Multiparametric 3T MR Imaging of Prostate Cancer: Histopathologic Correlation Using Customized MRI-Based Specimen Molds
نویسندگان
چکیده
B. Turkbey, H. Mani, V. Shah, M. Bernardo, A. Rastinehad, T. Pohida, Y. Pang, D. Daar, C. Benjamin, Y. McKinney, J. Shih, M. J. Merino, P. A. Pinto, and P. L. Choyke Molecular Imaging Program, NCI, NIH, Bethesda, MD, United States, Laboratory of Pathology, NCI, NIH, United States, Molecular Imaging Program, NCI, NIH, United States, Imaging Physics, SAIC Frederick, Inc., NCI-Frederick, Urologic Oncology Branch, NCI, NIH, United States, Division of Computational Bioscience, Center for Information Technology, NIH, United States, Philips Healthcare, United States, Biometric Research Branch, NCI, NIH, United States
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Final Gleason Score Prediction Using Discriminant Analysis and Support Vector Machine Based on Preoperative Multiparametric MR Imaging of Prostate Cancer at 3T
OBJECTIVE This study aimed at evaluating linear discriminant analysis (LDA) and support vector machine (SVM) classifiers for estimating final Gleason score preoperatively using multiparametric magnetic resonance imaging (mp-MRI) and clinical parameters. MATERIALS AND METHODS Thirty-three patients who underwent mp-MRI on a 3T clinical MR scanner and radical prostatectomy were enrolled in this ...
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