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 19, Issue 5 (September 2017) 19, 586–590; DOI:10.4103/1008-682X.186884

Prostate cancer prediction using the random forest algorithm that takes into account transrectal ultrasound findings, age, and serum levels of prostate-specific antigen

Li-Hong Xiao1, Pei-Ran Chen2, Zhong-Ping Gou3, Yong-Zhong Li4, Mei Li3, Liang-Cheng Xiang2, Ping Feng3

1 Institute of Clinical Trials, West China Hospital, Sichuan University, Chengdu; Department of Epidemiology and Biostatistics, West China School of Public Health, Sichuan University, Chengdu, China
2 Department of Epidemiology and Biostatistics, West China School of Public Health, Sichuan University, Chengdu, China
3 Institute of Clinical Trials, West China Hospital, Sichuan University, Chengdu, China
4 Department of Ultrasound, West China Hospital, Sichuan University, Chengdu, China

Correspondence: Dr. P Feng (pfyq@yahoo.com)

Date of Submission 30-Mar-2016 Date of Decision 13-May-2016 Date of Acceptance 01-Jul-2016 Date of Web Publication 02-Sep-2016

Abstract

The aim of this study is to evaluate the ability of the random forest algorithm that combines data on transrectal ultrasound findings, age, and serum levels of prostate-specific antigen to predict prostate carcinoma. Clinico-demographic data were analyzed for 941 patients with prostate diseases treated at our hospital, including age, serum prostate-specific antigen levels, transrectal ultrasound findings, and pathology diagnosis based on ultrasound-guided needle biopsy of the prostate. These data were compared between patients with and without prostate cancer using the Chi-square test, and then entered into the random forest model to predict diagnosis. Patients with and without prostate cancer differed significantly in age and serum prostate-specific antigen levels (P < 0.001), as well as in all transrectal ultrasound characteristics (P < 0.05) except uneven echo (P = 0.609). The random forest model based on age, prostate-specific antigen and ultrasound predicted prostate cancer with an accuracy of 83.10%, sensitivity of 65.64%, and specificity of 93.83%. Positive predictive value was 86.72%, and negative predictive value was 81.64%. By integrating age, prostate-specific antigen levels and transrectal ultrasound findings, the random forest algorithm shows better diagnostic performance for prostate cancer than either diagnostic indicator on its own. This algorithm may help improve diagnosis of the disease by identifying patients at high risk for biopsy.

Keywords: diagnosis; prostate cancer; prostate-specific antigen; random forest algorithm; transrectal ultrasound characteristics

Full Text | PDF |

 
Browse:  1314
 
Asian Journal of Andrology CN 31-1795/R ISSN 1008-682X  Copyright © 2023  Shanghai Materia Medica, Chinese Academy of Sciences.  All rights reserved.