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.
Enterotoxigenic Escherichia coli (ETEC)-mediated diarrhea can be mitigated by inhibiting bacterial adhesion to intestinal surface. Some lactic acid bacteria (LAB) produce exopolysaccharides (EPS) that can inhibit ETEC adhesion. In this study, we fermented soy flour-based dough (SoyD) with EPS-producing LAB strains Pediococcus pentosaceus TL (PpTL), Leuconostoc citreum TR (LcTR), Leuconostoc mesenteroides WA (LmWA) and L.
View Article and Find Full Text PDFBackground: Patients with head and neck squamous cell carcinoma (HNSCC) face several physical, emotional, and psychological challenges throughout treatment. Cisplatin-based chemoradiotherapy (CRT) is an effective but toxic treatment, with an increased risk for toxicities in patients with low skeletal muscle mass (SMM). Consequently, these patients are anticipated to experience greater treatment-related difficulties.
View Article and Find Full Text PDFAim: To investigate the effect of pyruvate and glucose on leucine transamination and 3-methylbutanal production by Lactococcus lactis, including the comparison with cells possessing glutamate dehydrogenase (GDH) activity.
Methods And Results: Lactococcus lactis cells were incubated in chemically defined medium (CDM) with the pH controlled at 5.2 to mimic cheese conditions.
Radiotherapy (RT) is a standard treatment for head and neck cancer (HNC) and chemoradiotherapy (CRT) is indicated for patients with locally advanced disease. Toxicities during treatment are common and can lead to early cessation of chemotherapy and radiotherapy (RT) interruptions, which can affect oncologic outcomes. Skeletal muscle mass (SMM) is a new biomarker to predict toxicities and overall survival.
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