Head and neck tumors (HNTs) constitute a multifaceted ensemble of pathologies that
primarily involve regions such as the oral cavity, pharynx, and nasal cavity. The intricate …

Abstract Machine learning models have been increasingly considered to model head and
neck cancer outcomes for improved screening, diagnosis, treatment, and prognostication of …

Objectives To investigate whether dosiomics can benefit to IMRT treated patient's
locoregional recurrences (LR) prediction through a comparative study on prediction …

Background This study aimed to investigate the value of clinical, radiomic features extracted
from gross tumor volumes (GTVs) delineated on CT images, dose distributions (Dosiomics) …

Normal tissue complication probability (NTCP) models that were formulated in the
Quantitative Analyses of Normal Tissue Effects in the Clinic (QUANTEC) are one of the …

Laryngeal squamous cell carcinoma is the second most common head and neck cancer. Its
pathogenesis is strongly associated with smoking. The management of this disease is …

Artificial intelligence (AI)-based models have become a growing area of interest in predictive
medicine and have the potential to aid physician decision-making to improve patient …

Purpose To assess the impact of prospectively sparing the parotid ducts via MRI sialography
on patient reported xerostomia for those receiving definitive radiotherapy (RT) for …

Background and Purpose Machine learning (ML) is emerging as a feasible approach to
optimize patients' care path in Radiation Oncology. Applications include autosegmentation …

Background The irradiation of sub-regions of the parotid has been linked to xerostomia
development in patients with head and neck cancer (HNC). In this study, we compared the …