Nasopharyngeal carcinoma (NPC) is one of the most common head and neck cancers. Early
diagnosis plays a critical role in the treatment of NPC. To aid diagnosis, deep learning …

Background Convolutional neural networks (CNNs) have the potential to automatically
delineate primary nasopharyngeal carcinoma (NPC) on magnetic resonance imaging (MRI) …

Objectives A convolutional neural network (CNN) was adapted to automatically detect early-
stage nasopharyngeal carcinoma (NPC) and discriminate it from benign hyperplasia on a …

Background Magnetic resonance images (MRI) is the main diagnostic tool for risk
stratification and treatment decision in nasopharyngeal carcinoma (NPC). However, the …

Background and objectives Administration of contrast is not desirable for all cases in clinical
setting, and no consensus in sequence selection for deep learning model development has …

Objectives/Hypothesis To develop a deep‐learning‐based automatic diagnosis system for
identifying nasopharyngeal carcinoma (NPC) from noncancer (inflammation and …

Background Recent studies have shown that deep learning can help tumor staging
automatically. However, automatic nasopharyngeal carcinoma (NPC) staging is difficult due …

Background The purpose of this study was to explore whether incorporating the peritumoral
region to train deep neural networks could improve the performance of the models for …

Objective As the prognosis of nasopharyngeal carcinoma (NPC) is influenced by various
factors, making it difficult for clinical physicians to predict the outcome, the objective of this …

Purpose We aimed to predict the prognosis of advanced nasopharyngeal carcinoma (stage
Ⅲ-Ⅳa) using Pre-and Post-treatment MR images based on deep learning (DL). Methods A …