ORIGINAL ARTICLE
Ahead of Print

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


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

Correspondence Address:
Ping Feng,
Institute of Clinical Trials, West China Hospital, Sichuan University, Chengdu
China
Login to access the Email id

Source of Support: None, Conflict of Interest: None

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.


[FULL TEXT] [PDF]
Print this article
Search
 Back
 
  Search Pubmed for
 
    -  Xiao LH
    -  Chen PR
    -  Gou ZP
    -  Li YZ
    -  Li M
    -  Xiang LC
    -  Feng P
 Citation Manager
 Article Access Statistics
 Reader Comments
 * Requires registration (Free)
 

 Article Access Statistics
    Viewed887    
    PDF Downloaded92    
    Cited by others 1    

Recommend this journal