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.
Purpose: We developed a multiple logistic regression model, an artificial neural network (ANN), and a support vector machine (SVM) model to predict the outcome of a prostate biopsy, and compared the accuracies of each model.Method: One thousand and seventy-seven consecutive patients who had undergon...
| Published in: | European Radiology Vol. 20; no. 6; pp. 1476 - 1485 |
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| Main Authors: | , , , , , , , , , , , , , , , |
| Format: | research Journal Article |
| Published: |
Springer Nature
Jun2010
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=105187989&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105187989 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Jun2010 vid: 20 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 105187989 50132273 NLM20016902 2010640770 10.1007/s00330-009-1686-x NLM20016902 105187989 ppf: 1476 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: 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. aug: au: Lee HJ Hwang SI Han SM Park SH Kim SH Cho JY Seong CG Choe G Lee, Hak Jong Hwang, Sung Il Han, Seok-Min Park, Seong Ho Kim, Seung Hyup Cho, Jeong Yeon Seong, Chang Gyu Choe, Gheeyoung affil: Department of Radiology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, Korea sug: subj: Artificial Intelligence Decision Support Systems, Clinical Image Interpretation, Computer Assisted Methods Logistic Regression Information Science Methods Prostatic Neoplasms Ultrasonography Ultrasonography Methods Adult Aged Aged, 80 and Over Decision Support Techniques Human Image Enhancement Methods Male Middle Age Rectum Ultrasonography Regression Reproducibility of Results Sensitivity and Specificity Adult: 19-44 years Aged: 65+ years Aged, 80 & over Middle Aged: 45-64 years Male ab: Purpose: We developed a multiple logistic regression model, an artificial neural network (ANN), and a support vector machine (SVM) model to predict the outcome of a prostate biopsy, and compared the accuracies of each model.Method: One thousand and seventy-seven consecutive patients who had undergone transrectal ultrasound (TRUS)-guided prostate biopsy were enrolled in the study. Clinical decision models were constructed from the input data of age, digital rectal examination findings, prostate-specific antigen (PSA), PSA density (PSAD), PSAD in transitional zone, and TRUS findings. The patients were divided into the training and test groups in a randomized fashion. Areas under the receiver operating characteristic (ROC) curve (AUC, Az) were calculated to summarize the overall performance of each decision model for the task of prostate cancer prediction.Results: The Az values of the ROC curves for the use of multiple logistic regression analysis, ANN, and the SVM were 0.768, 0.778, and 0.847, respectively. Pairwise comparison of the ROC curves determined that the performance of the SVM was superior to that of the ANN or the multiple logistic regression model.Conclusion: Image-based clinical decision support models allow patients to be informed of the actual probability of having a prostate cancer. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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