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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.

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참고문헌 (32건) : 자료제공( 네이버학술정보 )

참고문헌 목록에 대한 테이블로 번호, 참고문헌, 국회도서관 소장유무로 구성되어 있습니다.
번호 참고문헌 국회도서관 소장유무
1 Staging of prostate cancer using automatic feature selection, sampling and Dempster-Shafer fusion. 네이버 미소장
2 Image-based clinical decision support for transrectal ultrasound in the diagnosis of prostate cancer: comparison of multiple logistic regression, artificial neural network, and support vector machine. 네이버 미소장
3 Development of a nomogram to predict probability of positive initial prostate biopsy among Japanese patients. 네이버 미소장
4 A pilot study of etoposide, vinblastine, and doxorubicin plus involved field irradiation in advanced, previously untreated Hodgkin's disease. 네이버 미소장
5 Multicenter evaluation of an artificial neural network to increase the prostate cancer detection rate and reduce unnecessary biopsies. 네이버 미소장
6 DEVELOPMENT AND VALIDATION OF A NOMOGRAM PREDICTING THE OUTCOME OF PROSTATE BIOPSY BASED ON PATIENT AGE, DIGITAL RECTAL EXAMINATION AND SERUM PROSTATE SPECIFIC ANTIGEN 네이버 미소장
7 Development and external validation of an extended 10-core biopsy nomogram. 네이버 미소장
8 Algorithms based on prostate-specific antigen (PSA), free PSA, digital rectal examination and prostate volume reduce false-positive PSA results in prostate cancer screening. 네이버 미소장
9 Assessing individual risk for prostate cancer. 네이버 미소장
10 Nomograms and medicine. 네이버 미소장
11 Artificial neural network analysis (ANNA) of prostatic transrectal ultrasound 네이버 미소장
12 Cortes C, Vapnik V. Support vector networks. Mach Learn 1995;20:273-297 미소장
13 Prediction of pathological stages before prostatectomy in prostate cancer patients: Analysis of 12 systematic prostate needle biopsy specimens 네이버 미소장
14 Jiang L, Manry MT. Nonlinear networks for classification. ftp. simtel.net/pub/simtelnet/msdos/calculte/Nuclass706a.zip. Accessed on Aug 12, 2011 미소장
15 A decision support system based on support vector machines for diagnosis of the heart valve diseases. 네이버 미소장
16 Chang C-C, Lin C-J. LIBSVM-A library for support vector machines. http://www.csie.ntu.edu.tw/~cjlin/libsvm. Accessed on May 22, 2010 미소장
17 Cancer volume and site of origin of adenocarcinoma in the prostate: Relationship to local and distant spread 네이버 미소장
18 Using the percentage of biopsy cores positive for cancer, pretreatment PSA, and highest biopsy Gleason sum to predict pathologic stage after radical prostatectomy: the center for prostate disease research nomograms 네이버 미소장
19 THE PERCENT OF CORES POSITIVE FOR CANCER IN PROSTATE NEEDLE BIOPSY SPECIMENS IS STRONGLY PREDICTIVE OF TUMOR STAGE AND VOLUME AT RADICAL PROSTATECTOMY 네이버 미소장
20 Ability of Sextant Biopsies to Predict Radical Prostatectomy Stage 네이버 미소장
21 Predicting the extent of prostate cancer using the combination of systematic biopsy and serum prostate‐specific antigen in Japanese men 네이버 미소장
22 Use of artificial neural networks in prostate cancer. 네이버 미소장
23 An Artificial Neural Network for Prostate Cancer Staging when Serum Prostate Specific Antigen is 10 NG./ML. or Less 네이버 미소장
24 Performance of a neural network in detecting prostate cancer in the prostate-specific antigen reflex range of 2.5 to 4.0 ng/mL 네이버 미소장
25 Vapnik V. Statistical learning theory, Wiley series on adaptive and learning systems for signal processing, communications and control. New York: John Wiley & Sons, 1998 미소장
26 Support vector machines in sonography 네이버 미소장
27 Moradi M, Abolmaesumi P, Siemens DR, Sauerbrei EE, Boag AH, Mousavi P. Augmenting detection of prostate cancer in transrectal ultrasound images using SVM and RF time series. IEEE Trans Biomed Eng 2009;56:2214-2224 미소장
28 Feature selection and performance evaluation of support vector machine (SVM)-based classifier for differentiating benign and malignant pulmonary nodules by computed tomography. 네이버 미소장
29 Support vector machines versus logistic regression: improving prospective performance in clinical decision‐making 네이버 미소장
30 Comparison of Support Vector Machine and Artificial Neural Network Systems for Drug/Nondrug Classification 네이버 미소장
31 Support Vector Machines for Diagnosis of Breast Tumors on US Images 네이버 미소장
32 Is the percentage of cancer in biopsy cores predictive of extracapsular disease in T1‐T2 prostate carcinoma? 네이버 미소장

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