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Kim, Lee, Jung, Hwang, Sung, Cho, and Kim: Comparison of Accuracies for Image-based 1.5T and 3T MRI Using a Clinical Decision Support System Driven by a Support Vector Machine to Detect Seminal Vesicle Invasion of Prostate Cance
Uroradiology
Original Article

Journal of the Korean Society of Radiology 2010; 63(1): 49-56.

Published online: 31 July 2010

DOI: https://doi.org/10.3348/jksr.2010.63.1.49

Comparison of Accuracies for Image-based 1.5T and 3T MRI Using a Clinical Decision Support System Driven by a Support Vector Machine to Detect Seminal Vesicle Invasion of Prostate Cance

Sang Youn Kim, M.D.1, 2, Hak Jong Lee, M.D.1, 3, Dae Chul Jung, M.D.4, Sung Il Hwang, M.D.1, 3, Chang Kyu Sung, M.D.1, 5, Jeong Yeon Cho, M.D.1, 2, Seung Hyup Kim, M.D.1, 2

1Department of Radiology, Seoul National University College of Medicine, Korea.

2Department of Radiology, Seoul National University Hospital, Korea.

3Department of Radiology, Seoul National University Bundang Hospital, Korea.

4Department of Radiology, Research Institute and Hospital, National Cancer Center, Korea.

5Department of Radiology, Seoul National University Boramae Hospital, Korea.

Address reprint requests to: Hak Jong Lee, M.D., Ph.D., Department of Radiology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, 300, Gumi-dong, Bundang-gu, Seongnam-si, Gyeonggi-do 463-707, Korea. Tel. 82-31-787-7605, Fax. 82-31-787-4011, hakjlee@snu.ac.kr

Received 18 January 2010       Accepted 21 March 2010

Copyright © 2010 The Korean Society of Radiology

Abstract

Purpose

The purpose of this study is to develop image-based clinical decision support systems (CDSSs) using support vector machine models (SVMs) for the detection of seminal vesicle invasion (SVI) of prostate cancer and to compare the accuracies of 1.5T and 3.0T MR CDSSs.

Materials and Methods

A total of 548 prostate cancer patients who underwent a prostatectomy and preoperative MR using 1.5T or 3.0T were enrolled in this study. Each 1.5T and 3.0T group was subdivided into the training group and test group, arbitrarily. Images were analyzed in consensus by two radiologists. CDSS was constructed with input data that has the appearance of a seminal vesicle, PSA level and age in each training group, and with the output data of the probability for SVI using SVMs. The accuracy of the output data were evaluated with data of each test group. After a histopathologic correlation, the sensitivity, specificity and accuracy for the detection of SVI were compared in both 1.5T and 3.0T.

Results

For the diagnosis of SVI, the specificity and the accuracy of the 3.0T model were all statistically superior to those of the 1.5T model (90.4% vs. 73.1%; 88.7% vs. 74.6%) (p<0.05).

Conclusion

The image-based CDSS for the detection of SVI was successfully constructed using SVM. According to our CDSSs, the specificity and accuracy of 3.0T were superior to those of 1.5T.

Keywords: Decision Support Systems, Clinical, Medical Order Entry Systems, Magnetic Resonance Imaging, Prostatic Neoplasms, Seminal Vesicles

Figures and Tables

Fig. 1

Receiver operating characteristics (ROC) curve of clinical decision support systems using support vector machine (SVM) model at 1.5 T and 3.0T.

A. ROC curve of SVM model at 1.5 T machine shows the cut-off value 28.3 with sensitivity of 80.0% and specificity of 73.1%.
B. ROC curve of SVM model at 3 T machine shows the cut-off value of 31.8 with sensitivity of 72.7% and specificity of 90.4%.
jksr-63-49-g001
Fig. 2

A 76-year-old man with PSA of 4.946 and high probability of seminal vesicle invasion suggested by 3.0T SVM model.

3.0T prostate MR axial (A) and coronal (B) images show wall destruction of seminal vesicle (arrows). The SVM model suggested that the probability of seminal vesicle invasion in this patient is 61.6. Regarding that the discriminating value in 3.0T SVM model is 31.8, the probability of seminal vesicle invasion in this patient is very high. The pathologic report revealed that seminal vesicle invasion is present.
jksr-63-49-g002
Fig. 3

A 77-year-old man with PSA of 15.3 and low probability of seminal vesicle invasion suggested by 1.5T SVM model.

1.5T prostate MR axial (A) and coronal (B) images show no definite abnormality in seminal vesicle. The SVM model suggested that the probability of seminal vesicle invasion in this patient is 27.6. Regarding that the discriminating value in 1.5T SVM model is 28.3, the probability of seminal vesicle invasion in this patient is low. However, the pathologic report revealed that seminal vesicle invasion is present.
jksr-63-49-g003

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