MRI Evaluation of Benign Prostatic Hyperplasia: Correlation with International Prostate Symptom Score

作者：Guneyli, Serkan^*; Ward, Emily; Peng, Yahui; Yousuf, Ambereen Nehal; Trilisky, Igor; Westin, Charles; Antic, Tatjana; Oto, Aytekin

来源：Journal of Magnetic Resonance Imaging, 2017, 45(3): 917-925.

DOI：10.1002/jmri.25418

• BLADDER OUTLET OBSTRUCTION
• TRANSITION ZONE INDEX
• TRANSRECTAL ULTRASOUND
• VOLUME
• ANTIGEN
• CANCER
• GLAND
• FLOW

Purpose: To investigate the correlation between magnetic resonance imaging (MRI)-derived prostate parameters and benign prostatic hyperplasia (BPH) type with the International Prostate Symptom Score (IPSS). @@@ Materials and Methods: In all, 61 patients (median age, 60; range, 41-81 years) who underwent preoperative MRI and prostatectomy were included in this retrospective study. The MRI-based parameters including total prostate volume (TPV), transition zone (TZ) volume (TZV), TZ index, intravesical prostatic protrusion (IPP), the anterior fibromuscular stroma (AFMS) distance, prostatic urethral angle, bladder wall thickness, urethral wall thickness, urethral compression, urethral wall changes, and BPH type were correlated with total IPSS, IPSS-storage symptom (IPSS-ss), IPSS-voiding symptom (IPSS-vs), and responses to the individual IPSS questions using Spearman () or Pearson (r) correlation coefficients, one-way analysis of variance (ANOVA), and multiple linear regression. @@@ Results: TPV (r=0.414, P=0.001), TZV (r=0.405, P=0.001), IPP (r=0.270, P=0.04), and AFMS distance (r=0.363, P=0.004) correlated with total IPSS. In multiple linear regression analysis, TZV was the only predictor for total IPSS (P=0.001), IPSS-ss (P < 0.001), IPSS-vs (P=0.03), and the scores for the IPSS questions 1 (P=0.03) and 4 (P=0.001). TPV was a predictor of the scores for questions 2 (P=0.003), 3 (P=0.009), and 7 (P < 0.001). @@@ Conclusion: Several MRI-derived prostate measurements (TPV, TZV, IPP, AFMS distance) correlated with total IPSS. TZV was the only predictor for total IPSS based on multiple regression analysis.

• 出版日期2017-3
• 单位北京交通大学