MP42-07 USEFULNESS OF COMPUTER-AIDED DIAGNOSIS SYSTEM IN EVALUATING SEVERITY OF BENIGN PROSTATIC HYPERPLASIA, USING A SUPER-ELLIPSE MODEL TO CHARACTERIZE CHANGES IN PROSTATE CONTOURS

Abstract

You have accessJournal of UrologyImaging/Radiology: Uroradiology II (MP42)1 Apr 2020MP42-07 USEFULNESS OF COMPUTER-AIDED DIAGNOSIS SYSTEM IN EVALUATING SEVERITY OF BENIGN PROSTATIC HYPERPLASIA, USING A SUPER-ELLIPSE MODEL TO CHARACTERIZE CHANGES IN PROSTATE CONTOURS Sunao Shoji*, Yuka Shigenari, Izumi Hanada, Tatsuya Otaki, Takahiro Ogawa, Takahiro Ogawa, Masayoshi Kawakami, Hakushi Kim, Masanori Hasegawa, Yoshiaki Kawamura, Norihiro Koizumi, and Akira Miyajima Sunao Shoji*Sunao Shoji* More articles by this author , Yuka ShigenariYuka Shigenari More articles by this author , Izumi HanadaIzumi Hanada More articles by this author , Tatsuya OtakiTatsuya Otaki More articles by this author , Takahiro OgawaTakahiro Ogawa More articles by this author , Takahiro OgawaTakahiro Ogawa More articles by this author , Masayoshi KawakamiMasayoshi Kawakami More articles by this author , Hakushi KimHakushi Kim More articles by this author , Masanori HasegawaMasanori Hasegawa More articles by this author , Yoshiaki KawamuraYoshiaki Kawamura More articles by this author , Norihiro KoizumiNorihiro Koizumi More articles by this author , and Akira MiyajimaAkira Miyajima More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000000891.07AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: To evaluate the usefulness of a computer-aided diagnosis system (CAD) in evaluating the severity of benign prostatic hyperplasia (BPH) using a super-ellipse model to characterize changes in prostate contours. METHODS: We prospectively recruited 60 patients who were scanned with T2-weighted MRI (T2WI). A super-ellipse model was used to obtain structural features of their prostates. The super-ellipse shape can be characterized by parameter vector p= (lx, ly, r, sy, sq, xy, t, b) (Figure), and expressed by (x/ax)2/ɛ+(y/ay)2/ɛ=1. We used a learning algorithm in a support vector machine to learn the features of the prostate, and the BPH severity data, which included International Prostate Symptom Score (IPSS), IPSS quality of life (QOL), Over-Active Bladder Symptom Score (OABSS), maximum flow rate (ml/s), and residual urine volume (ml). We then analyzed the predictive effect of the learning model, from the structure of the prostate to BPH severity. RESULTS: To train the learning model, we used data from the T2WI and features of 20 patients, included 10 with severe BPH and 10 with normal prostates. The BPH severity of the other 40 patients were predicted from their T2WI, using the learning model; results were accuracy: 87.5%, precision: 84.6%, recall: 92.0% and F1 score: 88.0%. The area under curve for predicting BPH severity with the learned model was 0.98 (P<0.0001). However, classifying BPH severity was somewhat difficult due to similar features of prostates with different sizes. CONCLUSIONS: The present learning model can potentially predict BPH severity using the structural data from patients’ T2WI. However, scale information would be useful to predict with more high accuracy in addition to the learned model. Source of Funding: None. © 2020 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 203Issue Supplement 4April 2020Page: e605-e606 Advertisement Copyright & Permissions© 2020 by American Urological Association Education and Research, Inc.MetricsAuthor Information Sunao Shoji* More articles by this author Yuka Shigenari More articles by this author Izumi Hanada More articles by this author Tatsuya Otaki More articles by this author Takahiro Ogawa More articles by this author Takahiro Ogawa More articles by this author Masayoshi Kawakami More articles by this author Hakushi Kim More articles by this author Masanori Hasegawa More articles by this author Yoshiaki Kawamura More articles by this author Norihiro Koizumi More articles by this author Akira Miyajima More articles by this author Expand All Advertisement PDF downloadLoading ...