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Nasopharynx

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Robotic nasopharyngectomy via combined endonasal and transantral port: a preliminary cadaveric study.

The Laryngoscope
OBJECTIVES/HYPOTHESIS: The objective of this study was to determine the potential role of a surgical robotic system in nasopharyngeal surgery using bilateral transantral or combined endonasal/transantral port.

Demonstration of nasopharyngeal surgery with a single port operator-controlled flexible endoscope system.

Head & neck
BACKGROUND: Nasopharyngeal surgery is commonly performed with a rigid endoscope using a transnasal or transoral approach. Here, we demonstrate a flexible single port computer-assisted endoscopic system enabling easy transoral access to the nasopharyn...

Development of a compact continuum tubular robotic system for nasopharyngeal biopsy.

Medical & biological engineering & computing
Traditional posterior nasopharyngeal biopsy using a flexible nasal endoscope has the risks of abrasion and injury to the nasal mucosa and thus causing trauma to the patient. Recently, a new class of robots known as continuum tubular robots (CTRs) pro...

Deep Learning for Automated Contouring of Primary Tumor Volumes by MRI for Nasopharyngeal Carcinoma.

Radiology
Background Nasopharyngeal carcinoma (NPC) may be cured with radiation therapy. Tumor proximity to critical structures demands accuracy in tumor delineation to avoid toxicities from radiation therapy; however, tumor target contouring for head and neck...

Machine Learning Analysis of Image Data Based on Detailed MR Image Reports for Nasopharyngeal Carcinoma Prognosis.

BioMed research international
We aimed to assess the use of automatic machine learning (AutoML) algorithm based on magnetic resonance (MR) image data to assign prediction scores to patients with nasopharyngeal carcinoma (NPC). We also aimed to develop a 4-group classification sys...

Convolutional neural network in nasopharyngeal carcinoma: how good is automatic delineation for primary tumor on a non-contrast-enhanced fat-suppressed T2-weighted MRI?

Japanese journal of radiology
PURPOSE: Convolutional neural networks (CNNs) show potential for delineating cancers on contrast-enhanced MRI (ce-MRI) but there are clinical scenarios in which administration of contrast is not desirable. We investigated performance of the CNN for d...