Novel radiotherapy target definition using AI-driven predictions of glioblastoma recurrence from metabolic and diffusion MRI.

Journal: NPJ digital medicine
Published Date: Aug 7, 2025

Abstract

The current standard-of-care (SOC) practice for defining the clinical target volume (CTV) for radiation therapy (RT) in patients with glioblastoma still employs an isotropic 1-2 cm expansion of the T2-hyperintensity lesion, without considering the heterogeneous infiltrative nature of these tumors. This study aims to improve RT CTV definition in patients with glioblastoma by incorporating biologically relevant metabolic and physiologic imaging acquired before RT along with a deep learning model that can predict regions of subsequent tumor progression by either the presence of contrast-enhancement or T2-hyperintensity. The results were compared against two standard CTV definitions. Our multi-parametric deep learning model significantly outperformed the uniform 2 cm expansion of the T2-lesion CTV in terms of specificity (0.89 ± 0.05 vs 0.79 ± 0.11; p = 0.004), while also achieving comparable sensitivity (0.92 ± 0.11 vs 0.95 ± 0.08; p = 0.10), sparing more normal brain. Model performance was significantly enhanced by incorporating lesion size-weighted loss functions during training and including metabolic images as inputs.

Authors

  • Nate Tran
  • Tracy L Luks
    Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA.
  • Yan Li
    Interdisciplinary Research Center for Biology and Chemistry, Liaoning Normal University, Dalian, China.
  • Angela Jakary
    From the Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA.
  • Jacob Ellison
    Department of Radiology & Biomedical Imaging, University of California, San Francisco, CA, USA.
  • Bo Liu
    Wuhan United Imaging Healthcare Surgical Technology Co., Ltd., Wuhan, China.
  • Oluwaseun Adegbite
    Department of Radiology & Biomedical Imaging, University of California, San Francisco, CA, USA.
  • Devika Nair
    Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA.
  • Pranav Kakhandiki
    Department of Radiology & Biomedical Imaging, University of California, San Francisco, CA, USA.
  • Annette M Molinaro
    Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA.
  • Javier E Villanueva-Meyer
    1 Department of Radiology and Biomedical Imaging, University of California, San Francisco, 505 Parnassus Ave, L-352, San Francisco, CA 94143.
  • Nicholas Butowski
    Department of Neurological Surgery, University of California, San Francisco, CA, USA.
  • Jennifer L Clarke
    Department of Neurological Surgery, University of California, San Francisco, CA 94117, United States.
  • Susan M Chang
    Department of Neurological Surgery, University of California, San Francisco, San Francisco, California, USA.
  • Steve E Braunstein
    Department of Radiation Oncology, University of California, San Francisco, CA, USA.
  • Olivier Morin
    Department of Radiation Oncology, University of California, San Francisco, California.
  • Hui Lin
    Department of Mechanical Aerospace and Nuclear Engineering, Rensselaer Polytechnic Institute, Troy, NY, United States of America.
  • Janine M Lupo
    Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA; UCSF/UC Berkeley Graduate Group in Bioengineering, USA. Electronic address: janine.lupo@ucsf.edu.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals