### Results

#### The Demographic Characteristics of Patients

Overall, a total of 237 patients were included in this study. The descriptive parameters of the study cohort are shown in Table 1. The total positive number (rate) of needle biopsy was 92 (38.82%). The mean (range) age, BMI, PV, and PSA, of the population was 68.23 (range, 26.00–87.00) years, 22.21 (range, 15.67–31.25) kg/m^{2}, 62.03 (range, 10.91–169.86) mL, and 50.59 (range, 0.31–1,649.69) ng/mL, respectively. The number (%) of the BGSSs ≥6 was 92 (38.82%). The number (%) of mpMRI PI-RADS v2 scores >3 was 118 (49.79%). The mean neutrophil count, lymphocyte count, and NLR were 3.78×10^{9}/L, 1.76×10^{9}/L, and 2.39, respectively. Thirty-four (14.35%) of the patients had diabetes.

#### The Formula and Threshold

Univariate logistic regression tests showed that age, PSAD, DM, and mpMRI PI-RADS v2 scores were independent factors of PCa (Table 2) with ORs of 1.061, 7.557, 24.828, and 5.638, respectively. The OR of mpMRI PI-RADS v2 score was the largest, and it was an extremely robust risk factor for PCa. Multivariate logistic regression analyzed the age, PSAD, diabetes history, and mpMRI PI-RADS v2 score, along with the weighting indexes of these factors for PCa prediction (Table 3). The men with PCa identified by biopsy tended to be older, suffer diabetes, and have higher PSAD levels and mpMRI PI-RADS v2 scores.

Based on the weighting indexes of multivariate logistic regression analysis, we derived the following formula: Y = 2.599 × mpMRI PI-RADS v2 score + 1.766 × diabetes + 0.052 × age + 1.005 × PSAD – 9.119

ROC curves indicated that 0.3543 was the threshold of the formula, which constituted the formula's prediction sensitivity and specificity maximum. When applied to an individual, the Y value is calculated in combination with the patient's age, PSAD, mpMRI PI-RADS v2 score, and presence of diabetes. When the Y value is >0.3543, prostate biopsy is indicated.

#### Comparisons of Different Screening Methods

The ROC curve was used to compare the formula's predictive value for PCa with patients' age, PSAD, and mpMRI PI-RADS v2 score (Figure 1). The thresholds of PSA and PSAD were 10 ng/mL and 0.485 respectively, with the area under the curve (AUC) of these screening methods ranging from 0.810 to 0.912. The detective formula could best detect PCa with an AUC of 0.912, compared with AUCs of 0.810, 0.849, and 0.890 for PSA level, PSAD, and mpMRI PI-RADS v2 score, respectively (Figure 2).

Figure 1.

ROC curves for PSA, PSAD, mpMRI PI-RADS v2 score, and our formula model. (A) PSA, AUC: 0.810; (B) PSAD, AUC: 0.849; (C) mpMRI PI-RADS v2 score, AUC: 0.890; (D) our formula model, AUC: 0.912. ROC, receiver operating characteristic; PSA, prostate-specific antigen; PSAD, PSA density; mpMRI, multiparametric magnetic resonance imaging; PI-RADS, prostate imaging-reporting and data system; AUC, area under the curve.

Figure 2.

Comparison of ROC curves among PSA, PSAD, mpMRI PI-RADS v2 score, and our formula model. The formula model demonstrated the best capacity for the detection of PCa with an AUC of 0.912. ROC, receiver operating characteristic; PSA, prostate-specific antigen; PSAD, PSA density; mpMRI, multiparametric magnetic resonance imaging; PI-RADS, prostate imaging-reporting and data system; PCa, prostate cancer; AUC, area under the curve.

Moreover, we used sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), overall diagnostic accuracy (ODA), positive likelihood ratio (+LR), and negative likelihood ratio (–LR) to analyze and compare the diagnostic value and clinical application value of the formula and other methods (Table 4). PSA had the lowest ODA (56.54%). The detective formulas achieved optimum sensitivity (91.30%), NPV (93.55%), and ODA (84.39%) over those of PSA and PSAD alone. The specificity (80.00%) and PPV (74.34%) of the detective formula were lower than PSAD. +LR of the formula was larger than PSA (4.57/1.20), and the formula achieved the minimum –LR.

Transl Androl Urol. 2020;9(2):574-582. © 2020 AME Publishing Company