Comparable performance between automatic and manual laryngeal and hypopharyngeal GTV delineations validated with pathology.

Purpose: Deep learning is a promising approach to increase reproducibility and time-efficiency of GTV delineation in head and neck cancer, but model evaluation primarily relies on manual GTV delineations as reference annotation, which are subjective and tend to overestimate tumor volume. This study aimed to validate a deep learning model for laryngeal and hypopharyngeal GTV segmentation with pathology and to compare its performance with clinicians' manual delineations.

Materials And Methods: A retrospective dataset of 193 laryngeal and hypopharyngeal cancer patients was used to train a deep learning model with clinical GTV delineations as reference.

Improved delineation with diffusion weighted imaging for laryngeal and hypopharyngeal tumors validated with pathology.

Objective: This study aims to determine the added value of a geometrically accurate diffusion-weighted (DW-) MRI sequence on the accuracy of gross tumor volume (GTV) delineations, using pathological tumor delineations as a ground truth.

Methods: Sixteen patients with laryngeal or hypopharyngeal carcinoma were included. After total laryngectomy, the specimen was cut into slices.

Internal Jugular Vein Geometry Under Multiple Inclination Angles with 3D Low-Field MRI in Healthy Volunteers.

Background: Cerebral venous pathways are subjected to geometrical and patency changes due to body position. The internal jugular veins (IJVs) are the main venous drainage pathway in supine position. Their patency and geometry should be evaluated under different body inclination angles over a three-dimensional (3D) volume in the healthy situation to better understand pathological cases.