Closing the gap in plan quality: Leveraging deep-learning dose prediction for adaptive radiotherapy.

Journal: Journal of applied clinical medical physics
PMID: 40108745

Abstract

PURPOSE: Balancing quality and efficiency has been a challenge for online adaptive therapy. Most systems start the online re-optimization with the original planning goals. While some systems allow planners to modify the planning goals, achieving a high-quality plan within time constraints remains a common barrier. This study aims to bolster plan quality by leveraging a deep-learning dose prediction model to predict new planning goals that account for inter-fractional anatomical changes.

Authors

  • Sean J Domal
    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.
  • Justin Visak
    Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas.
  • Michael Dohopolski
    Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, United States of America.
  • Dominic Moon
    Medical Artificial Intelligence and Automation Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
  • Vladimir Avkshtol
    Medical Artificial Intelligence and Automation Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
  • Dan Nguyen
    University of Massachusetts Chan Medical School, Worcester, Massachusetts.
  • Steve Jiang
  • 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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