 65.8%.

Conclusions: Combining multiple biochemical markers with PSA enhances the diagnostic accuracy for PCa, offering additional predictive value. This multimarker approach has the potential to improve PCa screening and reduce unnecessary biopsies.