| Autor: |
Yoo S; Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada., Gujrathi I; Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada., Haider MA; Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada.; Institute of Medical Science, University of Toronto, Toronto, ON, Canada.; Department of Medical Imaging, University of Toronto, Toronto, ON, Canada.; Sunnybrook Research Institute, Toronto, ON, Canada., Khalvati F; Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada. farzad.khalvati@utoronto.ca.; Institute of Medical Science, University of Toronto, Toronto, ON, Canada. farzad.khalvati@utoronto.ca.; Department of Medical Imaging, University of Toronto, Toronto, ON, Canada. farzad.khalvati@utoronto.ca.; Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada. farzad.khalvati@utoronto.ca. |
| Abstrakt: |
Prostate cancer is one of the most common forms of cancer and the third leading cause of cancer death in North America. As an integrated part of computer-aided detection (CAD) tools, diffusion-weighted magnetic resonance imaging (DWI) has been intensively studied for accurate detection of prostate cancer. With deep convolutional neural networks (CNNs) significant success in computer vision tasks such as object detection and segmentation, different CNN architectures are increasingly investigated in medical imaging research community as promising solutions for designing more accurate CAD tools for cancer detection. In this work, we developed and implemented an automated CNN-based pipeline for detection of clinically significant prostate cancer (PCa) for a given axial DWI image and for each patient. DWI images of 427 patients were used as the dataset, which contained 175 patients with PCa and 252 patients without PCa. To measure the performance of the proposed pipeline, a test set of 108 (out of 427) patients were set aside and not used in the training phase. The proposed pipeline achieved area under the receiver operating characteristic curve (AUC) of 0.87 (95[Formula: see text] Confidence Interval (CI): 0.84-0.90) and 0.84 (95[Formula: see text] CI: 0.76-0.91) at slice level and patient level, respectively. |
