Endocrine dysfunction in long-term survivors of pediatric head and neck rhabdomyosarcoma.

Objective: Survivors of pediatric head and neck rhabdomyosarcoma (HNRMS) are at risk of developing endocrinopathies following local treatment, resulting from radiation damage to the pituitary gland, hypothalamus, or thyroid gland, often at a young age. Our aim was to determine the prevalence of endocrine dysfunction in long-term HNRMS survivors and compare the prevalence of anterior pituitary insufficiency (API) among different local treatment strategies: external beam radiation with photons, external beam radiation with protons, microscopically radical surgery combined with external irradiation, and macroscopic radical surgery combined with brachytherapy.

Design And Methods: Head and neck rhabdomyosarcoma survivors treated between 1993 and 2017, with ≥2 years of follow-up, without recurrent disease or secondary malignancy were eligible for this study.

Improved ground truth annotation by multimodal image registration from 3D ultrasound to histopathology for resected tongue carcinoma.

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).

Clinical validation of the accuracy of an intra-operative assessment tool using 3D ultrasound compared to histopathology in patients with squamous cell carcinoma of the tongue.

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.

Implementing a deep learning model for automatic tongue tumour segmentation in ex-vivo 3-dimensional ultrasound volumes.

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.