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

Journal: Journal of applied clinical medical physics
Published Date: Dec 3, 2024

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).

Authors

  • Justin Visak
    Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas.
  • Chien-Yi Liao
    Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan.
  • Xinran Zhong
  • Biling Wang
  • Sean Domal
    Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas.
  • Hui-Ju Wang
    Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
  • Austen Maniscalco
    Medical Artificial Intelligence and Automation Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
  • Arnold Pompos
    Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
  • Dan Nyguen
    Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
  • David Parsons
    Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas. Electronic address: david.parsons@utsouthwestern.edu.
  • Andrew Godley
    Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas.
  • Weiguo Lu
    Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, TX, United States of America.
  • Steve Jiang
  • Dominic Moon
    Medical Artificial Intelligence and Automation Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
  • David Sher
    Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas.
  • Mu-Han Lin
    Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas.

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