摘要
目的观察^(18)F-PSMA-1007 PET/CT最大标准摄取值(SUV_(max))预测高危险度前列腺癌(PCa)的价值。方法回顾性分析经病理证实且接受^(18)F-PSMA-1007 PET/CT检查的68例PCa患者,依据D'Amico危险分层标准将其分为低-中危组和高危组,比较组间各参数差异。以SUV_(max)构建高危PCa的Logistic回归模型,利用受试者工作特征(ROC)曲线评价其诊断效能。结果68例PCa的SUV_(max)为25.652(16.670,38.355);术前Gleason评分为8(7,9)分,术前平均总前列腺特异性抗原(tPSA)水平29.014(14.075,127.157)ng/ml。高危组(n=33)SUV_(max)为28.681(17.514,39.950)、tPSA为47.965(27.210,170.575)ng/ml,均高于低-中危组(n=35)的17.415(8.795,28.675)和13.345(6.958,18.443)ng/ml(P均<0.05)。以SUV_(max)建立的二分类Logistic回归模型预测高危PCa的ROC曲线下面积为0.773[95%CI(0.652,0.894)],截断值取0.78时,敏感度和特异度分别为81.62%和68.24%。结论^(18)F-PSMA-1007 PET/CT所示SUV_(max)可作为高危险度PCa的独立预测因子,为制定治疗方案及随访提供参考。
Objective To explore the value of^(18)F-PSMA-1007 PET/CT maximum standard uptake value(SUV_(max))for predicting high risk stratification prostate cancer(PCa).Methods Data of 68 patients PCa proved by biopsy pathology who underwent^(18)F-PSMA-1007 PET/CT scanning were retrospectively analyzed.According to D'Amico risk criteria,the patients were divided into low-medium risk group(n=35)and high risk group(n=33).Then relative parameters were compared between groups.A Logistic regression model for high risk PCa was established based on SUV_(max),and the diagnostic efficiency of this model was evaluated with receiver operating characteristic(ROC)curve analysis.Results The median SUV_(max)was 25.652(16.670,38.355),the median preoperative Gleason score was 8(7,9),and the median preoperative total prostate-specific antigen(tPSA)was 29.014(14.075,127.157)ng/ml of 68 cases of PCa.SUV_(max)was 28.681(17.514,39.950)and tPSA was 47.965(27.210,170.575)ng/ml in high risk group,higher than those in low-medium risk group(17.415[8.795,28.675]and 13.345[6.958,18.443]ng/ml,both P<0.05).The area under ROC curve of the Logistic regression model based on SUV_(max)was 0.773(95%CI[0.652,0.894]).Taken 0.78 as the cut-off value,the sensitivity and specificity was 81.62%and 68.24%,respectively.Conclusion SUV_(max)of^(18)F-PSMA-1007 PET/CT could be used as an independent predictor of high-risk PCa,hence providing references for treatment planning and following-up.
作者
白璐
郑安琪
李运轩
王卓楠
高俊刚
沈聪
高凡
段小艺
BAI Lu;ZHENG Anqi;LI Yunxuan;WANG Zhuonan;GAO Jungang;SHEN Cong;GAO Fan;DUAN Xiaoyi(Department of Medical Imaging,2.Clinical Research Center,the First Affiliated Hospital of Xi'an Jiaotong University,Xi'an 710061,China)
出处
《中国医学影像技术》
CSCD
北大核心
2021年第9期1386-1390,共5页
Chinese Journal of Medical Imaging Technology
基金
西安交通大学第一附属医院临床研究项目(XJTU1AF-CRF-2020-008)
西安交通大学第一附属医院新医疗新技术(XJYFY-2019J1)。