Abstract #0584

Quantitative MRI-Driven Deep Learning for Detection of Clinical Significant Prostate Cancer

Shiwen Shen^1,2, Xinran Zhong^1,3, Willam Hsu¹, Alex Bui¹, Holden Wu¹, Michael Kuo¹, Steven Raman¹, Daniel Margolis¹, and Kyunghyun Sung¹

¹Department of Radiological Sciences, University of California, Los Angeles, Los Angeles, CA, United States, ²Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, United States, ³Physics and Biology in Medicine IDP, University of California, Los Angeles, Los Angeles, CA, United States

We present a novel automatic classification method to distinguish between indolent and clinically significant prostatic carcinoma using multi-parametric MRI (mp-MRI). The main contributions are 1) utilizing state-of-art deep learning method to characterize the lesion in mp-MRI through a pre-trained convolutional neural network model, OverFeat, 2) building a hybrid two-order classification model that combines deep learning and conventional statistical features, and 3) avoiding annotation of the lesion boundaries and anatomical-location-specific training. The proposed method was evaluated using 102 lesions of prostate cancer and achieved significantly higher accuracy than the method with traditional statistical features.

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