Abstract: Objective To evaluate the diagnostic value of the prostate health index (PHI) and prostate imaging reporting and data system (PI-RADS) score for prostate cancer when serum prostate-specific antigen (PSA) is in the gray zone(4-10 μg/L), and to establish a prediction model for the combined diagnosis of prostate cancer. Methods A total 131 patients who underwent transperineal prostate biopsy in Department of Urology at the Affiliated Hospital of Xuzhou Medical University from November 2022 to December 2023 were selected and their clinical data were collected. According to pathological results, the patients were divided into two groups: a prostate cancer group (n=35) and a benign prostatic hyperplasia group (n=96). The value of each variable for predicting prostate cancer was evaluated by binary logistic regression analysis, and a diagnosis model for predicting prostate cancer based on PHI combined with PI-RADS was established (Logit(P)). Receiver operating characteristic (ROC) curves were plotted for each independent variable and the prediction model Logit(P), and the area under the curve (AUC) was compared. The optimal cutoff values for each factor and the prediction model Logit(P) were determined to evaluate their diagnostic performance. Results There were no statistical differences in age, total prostate-specific antigen (tPSA), free prostate-specific antigen ratio (%fPSA), and prostate volume between the two groups (P>0.05). In contrast, significant differences were observed in PHI, prostate-specific antigen isoform 2 (p2PSA), and prostate health index density (PHID) (P<0.05). The AUCs for tPSA, %fPSA, p2PSA, PHI and PHID in diagnosing prostate cancer were 0.565, 0.634, 0.771, 0.812 and 0.802, respectively. The prediction model based on PHI combined with PI-RADS score was Logit(P)=1.117×PI-RADS score+0.086×PHI-7.886. The AUC of the predictive model Logit(P) was 0.848, which was significantly higher than that of each individual indicator (P<0.05). Conclusions PHI has superior diagnostic performance to traditional prostate cancer biomarkers in patients with serum PSA levels of 4-10 μg/L. The prediction model based on PHI combined with PI-RADS score has better diagnostic performance than the individual indicators, which can effectively reduce the rate of missed diagnosis of prostate cancer and unnecessary prostate biopsies.