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Abstract #4826

Deep Learning to Discriminate Nasopharyngeal Carcinoma and Benign Hyperplasia on MRI

Lun M. Wong1, Ann D. King1, and Qiyong Ai1
1Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, Hong Kong

Benign hyperplasia is a common finding in the adenoid and walls of the nasopharynx and may hamper the detection of early-stage nasopharyngeal carcinoma (NPC) on MRI. In this study we aim to utilize deep learning to discriminate early-stage NPC from benign hyperplasia using T2-weighted-fat-suppressed MR images. We tested our method on a dataset of 413 cases, comprising 203 with early-stage NPC confined to the nasopharynx and 210 with benign hyperplasia. After training with validation (n=350 and n=13 respectively) followed by testing (n=50), the network achieved a promising result with a sensitivity of 100% and specificity of 83% for NPC detection.

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