切换至 "中华医学电子期刊资源库"

中华腔镜泌尿外科杂志(电子版) ›› 2021, Vol. 15 ›› Issue (04) : 275 -279. doi: 10.3877/cma.j.issn.1674-3253.2021.04.002

临床研究

基于多参数磁共振建立前列腺穿刺活检预测模型
卢振权1, 罗兵锋1, 李佩丰1, 袁渊1, 邢亚平1, 罗琳2, 王艳3, 贾亦真4, 侯健1, 梁松武1, 季瑞东1, 罗光彦1, 朱逸飞1,()   
  1. 1. 518058 深圳,香港大学深圳医院泌尿外科
    2. 518058 深圳,香港大学深圳医院放射部
    3. 518058 深圳,香港大学深圳医院病理部
    4. 518058 深圳,香港大学深圳医院中心实验室
  • 收稿日期:2021-04-17 出版日期:2021-08-01
  • 通信作者: 朱逸飞

A predictive model established based on MRI parameters for prostate biopsy in population with PSA greater than 4 ng/ml

Zhenquan Lu1, Bingfeng Luo1, Peifeng Li1, Yuan Yuan1, Yaping Xing1, Lin Luo2, Yan Wang3, Yizhen Jia4, Jian Hou1, Songwu Liang1, Ruidong Ji1, Guangyan Luo1, Yifei Zhu1,()   

  1. 1. Department of Urology, the University of Hong Kong Shenzhen Hospital, Shenzhen 518058, China
    2. Department of Radiology, the University of Hong Kong Shenzhen Hospital, Shenzhen 518058, China
    3. Department of Pathology, the University of Hong Kong Shenzhen Hospital, Shenzhen 518058, China
    4. Department of Central Laboratory, the University of Hong Kong Shenzhen Hospital, Shenzhen 518058, China
  • Received:2021-04-17 Published:2021-08-01
  • Corresponding author: Yifei Zhu
引用本文:

卢振权, 罗兵锋, 李佩丰, 袁渊, 邢亚平, 罗琳, 王艳, 贾亦真, 侯健, 梁松武, 季瑞东, 罗光彦, 朱逸飞. 基于多参数磁共振建立前列腺穿刺活检预测模型[J/OL]. 中华腔镜泌尿外科杂志(电子版), 2021, 15(04): 275-279.

Zhenquan Lu, Bingfeng Luo, Peifeng Li, Yuan Yuan, Yaping Xing, Lin Luo, Yan Wang, Yizhen Jia, Jian Hou, Songwu Liang, Ruidong Ji, Guangyan Luo, Yifei Zhu. A predictive model established based on MRI parameters for prostate biopsy in population with PSA greater than 4 ng/ml[J/OL]. Chinese Journal of Endourology(Electronic Edition), 2021, 15(04): 275-279.

目的

拟建立包含常用临床指标及磁共振参数的前列腺癌穿刺活检预测模型。

方法

回顾性分析2018年1月至2021年2月于香港大学深圳医院行超声引导下前列腺穿刺活检患者的临床资料。对年龄、前列腺体积、直肠指检结果、经直肠前列腺超声结果(TRUS)、fPSA/tPSA、第二版前列腺影像报告和数据系统(PI-RADs v2)评分、动态对比增强、表观弥散系数(ADC)等临床及核磁共振指标进行单因素及多因素分析,并建立前列腺穿刺活检预测模型,计算其ROC曲线下面积诊断效能。

结果

单因素分析显示年龄、前列腺体积、直肠指检结果、前列腺超声结果、ADC、动态对比增强及PI-RADS v2评分差异具有统计学意义(P<0.05),tPSA水平差异无统计学意义(P=0.1)。多因素分析显示fPSA/tPSA、经直肠超声结果、ADC及PI-RADS v2评分为独立危险因素。利用上述指标建立前列腺穿刺活检预测模型为P=1/1+exp[11.1-3.49×(fPSA/tPSA)-2.02×TRUS-1.59×ADC-1.73×PI-RADS]。

结论

基于PI-RADS v2评分及ADC建立前列腺癌穿刺活检预测模型,与单独应用PSA及其他临床指标相比能够提高前列腺癌的诊断效能。

Objective

To establish a prostate cancer biopsy prediction model that includes common clinical indicators, imaging indicators.

