Abstract #0817

Texture-Based Deep Learning for Prostate Cancer Classification with Multiparametric MRI

Yongkai Liu^1,2, Haoxin Zheng¹, Zhengrong Liang³, Miao Qi¹, Wayne Brisbane⁴, Leonard Marks⁴, Steven Raman¹, Robert Reiter⁴, Guang Yang⁵, and Kyunghyun Sung¹

¹Department of Radiological Sciences, University of California, Los Angeles, Los Angeles, CA, United States, ²Physics and Biology in Medicine IDP, University of California, Los Angeles, Los Angeles, CA, United States, ³Departments of Radiology and Biomedical Engineering, Stony Brook University, Stony Brook, New York, NY, United States, ⁴Department of Urology, University of California, Los Angeles, Los Angeles, CA, United States, ⁵National Heart and Lung Institute, Imperial College London, London, United Kingdom

Accurate classification of prostate cancer (PCa) enables better prognosis and selection of treatment plans. We presented a textured-based deep learning method to enhance prostate cancer classification performance by enriching deep learning with prostate cancer texture information.

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