Objectives: This study's objectives are (1) to investigate the registration accuracy from intraoperative ultrasound (US) to histopathological images, (2) to assess the agreement and correlation between measurements in registered 3D US and histopathology, and (3) to train a nnUNet model for automatic segmentation of 3D US volumes of resected tongue specimens.
Methods: Ten 3D US volumes were acquired, including the corresponding digitalized histopathological images (n = 29). Based on corresponding landmarks, the registrations between 3D US and histopathology images were calculated and evaluated using the target registration error (TRE).
Background: Histopathological analysis often shows close resection margins after surgical removal of tongue squamous cell carcinoma (TSCC). This study aimed to investigate the agreement between intraoperative 3D ultrasound (US) margin assessment and postoperative histopathology of resected TSCC.
Methods: In this study, ten patients were prospectively included.
Three-dimensional (3D) ultrasound can assess the margins of resected tongue carcinoma during surgery. Manual segmentation (MS) is time-consuming, labour-intensive, and subject to operator variability. This study aims to investigate use of a 3D deep learning model for fast intraoperative segmentation of tongue carcinoma in 3D ultrasound volumes.
View Article and Find Full Text PDFEur Arch Otorhinolaryngol
April 2024
Purpose: Treatment of head and neck cancer (HNC) may lead to obstructive sleep apnea (OSA), but conclusive results on the prevalence of OSA are lacking. The objective of this study is to investigate the prevalence of OSA in a cohort of patients treated for advanced T-stage HNC.
Methods: A cross-sectional study was conducted in two tertiary cancer care centers including patients at least 1 year after treatment with curative intent with surgery and/or (chemo)radiotherapy ((C)RT) for advanced T-staged (T3-4) cancer of the oral cavity, oropharynx, hypopharynx, or larynx.