Methods

The clinical data of patients who underwent ultrasound-guided prostate biopsy in the University of Hong Kong-Shenzhen Hospital from January 2018 to February 2021 were analyzed retrospectively. Univariate analysis and multivariate analysis were conducted on MRI indicators and clinical indicators which include age, prostate volume, digital rectal examination results, transrectal prostate B-ultrasound results, fPSA/tPSA, PI-RADs v2 scores, dynamic contrast enhancement, apparent diffusion coefficient, etc.. A prostate biopsy prediction model was established and the area under the ROC curve was calculated to judge the diagnostic efficacy.

Result

Univariate analysis showed that differences of age, prostate volume, digital rectal examination results, prostate ultrasound results, apparent diffusion coefficient, dynamic contrast enhancement and PI-RADS v2 score were statistically significant (P<0.05), and there was no difference in tPSA (P=0.1). Multivariate analysis showed that fPSA/tPSA, transrectal ultrasound results, apparent diffusion coefficient and PI-RADS v2 score were independent risk factors. The prediction model of prostate biopsy established by the above indicators is P=1/1+exp[11.1-3.49×(fPSA/tPSA)-2.02×TRUS-1.59×ADC-1.73×PI-RADS].

Conclusion

Compared with only PSA and other clinical indicators, the establishment of a prostate cancer biopsy prediction model based on the PI-RADS v2 score and apparent diffusion coefficient can improve the diagnostic efficiency of prostate cancer.

表1 因变量赋值情况
表2 样本一般特征及单因素分析结果
图1 前列腺癌预测模型及独立危险因素ROC曲线
表3 前列腺癌多因素预测Logistic回归结果
表4 前列腺癌预测模型与独立危险因素ROC曲线比较
表5 恶性肿瘤组患者特征
[1]
Saeter T, Vlatkovic L, Waaler G, et al. Intraductal carcinoma of the prostate on diagnostic needle biopsy predicts prostate cancer mortality: a population-based study[J]. Prostate, 2017, 77(8): 859-865.
[2]
Vasavada SR, Dobbs RW, Kajdacsy-balla AA, et al. Inflammation on prostate needle biopsy is associated with lower prostate cancer risk: a meta-analysis [J]. J Urol, 2018, 199(5): 1174-1181.
[3]
Lavallee LT, Binette A, Witiuk K, et al. Reducing the harm of prostate cancer screening: repeated prostate-specific antigen testing [J]. Mayo Clin Proc, 2016, 91(1): 17-22.
[4]
Yoon DK, Park JY, Yoon S, et al. Can the prostate risk calculator based on Western population be applied to Asian population? [J]. Prostate, 2012, 72(7): 721-729.

