Multivariate analysis of prediction prostate cancer based on the contrast-enhanced transrectal ultrasonography and clinical data

Objective To explore the value of transrectal contrast-enhanced ultrasonography(TR-CEUS) and clinical data in prediction prostate cancer (PCa). Methods The clinical and images data of 152 patients who highly suspected with PCa were analyzed retrospectively. All patients were not treated before operation, in addition, TR-CEUS and prostate biopsy were taken. To analyze images with time intensity curves(TIC) analysis software, 6 parameters including arrival time(RT), time-to-peak(TTP), peak intensity(PI) and so on were measured. According to the results of pathological findings and images, the patients were classified into PCa and benign prostatic hyperplasia(BPH) group. The pathological result was used as the dependent variable, the P<0.30 variables as independent variables, multivariate analysis was performed using Logistic regression, the statistically significant factors were used to establish a diagnosis model, construct ROC curve and calculate the area under the curve (AUC). Results Of total 152 patients, BPH accounted for 54.6%(83/152), PCa accounted for 45.4%(69/152). In the single factor analysis, the RT of TR-CEUS parameter in PCa group was lower than that in BPH group (P=0.021), and the PI was higher than that in BPH group (P=0.005). The single factor analysis of P<0.30 variables including age, volume, PSA, RT, PI, MTT, AUC and TTP eight variables were used in Logistic regression, the results showed that PSA, PI and AUC were independent risk factors in prediction PCa (P<0.05), established a diagnosis model. The area under ROC curve was 0.797 (P<0.001), the diagnosis model of boundary value was 0.383, the PCa forecast sensitivity was 77.9%, specificity was 79.5%, Youden index was 57.5%, the positive predictive value was 65.1%, and the negative predictive value was 81.5%. Conclusions In the TR-CEUS parameter, PI and AUC are independent predictors in prediction PCa, and the establishment of a diagnostic model combined with serum PSA has a certain clinical value in predicting PCa. Key words: Contrast-enhanced ultrasound; Prostatic neoplasms; Logistic regression