Tree-Augmented NaÏve Bayesian network model for predicting prostate cancer.
- Author:
Li-Hong XIAO
1
,
2
;
Pei-Ran CHEN
1
;
Mei LI
3
;
Zhong-Ping GOU
3
;
Liang-Cheng XIANG
1
;
Yong-Zhong LI
4
;
Ping FENG
3
;
Author Information
- Publication Type:Journal Article
- Keywords: age; prostate cancer; prostate-specific antigen; transrectal ultrasound image; tree-augmented NaÏve Bayesian network
- MeSH: Bayes Theorem; Biopsy; Humans; Male; Predictive Value of Tests; Prostate; Prostate-Specific Antigen; blood; Prostatic Neoplasms; diagnosis; Sensitivity and Specificity
- From: National Journal of Andrology 2016;22(6):506-510
- CountryChina
- Language:Chinese
-
Abstract:
ObjectiveTo evaluate the integrated performance of age, serum PSA, and transrectal ultrasound images in the prediction of prostate cancer using a Tree-Augmented NaÏve (TAN) Bayesian network model.
METHODSWe 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.
RESULTSThe 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.
CONCLUSIONSBased 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.
