ABSTRACT
OBJECTIVES: This study aimed to assess the efficacy of multiparametric ultrasonography (mpUS) combined with serological examination, as a non-invasive method, in detecting prostate cancer (PCa) or high-grade prostate cancer (HGPCa) respectively. METHODS: A cohort of 245 individuals with clinically suspected PCa were enrolled. All subjects underwent a comprehensive evaluation, including basic data collection, serological testing, mpUS and prostate biopsy. Random Forest (RF) models were developed, and the mean area under the curve (AUC) in 100 cross-validations was used to assess the performance in distinguishing PCa from HGPCa. RESULTS: mpUS features showed significant differences (p < 0.001) between the PCa and non-PCa groups, as well as between the HGPCa and low-grade prostate cancer (LGPCa) groups including prostate-specific antigen density (PSAD), transrectal real-time elastography (TRTE) and intensity difference (ID). The RF model, based on these features, demonstrated an excellent discriminative ability for PCa with a mean area under the curve (AUC) of 0.896. Additionally, another model incorporating free prostate-specific antigen (FPSA) and color Doppler flow imaging (CDFI) achieved a high accuracy in predicting HGPCa with a mean AUC of 0.830. The nomogram derived from these models exhibited excellent individualized prediction of PCa and HGPCa. CONCLUSION: The RF models incorporating mpUS and serological variables achieved satisfactory accuracies in predicting PCa and HGPCa.
