CT ventilation images produced by a 3D neural network show improvement over the Jacobian and HU DIR-based methods to predict quantized lung function.

Journal: Medical physics
PMID: 39579381

Abstract

BACKGROUND: Radiation-induced pneumonitis affects up to 33% of non-small cell lung cancer (NSCLC) patients, with fatal pneumonitis occurring in 2% of patients. Pneumonitis risk is related to the dose and volume of lung irradiated. Clinical radiotherapy plans assume lungs are functionally homogeneous, but evidence suggests that avoidance of high-functioning lung during radiotherapy can reduce the risk of radiation-induced pneumonitis. Radiotherapy avoidance structures can be constructed based on high-function regions indicated in a ventilation map, which can be produced from CT images.

Authors

  • Daryl Wilding-McBride
    Medical Radiations, School of Health and Biomedical Sciences, RMIT University, Bundoora, Victoria, Australia.
  • Jeremy Lim
    Image X Institute, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia.
  • Hilary Byrne
    Image X Institute, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia.
  • Ricky O'Brien
    Medical Radiations, School of Health and Biomedical Sciences, RMIT University, Bundoora, Victoria, Australia.

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