BACKGROUND: The global incidence of prostate cancer (PCa) is rising, necessitating improved diagnostic strategies. This study explores coagulation parameters' predictive value for clinically significant PCa (csPCa) and develops a nomogram. RESEARCH DESIGN AND METHODS: This study retrospectively analyzed data from 702 patients who underwent prostate biopsy at Shandong Provincial Hospital (SDPH) and 142 patients at Shandong Cancer Hospital and Institute (SDCHI). SDPH patients were randomly assigned at a 7:3 ratio for internal validation, while SDCHI data served as external validation. LASSO and logistic regression identified the best predictive factors for csPCa, which were used to construct a model. The model's efficacy was tested using AUC, calibration curves, and decision curve analysis. RESULTS: TPSA, age, D-dimer, prostate volume (PV), and digital rectal examination (DRE) were identified as independent risk factors for csPCa. A predictive model was constructed using a nomogram. The AUC for the training set was 0.841, for internal validation 0.809, and for external validation 0.814. Calibration and decision curves confirmed the model's clinical utility. CONCLUSIONS: The nomogram incorporating D-dimer, TPSA, age, PV, and DRE provides a highly accurate tool for assessing csPCa risk in individuals with PSA levels of 4-20 ng/mL, supporting personalized diagnostics and clinical decision-making.