Evaluation of deep learning based dose prediction in head and neck cancer patients using two different types of input contours.

ABSTRACT:

Purpose

This study evaluates deep learning (DL) based dose prediction methods in head and neck cancer (HNC) patients using two types of input contours.

Materials and methods

Seventy-five HNC patients undergoing two-step volumetric-modulated arc therapy were included. Dose prediction was performed using the AIVOT prototype (AiRato.Inc, Sendai, Japan), a commercial software with an HD U-net-based dose distribution prediction system. Models were developed for the initial plan (46 Gy/23Fr) and boost plan (24 Gy/12Fr), trained with 65 cases and tested with 10 cases. The 8-channel model used one target (PTV) and seven organs at risk (OARs), while the 10-channel model added two dummy contours (PTV ring and spinal cord PRV). Predicted and deliverable doses, obtained through dose mim

SUBMITTER: Saito M 

PROVIDER: S-EPMC11633794 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature