NIH R01 · 2024
Deep learning-based target volume delineation capturing observer variability in head and neck cancer
We propose to develop and evaluate robust deep learning (DL)-based approaches capable of accurately delineating target volumes and predicting recurrence in head and neck cancer (HNC) patients. Radiation therapy (RT) is one of the most common treatments for HNC patients. Advanced RT techniques enable highly conformal dose delivery to target volumes. However, a major challenge in the RT planning for HNC is delineating target tumor volumes. Despite the availability of consensus guidelines, delineating the gross target volume (GTV) and the clinical target volume (CTV) for HNC is time-consuming and requires extensive clinical expertise. It demands a comprehensive understanding of the region's…
From the public funding record at NIH RePORTER. Describes the funded project, not the reviews below.