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Abstract #2391

Applying Radiomics and Convolutional Neural Network Analysis to distinguish Lymph Node Invasion in Prostate Cancer using Multi-parametric MRI

Yang Song1, Ying Hou2, Min-xiong Zhou3, Xu Yan4, Ye-feng Yao1, Yu-dong Zhang2, and Guang Yang1
1Shanghai Key Laboratory of Magnetic Resonance, East China Normal Univeristy, Shanghai, China, 2Department of Radiology, the First Affiliated Hospital with Nanjing Medical University, Nanjing, China, 3Shanghai University of Medicine & Health Sciences, Shanghai, China, 4MR Scientific Marketing, Siemens Healthcare, Shanghai, China

We compared a radiomics model and a convolutional neural network (CNN) model to distinguish lymph node invasion (LNI) in prostate cancer (PCa) using multi-parametric magnetic resonance images (mp-MRI). We trained the models in 281 patients and evaluated them in another 71 cases. The radiomics/CNN model produced an AUC 0.741/0.722, sensitivity 0.769/0.692, specificity 0.690/0.845 for the differentiation of LNI, which showed potential for the diagnosis of LNI in PCa.

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