The predictive study of ultrasound parameters combined with serological indicators for Gleason score risk after prostate cancer surgery / 中华超声影像学杂志

ABSTRACT

Objective:To establish the prediction model of postoperative Gleason score (GS) risk of prostatic cancer (PCa), and to compare the diagnostic efficacy of the model and each independent risk factor for PCa medium-high risk group.Methods:The clinical data of 362 patients who accepted transrectal prostate biopsy in the Run Run Shaw Hospital Affiliated to Zhejiang University School of Medicine from January 2018 to December 2019 were analyzed retrospectively, and a total of 343 patients with prostate cancer who met the enrollment criteria were selected. According to the GS grading system, these patients were divided into low risk group, moderate risk group and high risk group. At first, the single factor analysis and Spearman rank correlation were used to find out the effective indicators with good correlation with GS risk. Then, multiple linear regression equation was applied for multi-factor analysis to obtain the independent risk factors and the prediction model for predicting GS risk, and then the ROC curve was used to compare the diagnostic efficacy of each independent risk factor and prediction model for PCa medium-high risk group.Results:In the single factor analysis, the differences of all indicators in GS risk were statistically significant (all P<0.05). In the correlation analysis with GS risk, except for the indicators of prostate volume (all P>0.05), the other indexes had linear correlations with the different risks of GS (all P<0.05). Among them, the total prostate specific antigen and two-dimensional ultrasound (2D-US) score showed moderate positive correlations( rs=0.402, 0.579, all P<0.001), contrast enhanced ultrasound (CEUS) score showed a high positive correlation ( rs=0.709, P<0.001), and the rest indexes showed low positive correlations. Multiple linear regression was used to obtain two independent risk factors of 2D-US score ( X1) and CEUS score ( X2) for the prediction of GS risk, then, a prediction model was established Y=0.863+ 0.066 X1+ 0.27 X2, the corresponding linear coefficient differences were statistically significant(all P<0.05). By the ROC analysis, the areas under the curves of 2D-US score, CEUS score and the prediction model were 0.838, 0.906 and 0.907, respectively. Conclusions:2D-US score and CEUS score are independent risk factors for predicting postoperative GS risk, and the diagnostic efficacy of the prediction model is higher than those of the 2D-US score and CEUS score for the medium-high risk group.