Tree-Augmented Naive Bayesian network model for predicting prostate cancer
XIAO Li-hong
CHEN Pei-ran
LI Mei
GOU Zhong-ping
XIANG Liang-cheng
LI Yong-zhong
FENG Ping
Abstract：Objective:To evaluate the integrated performance of age,serum PSA,and transrectal ultrasound images in the prediction of prostate cancer using a Tree-Augmented Na(i)ve (TAN) Bayesian network model.Methods:We collected such data as age,serum PSA,transrectal ultrasound findings,and pathological diagnoses from 941 male patients who underwent prostate biopsy from January 2008 to September 2011.Using a TAN Bayesian network model,we analyzed the data for predicting prostate cancer,and compared them with the gold standards of pathological diagnosis.Results:The accuracy,sensitivity,specificity,positive prediction rate,and negative prediction rate of the TAN Bayesian network model were 85.11％,88.37％,83.67％,70.37％,and 94.25％,respectively.Conclusion:Based on age,serum PSA,and transrectal ultrasound images,the TAN Bayesian network model has a high value for the prediction of prostate cancer,and can help improve the clinical screening and diagnosis of the disease.
Keywords：tree-augmented Na(i)ve Bayesian networkprostate cancerprostate-specific antigentransrectal ultrasound imageage
Publication Date：2016-06-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)