Multifactorial diagnostic model combining SAT-PCA3 in prostate cancer

To investigate the feasibility of Simultaneous Amplification and Testing PCA3 (SAT-PCA3, a urine-based prostate cancer-specific biomarker) combined with conventional clinical information in the diagnosis of prostate cancer (PCa).

This retrospective study analyzed 137 patients with complete clinical data. Patients with a biopsy Gleason score ≥ 6 were classified as having PCa. Clinical indicators showing significant differences between PCa and non-PCa groups were identified via univariate analysis. A multivariate model was constructed using pathological diagnosis as the outcome and age, digital rectal exam (DRE) result, prostate-specific antigen (PSA), SAT-PCA3 result, and Prostate Imaging Reporting and Data System (PIRADS) score as predictors. The DeLong test was performed to compare differences in the area under the receiver operating characteristic (ROC) area under the curve (AUC) between the univariate model and the multivariate model.

A total of 137 patients were included: 65 were diagnosed with PCa and 72 were non-PCa. Statistical differences existed between the PCa and non-PCa in age, PSA, DRE, PIRADS score, and SAT-PCA3 (p < 0.05). All variables were independently associated with PCa. The coefficient of determination (R²) values is 0.626 in the multivariate model. The AUCs of the age [0.711(95%CI: 0.625-0.796)], DRE [0.626(95%CI: 0.545-0.708)], PSA [0.684(95%CI: 0.593-0.774)], SAT-PCA3 [0.786(95%CI: 0.706-0.866)], PIRADS [0.795(95%CI: 0.722-0.866)] were all less than the multivariate model [0.912(95%CI: 0.866-0.958)], and the difference was statistically significant (p < 0.001).

A diagnostic model combining conventional clinical information (age, DRE, PSA, PIRADS score) with the SAT-PCA3 significantly improves the diagnostic accuracy for prostate cancer compared to any single parameter alone.