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Abstract #0716

A Patch-Based Convolutional Neural Network Model for the Diagnosis of Prostate Cancer using Multi-Parametric Magnetic Resonance Images

Yang Song1, Yu-Dong Zhang2, Xu Yan3, Bingwen Hu1, and Guang Yang1

1Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China, 2Department of Radiology, the First Affiliated Hospital with Nanjing Medical University, Nanjing, China, 3MR Scientific Marketing, Siemens Healthcare, Shanghai, China

We proposed a patch-based convolutional neural network (CNN) model to distinguish prostate cancer using multi-parametric magnetic resonance images (mp-MRI). Our CNN model was trained in 182 patients including 193 cancerous (CA) vs. 259 normal (NC) regions, and tested independently in 21 patients including 21 CA vs 31 NC regions. The model produced an area under the receiver operating characteristic curve of 0.869, sensitivity of 90.5% and specificity of 67.7% for the differentiation of CA from normal regions, showing its potential for the diagnosis of prostate cancer in clinical application.

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