Korean J Radiol.  2011 Oct;12(5):588-594. 10.3348/kjr.2011.12.5.588.

Pre-Operative Prediction of Advanced Prostatic Cancer Using Clinical Decision Support Systems: Accuracy Comparison between Support Vector Machine and Artificial Neural Network

• Kim SY
• Moon SK
• Jung DC
• Hwang SI
• Sung CK
• Cho JY
• Kim SH
• Lee J
• Lee HJ

• KMID: 1116444
• DOI: http://doi.org/10.3348/kjr.2011.12.5.588

Abstract

OBJECTIVE
The purpose of the current study was to develop support vector machine (SVM) and artificial neural network (ANN) models for the pre-operative prediction of advanced prostate cancer by using the parameters acquired from transrectal ultrasound (TRUS)-guided prostate biopsies, and to compare the accuracies between the two models.
MATERIALS AND METHODS
Five hundred thirty-two consecutive patients who underwent prostate biopsies and prostatectomies for prostate cancer were divided into the training and test groups (n = 300 versus n = 232). From the data in the training group, two clinical decision support systems (CDSSs-[SVM and ANN]) were constructed with input (age, prostate specific antigen level, digital rectal examination, and five biopsy parameters) and output data (the probability for advanced prostate cancer [> pT3a]). From the data of the test group, the accuracy of output data was evaluated. The areas under the receiver operating characteristic (ROC) curve (AUC) were calculated to summarize the overall performances, and a comparison of the ROC curves was performed (p < 0.05).
RESULTS
The AUC of SVM and ANN is 0.805 and 0.719, respectively (p = 0.020), in the pre-operative prediction of advanced prostate cancer.
CONCLUSION
The performance of SVM is superior to ANN in the pre-operative prediction of advanced prostate cancer.

Keyword

Decision support systems, clinical; Medical order entry systems; Prostatic neoplasms; Staging; Needle biopsy

MeSH Terms

Adult
Aged
Aged, 80 and over
Area Under Curve
Biopsy, Needle
*Decision Support Systems, Clinical
Humans
Male
Middle Aged
*Neural Networks (Computer)
Prostate-Specific Antigen/blood
Prostatectomy
Prostatic Neoplasms/*diagnosis/pathology/surgery
ROC Curve
Sensitivity and Specificity
*Support Vector Machines

Figure

• 

  Fig. 1 Receiver operating characteristic (ROC) curve analysis of clinical decision support systems using support vector machine (SVM) and artificial neural network (ANN) models. Area under ROC curve (AUC) value of SVM was superior to ANN.

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