Introduction: To evaluate the predictive value of the pan-immune-inflammation value (PIV) and other systemic inflammatory markers, including the neutrophil-to-lymphocyte ratio (NLR), derived neutrophil-to-lymphocyte ratio (dNLR), monocyte-to-lymphocyte ratio (MLR), platelet-to-lymphocyte ratio (PLR), and systemic immune-inflammation index (SII), for prostate cancer (PCa) and clinically significant prostate cancer (CSPCa) in patients with a prostate-specific antigen (PSA) value between 4 and 20 ng/mL. Patients and Methods: The clinical data of 319 eligible patients who underwent prostate biopsies in our hospital from August 2019 to June 2022 were retrospectively analyzed. CSPCa was defined as a “Gleason grade group of ≥2”. A univariable logistic regression analysis and multivariable logistic regression analysis were conducted to analyze the association between the PIV, SII, MLR, and PCa/CSPCa. For the inflammatory indicators included in the multivariable logistic regression analysis, we constructed models by combining the separate inflammatory indicator and other significant predictors and compared the area under the curve (AUC). A nomogram based on the PIV for PCa was developed. Results: We included 148 PCa patients (including 127 CSPCa patients) and 171 non-PCa patients in total. The patients with PCa were older, had higher MLR, SII, PIV, and total PSA (TPSA) values, consumed more alcohol, and had lower free/total PSA (f/T) values than the other patients. Compared with the non-CSPCa group, the CSPCa group had higher BMI, MLR, PIV, TPSA values, consumed more alcohol, and had lower f/T values. The univariable regression analysis showed that drinking history, higher MLR, PIV, and TPSA values, and lower f/T values were independent predictors of PCa and CSPCa. The AUC of the PIV in the multivariable logistic regression model was higher than those of the MLR and SII. In addition, the diagnostic value of the PIV + PSA for PCa was better than the PSA value. However, the diagnostic value for CSPCa was not significantly different from that of using PSA alone, while the AUC of the PIV + PSA was higher than the individual indicator of the PSA value. Conclusions: Our study suggests that for the patients who were diagnosed with PSA values between 4 and 20 ng/mL, the PIV and MLR are potential indicators for predicting PCa and CSPCa. In addition, our study indicates that the new inflammatory index PIV has clinical value in the diagnosis of PCa and CSPCa.

(1) Background: The study aimed to construct nomograms to improve the detection rates of prostate cancer (PCa) and clinically significant prostate cancer (CSPCa) in the Asian population. (2) Methods: This multicenter prospective study included a group of 293 patients from three hospitals. Univariable and multivariable logistic regression analysis was performed to identify potential risk factors and construct nomograms. Discrimination, calibration, and clinical utility were used to assess the performance of the nomogram. The web-based dynamic nomograms were subsequently built based on multivariable logistic analysis. (3) Results: A total of 293 patients were included in our study with 201 negative and 92 positive results in PCa. Four independent predictive factors (age, prostate health index (PHI), prostate volume, and prostate imaging reporting and data system score (PI-RADS)) for PCa were included, and four factors (age, PHI, PI-RADS, and Log PSA Density) for CSPCa were included. The area under the ROC curve (AUC) for PCa was 0.902 in the training cohort and 0.869 in the validation cohort. The AUC for CSPCa was 0.896 in the training cohort and 0.890 in the validation cohort. (4) Conclusions: The combined diagnosis of PHI and PI-RADS can avoid more unnecessary biopsies and improve the detection rate of PCa and CSPCa. The nomogram with the combination of age, PHI, PV, and PI-RADS could improve the detection of PCa, and the nomogram with the combination of age, PHI, PI-RADS, and Log PSAD could improve the detection of CSPCa.