MP53-10 MULTI-PARAMETRIC MRI MAPS FOR DETECTION AND GRADING OF DOMINANT PROSTATE TUMORS

You have accessJournal of UrologyProstate Cancer: Detection & Screening II1 Apr 2014MP53-10 MULTI-PARAMETRIC MRI MAPS FOR DETECTION AND GRADING OF DOMINANT PROSTATE TUMORS Xiaobo Ding, Dan Tong, and Huimao Zhang Xiaobo DingXiaobo Ding More articles by this author , Dan TongDan Tong More articles by this author , and Huimao ZhangHuimao Zhang More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2014.02.1641AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail Introduction and Objectives To develop an image-based technique capable of detection and grading of prostate cancer, which combines features extracted from multiparametric MRI into a single parameter map of cancer probability. Methods A combination of features extracted from diffusion tensor MRI and dynamic contrast enhanced MRI was used to characterize biopsy samples from 20 patients. Support vector machines were used to separate the cancerous samples from normal biopsy samples and to compute a measure of cancer probability, presented in the form of a cancer colormap. The classification results were compared with the biopsy results and the classifier was tuned to provide the largest area under the receiver operating characteristic (ROC) curve. Based solely on the tuning of the classifier on the biopsy data, cancer colormaps were also created for whole-mount histopathology slices from four radical prostatectomy patients. Results An area under ROC curve of 0.95 was obtained on the biopsy dataset and was validated by a "leave-one-patient-out" procedure. The proposed measure of cancer probability shows a positive correlation with Gleason score. The cancer colormaps created for the histopathology patients do display the dominant tumors. The colormap accuracy increases with measured tumor area and Gleason score. Conclusions Dynamic contrast enhanced imaging and diffusion tensor imaging, when used within the framework of supervised classification, can play a role in characterizing prostate cancer. © 2014FiguresReferencesRelatedDetails Volume 191Issue 4SApril 2014Page: e592 Advertisement Copyright & Permissions© 2014MetricsAuthor Information Xiaobo Ding More articles by this author Dan Tong More articles by this author Huimao Zhang More articles by this author Expand All Advertisement Advertisement PDF DownloadLoading ...