Abstract #0814

Differential Diagnosis of Prostate Cancer and Benign Prostatic Hyperplasia Based on Prostate DCE-MRI by Using Deep Learning with Different Peritumoral Areas

Yang Zhang^1,2, Weikang Li³, Zhao Zhang³, Yingnan Xue³, Yan-Lin Liu², Peter Chang², Daniel Chow², Ke Nie¹, Min-Ying Su², and Qiong Ye^3,4

¹Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ, United States, ²Department of Radiological Sciences, University of California, Irvine, CA, United States, ³Department of Radiology, The First Affiliate Hospital of Wenzhou Medical University, Wenzhou, China, ⁴High Magnetic Field Laboratory, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China

A bi-directional Convolutional Long Short Term Memory (CLSTM) Network was previously shown capable of differentiating prostate cancer and benign prostate hyperplasia (BPH) based DCE-MRI that acquired 40 time frame images. The purpose of this work was to investigate the diagnostic value of peritumoral tissues. Several different methods were used to expand peritumoral tissues surrounding the lesion, and they were used as the input to the diagnostic network. A total of 135 cases were analyzed, including 73 prostate cancer and 62 BPH. Based on 4-fold cross-validation, the region growing based ROI had the best performance, with a mean AUC of 0.89.

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Differential Diagnosis of ﻿ Prostate Cancer and Benign Prostatic Hyperplasia Based on Prostate DCE-MRI by Using Deep Learning with Different Peritumoral Areas

Differential Diagnosis of Prostate Cancer and Benign Prostatic Hyperplasia Based on Prostate DCE-MRI by Using Deep Learning with Different Peritumoral Areas