Assessing population-based to personalized planning strategies for head and neck adaptive radiotherapy.

ABSTRACT:

Purpose

Optimal head-and-neck cancer (HNC) treatment planning requires accurate and feasible planning goals to meet dosimetric constraints and generate robust online adaptive treatment plans. A new x-ray-based adaptive radiotherapy (ART) treatment planning system (TPS) version 2.0 emulator includes novel methods to drive the planning process including the revised intelligent optimization engine algorithm (IOE2). HNC is among the most challenging and complex sites and heavily depends on planner skill and experience to successfully generate a reference plan. Therefore, we evaluate the new TPS performance via conventionally accepted planning strategies with/without artificial intelligence (AI) and knowledge-based planning (KBP).

Methods

Our institution has a pre-clinical releas

SUBMITTER: Visak J 

PROVIDER: S-EPMC11905247 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature