Value of Gailina nomogram in predicting seminal vesicle invasion in prostate cancer
10.3760/cma.j.issn.1000-6702.2013.05.013
- VernacularTitle:Gallina列线图预测前列腺癌精囊浸润的应用价值
- Author:
Chao GAO
;
Zhihong ZHANG
;
Yong XU
;
Zhifei LIU
;
Lei QIAO
;
Tao ZHANG
- Publication Type:Journal Article
- Keywords:
Prostatic neoplasms;
Seminal vesicle invasion;
Nomograms;
Partin tables;
Prediction
- From:
Chinese Journal of Urology
2013;(5):369-373
- CountryChina
- Language:Chinese
-
Abstract:
Objective To evaluate the accuracy of Gallina nomogram in predicting seminal vesicle invasion (SVI) in prostate cancer.Methods From January 2009 to December 2011,89 patients with prostate carcinoma underwent open retropubic or laparoscopic radical prostatectomy.Complete data of preoperative serum prostate specific antigen (PSA),clinical stage,biopsy Gleason score,percentage of positive biopsy cores,pelvic MRI and pathological report of prostatectomy specimen were collected,and all the patients met the inclusion criteria of Gallina nomogram,2001 Partin tables and 2007 Partin tables.Postoperative pathological results were respectively compared with MRI and the incidence of SVI predicted by the three tools,and the sensitivity,specificity and accuracy of MRI in predicting SVI were calculated.The receiver operating characteristics curves were performed to test the predictive accuracy of SVI of each tool.Results The incidences of organ-confined disease,capsule invasion,SVI and lymph node metastasis were 58.4％,16.9％,22.5％,and 2.2％,respectively.The sensitivity,specificity and accuracy of MRI in predicting SVI was 45.0％ (9/20),71.0％ (49/69) and 65.2％ (58/89),respectively.The area under the curve (AUC) for SVI disease prediction of 2001 Partin tables,2007 Partin tables and Gallina nomogram was 0.712,0.711 and 0.801,respectively.Conclusions The sensitivity of MRI in predicting SVI is poor,the specificity and accuracy are common.All the predictive tools have a reasonable value for SVI (AUC ＞ 0.7),and Gallina nomogram is superior to two versions of Partin tables in predicting SVI.
