The role of PSMA PET/CT to predict upgrading in patients undergoing radical prostatectomy for ISUP grade group 1 prostate cancer

The Prostate(2024)

引用 0|浏览16
暂无评分
摘要
Introduction and Objectives To investigate the additive role of prostate-specific membrane antigen (PSMA) positron emission tomography/computed tomography (PET/CT) independent from multiparametric magnetic resonance imaging (mpMRI) and clinical-pathological parameters to predict pathological upgrading in patients with ISUP grade group (GG) 1 prostate cancer (PCa) at prostate biopsy.Materials and Methods A total of 41 patients who underwent robotic radical prostatectomy (RP) for GG1 disease at prostate biopsy with preoperative PSMA PET/CT and mpMRI images available for central review were included in the study. Univariate and multivariate logistic regression analyses were performed to determine the independent predictors of pathological upgrading (GG = 2).Results Final RP pathology revealed upgrading in 26 patients (65.9%); to GG 2 disease in 25 cases and GG 4 disease in one case. International Society of Urological Pathology (ISUP) upgrading rates for prostate imaging-reporting and data system (PIRADS)-5, PIRADS-4, and PIRADS = 3 lesions were 78%, 74%, and 38%, respectively. Fourteen out of 15 (93.3%) patients with an SUVmax = 5.6 and all patients with an SUVmax = 6.5 (n = 10) had pathological upgrading. The upgrading rate in patients with SUV < 5.6 was 46.2% (12/26). Intraprostatic SUVmax = 5.6 was found as the only independent predictor of pathological upgrading in multivariate analysis.Conclusion High prostatic PSMA uptake was found to be a very reliable predictor of pathological upgrading, but low PSMA uptake cannot exclude pathological upgrading. Intraprostatic PSMA uptake along with previously known mpMRI and biopsy-related parameters should be considered when making a treatment decision in patients with GG1 PCa at prostate biopsy.
更多
查看译文
关键词
mpMRI,PET,predicting factors,prostate cancer,PSMA,upgrade
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要