Deep learning prediction of post-SBRT liver function changes and NTCP modeling in hepatocellular carcinoma based on DGAE-MRI.

Journal: Medical physics
PMID: 36988423

Abstract

BACKGROUND: Stereotactic body radiation therapy (SBRT) produces excellent local control for patients with hepatocellular carcinoma (HCC). However, the risk of toxicity for normal liver tissue is still a limiting factor. Normal tissue complication probability (NTCP) models have been proposed to estimate the toxicity with the assumption of uniform liver function distribution, which is not optimal. With more accurate regional liver functional imaging available for individual patient, we can improve the estimation and be more patient-specific.

Authors

  • Lise Wei
    Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA.
  • Madhava P Aryal
    Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA.
  • Kyle Cuneo
    Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA.
  • Martha Matuszak
    b Department of Radiation Oncology , University of Michigan , Ann Arbor , MI , USA.
  • Theodore S Lawrence
    Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA.
  • Randall K Ten Haken
    Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA.
  • Yue Cao
    Department of Forensic Medicine, Nanjing Medical University, Nanjing, 211166, Jiangsu, People's Republic of China.
  • Issam El Naqa
    Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA.

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