URL    
[5]
王淼,王萱,侯惠民, 等. mp-MRI对前列腺癌诊断的敏感性及影响因素-基于前列腺癌病理大切片的单中心临床研究[J]. 中华泌尿外科杂志, 2020, 41(10): 746-751.
[6]
GJLH Van Leenders, dKTH Van, DJ Grignon, et al. The 2019 international society of urological pathology (isup) consensus conference on grading of prostatic carcinoma [J]. Am J Surg Pathol, 2020, 44(8): e87-e99.
[7]
Forte V, Cavallo AU, Bertolo R, et al. PI-RADS score v.2 in predicting malignancy in patients undergoing 5alpha-reductase inhibitor therapy[J]. Prostate Cancer Prostatic Dis, 2021, 24(1): 150-155.
[8]
Zhang L, Tang M, Chen S, et al. A meta-analysis of use of Prostate Imaging Reporting and Data System Version 2 (PI-RADS V2) with multiparametric MR imaging for the detection of prostate cancer[J].Eur Radiol,2017, 27(12): 5204-5214.
[9]
Rudolph MM, Baur ADJ, Cash H, et al. Diagnostic performance of PI-RADS version 2.1 compared to version 2.0 for detection of peripheral and transition zone prostate cancer [J]. Sci Rep, 2020, 10(1): 15982.
[10]
Oto A, Kayhan A, Jiang Y, et al. Prostate cancer: differentiation of central gland cancer from benign prostatic hyperplasia by using diffusion-weighted and dynamic contrast-enhanced MR imaging [J]. Radiology, 2010, 257(3): 715-723.
[11]
Delongchamps NB, Beuvon F, Eiss D, et al. Multiparametric MRI is helpful to predict tumor focality, stage, and size in patients diagnosed with unilateral low-risk prostate cancer [J]. Prostate Cancer Prostatic Dis, 2011, 14(3): 232-237.
[12]
Manetta R, Palumbo P, Gianneramo C, et al. Correlation between adc values and gleason score in evaluation of prostate cancer: multicentre experience and review of the literature[J]. Gland Surg, 2019, 8(Suppl 3): S216-S22.
[13]
Dola EF, Nakhla OL, Genidi EA. Assessing the validity of Prostate Imaging Reporting and Data System version 2 (PI-RADS v2) scoring system in diagnosis of peripheral zone prostate cancer[J]. Eur J Radiol Open, 2017, 4: 19-26.
[14]
Woo S, Suh CH, Kim SY, et al. Diagnostic performance of prostate imaging reporting and data system version 2 for detection of prostate cancer: a systematic review and diagnostic meta-analysis[J]. Eur Urol, 2017, 72(2): 177-188.
[1] 洪玮, 叶细容, 刘枝红, 杨银凤, 吕志红. 超声影像组学联合临床病理特征预测乳腺癌新辅助化疗完全病理缓解的价值[J/OL]. 中华医学超声杂志(电子版), 2024, 21(06): 571-579.
[2] 奚玲, 仝瀚文, 缪骥, 毛永欢, 沈晓菲, 杜峻峰, 刘晔. 基于肌少症构建的造口旁疝危险因素预测模型[J/OL]. 中华普外科手术学杂志(电子版), 2025, 19(01): 48-51.
[3] 李代勤, 刘佩杰. 动态增强磁共振评估中晚期低位直肠癌同步放化疗后疗效及预后的价值[J/OL]. 中华普外科手术学杂志(电子版), 2025, 19(01): 100-103.
[4] 李伟, 宋子健, 赖衍成, 周睿, 吴涵, 邓龙昕, 陈锐. 人工智能应用于前列腺癌患者预后预测的研究现状及展望[J/OL]. 中华腔镜泌尿外科杂志(电子版), 2024, 18(06): 541-546.
[5] 屈勤芳, 束方莲. 盆腔器官脱垂患者盆底重建手术后压力性尿失禁发生的影响因素及列线图预测模型构建[J/OL]. 中华腔镜泌尿外科杂志(电子版), 2024, 18(06): 606-612.
[6] 祝炜安, 林华慧, 吴建杰, 黄炯煅, 吴婷婷, 赖文杰. RDM1通过CDK4促进前列腺癌细胞进展的研究[J/OL]. 中华腔镜泌尿外科杂志(电子版), 2024, 18(06): 618-625.
[7] 王功炜, 李书豪, 魏松, 吕博然, 胡成. 溶瘤病毒M1对不同前列腺癌细胞杀伤效果的研究[J/OL]. 中华腔镜泌尿外科杂志(电子版), 2024, 18(06): 626-632.
[8] 施一辉, 张平新, 朱勇, 杨德林. 机器人辅助前列腺根治术后切缘阳性的研究进展[J/OL]. 中华腔镜泌尿外科杂志(电子版), 2024, 18(06): 633-637.
[9] 公宇, 廖媛, 尚梅. 肝细胞癌TACE术后复发影响因素及预测模型建立[J/OL]. 中华肝脏外科手术学电子杂志, 2024, 13(06): 818-824.
[10] 孙晗, 于冰, 武侠, 周熙朗. 基于循环肿瘤DNA 甲基化的结直肠癌筛查预测模型的构建与验证[J/OL]. 中华消化病与影像杂志(电子版), 2024, 14(06): 500-506.
[11] 张立俊, 孙存杰, 胡春峰, 孟冲, 张辉. MSCT、DCE-MRI 评估术前胃癌TNM 分期的准确性研究[J/OL]. 中华消化病与影像杂志(电子版), 2024, 14(06): 519-523.
[12] 王景明, 王磊, 许小多, 邢文强, 张兆岩, 黄伟敏. 腰椎椎旁肌的研究进展[J/OL]. 中华临床医师杂志(电子版), 2024, 18(09): 846-852.
[13] 韦巧玲, 黄妍, 赵昌, 宋庆峰, 陈祖毅, 黄莹, 蒙嫦, 黄靖. 肝癌微波消融术后中重度疼痛风险预测列线图模型构建及验证[J/OL]. 中华临床医师杂志(电子版), 2024, 18(08): 715-721.
[14] 蔡晓雯, 李慧景, 丘婕, 杨翼帆, 吴素贤, 林玉彤, 何秋娜. 肝癌患者肝动脉化疗栓塞术后疼痛风险预测模型的构建及验证[J/OL]. 中华临床医师杂志(电子版), 2024, 18(08): 722-728.
[15] 董晟, 郎胜坤, 葛新, 孙少君, 薛明宇. 反向休克指数乘以格拉斯哥昏迷评分对老年严重创伤患者发生急性创伤性凝血功能障碍的预测价值[J/OL]. 中华临床医师杂志(电子版), 2024, 18(06): 541-547.
阅读次数
全文


摘要