Home  |   Archive  |   Online Submission  |   News & Events  |   Subscribe  |   APFA  |   Society  |   Contact Us  |   中文版
Search   
 
Journal

Ahead of print
Authors' Accepted
    Manuscripts
new!
Current Issue
Archive
Acknowledgments
Special Issues
Browse by Category

Manuscript Submission

Online Submission
Online Review
Instruction for Authors
Instruction for Reviewers
English Corner new!

About AJA

About AJA
Editorial Board
Contact Us
News

Resources & Services

Advertisement
Subscription
Email alert
Proceedings
Reprints

Download area

Copyright licence
EndNote style file
Manuscript word template
Guidance for AJA figures
    preparation (in English)

Guidance for AJA figures
    preparation (in Chinese)

Proof-reading for the
    authors

AJA Club (in English)
AJA Club (in Chinese)

 
Abstract

Volume 22, Issue 2 (March 2020) 22, 213–216; 10.4103/aja.aja_46_19

Establishment of two new predictive models for prostate cancer to determine whether to require prostate biopsy when the PSA level is in the diagnostic gray zone (4–10 ng ml−1)

Jun Liu, Zhi-Qian Wang, Min Li, Ming-Yang Zhou, Yi-Fei Yu, Wei-Wei Zhan

Department of Ultrasonic Diagnosis, Rui Jin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, China

Correspondence: Dr. WW Zhan (shanghairuijinus@163.com)

04-Jun-2019

Abstract

Our goal was to establish two new predictive models of prostate cancer to determine whether to require a prostate biopsy when the prostate-specific antigen level is in the diagnostic gray zone. A retrospective analysis of 197 patients undergoing prostate biopsy with prostate-specific antigens between 4 and 10 ng ml−1 was conducted. Of these, 47 patients were confirmed to have cancer, while the remaining 150 patients were diagnosed with benign prostate disease after examining biopsy pathology. Two multivariate logistic regression models were established including age, prostate volumes, free/total prostate-specific antigen ratio, and prostate-specific antigen density using SPSS 19.0 to obtain the predicted probability and Logit P, and then, two receiver operating characteristic (ROC) curves were drawn to obtain the best cutoff value for prostate biopsy: one for the group of all the prostate cancers and one for the group of clinically significant prostate cancers. The best cutoff value for prostate biopsy was 0.25 from the multivariate logistic regression ROC curve model of all the prostate cancers, which gave a sensitivity of 75.4% and a specificity of 75.8%. The best cutoff value for prostate biopsy was 0.20 from the multivariate logistic regression model of clinically significant prostate cancers, which gave a sensitivity of 76.7% and a specificity of 80.1%. We identified the best cutoff values for prostate biopsy (0.25 for all prostate cancers and 0.20 for clinically significant prostate cancers) to determine whether to require prostate biopsy when the PSA level is in the diagnostic gray zone.

Keywords: predictive model; prostate biopsy; prostate cancer; prostate-specific antigen

Full Text | PDF |

 
Browse:  1269
 
Copyright 1999-2017  Shanghai Materia Medica, Shanghai Jiao Tong University.  All rights reserved