Differentiating between nasopharyngeal cancer and nasopharyngeal malignant lymphoma (ML) remains challenging on cross-sectional images. The aim of this study is to investigate the usefulness of texture features on unenhanced CT for differentiating be...
International journal of computer assisted radiology and surgery
33782844
PURPOSE: Nasopharyngeal carcinoma (NPC) is a category of tumors with high incidence in head-and-neck (H&N) body region, and the diagnosis and treatment planning are usually conducted by radiologists manually, which is tedious, time-consuming and unre...
OBJECTIVES: We aimed to build a survival system by combining a highly-accurate machine learning (ML) model with explainable artificial intelligence (AI) techniques to predict distant metastasis in locoregionally advanced nasopharyngeal carcinoma (NPC...
In this work, we develop a deep learning-guided fiberoptic Raman diagnostic platform to assess its ability of real-time nasopharyngeal carcinoma (NPC) diagnosis and post-treatment follow-up of NPC patients. The robust Raman diagnostic platform is es...
BACKGROUND: Induction chemotherapy (ICT) plus concurrent chemoradiotherapy (CCRT) and CCRT alone were the optional treatment regimens in locoregionally advanced nasopharyngeal carcinoma (NPC) patients. Currently, the choice of them remains equivocal ...
OBJECTIVES/HYPOTHESIS: To develop a deep-learning-based automatic diagnosis system for identifying nasopharyngeal carcinoma (NPC) from noncancer (inflammation and hyperplasia), using both white light imaging (WLI) and narrow-band imaging (NBI) nasoph...
BACKGROUND: Manual segment target volumes were time-consuming and inter-observer variability couldn't be avoided. With the development of computer science, auto-segmentation had the potential to solve this problem.