Mechanically assisted non-invasive ventilation for liver SABR: Improve CBCT, treat more accurately.

Authors

Pierrard J,Audag N,Massih CA,Garcia MA,Moreno EA,Colot A,Jardinet S,Mony R,Nevez Marques AF,Servaes L,Tison T,den Bossche VV,Etume AW,Zouheir L,Ooteghem GV

Affiliations (4)

  • Institut de Recherche Experimentale et Clinique (IREC), Center of Molecular Imaging, Radiotherapy and Oncology (MIRO), Université Catholique de Louvain, Brussels, Belgium.
  • Department of Radiation Oncology, Cliniques Universitaires Saint-Luc, Brussels, Belgium.
  • Institut de Recherche Expérimentale et Clinique (IREC), Pôle de Pneumologie, ORL (airways) & Dermatologie (skin), Groupe Recherche en Kinésithérapie Respiratoire, Université Catholique de Louvain, Brussels, Belgium.
  • Service de kinésithérapie et ergothérapie, Cliniques universitaires Saint-Luc, Brussels, Belgium.

Abstract

Cone-beam computed tomography (CBCT) for image-guided radiotherapy (IGRT) during liver stereotactic ablative radiotherapy (SABR) is degraded by respiratory motion artefacts, potentially jeopardising treatment accuracy. Mechanically assisted non-invasive ventilation-induced breath-hold (MANIV-BH) can reduce these artefacts. This study compares MANIV-BH and free-breathing CBCTs regarding image quality, IGRT variability, automatic registration accuracy, and deep-learning auto-segmentation performance. Liver SABR CBCTs were presented blindly to 14 operators: 25 patients with FB and 25 with MANIV-BH. They rated CBCT quality and IGRT ease (rigid registration with planning CT). Interoperator IGRT variability was compared between FB and MANIV-BH. Automatic gross tumour volume (GTV) mapping accuracy was compared using automatic rigid registration and image-guided deformable registration. Deep-learning organ-at-risk (OAR) auto-segmentation was rated by an operator, who recorded the time dedicated for manual correction of these volumes. MANIV-BH significantly improved CBCT image quality ("Excellent"/"Good": 83.4 % versus 25.4 % with FB, p < 0.001), facilitated IGRT ("Very easy"/"Easy": 68.0 % versus 38.9 % with FB, p < 0.001), and reduced IGRT variability, particularly for trained operators (overall variability of 3.2 mm versus 4.6 mm with FB, p = 0.010). MANIV-BH improved deep-learning auto-segmentation performance (80.0 % rated "Excellent"/"Good" versus 4.0 % with FB, p < 0.001), and reduced median manual correction time by 54.2 % compared to FB (p < 0.001). However, automatic GTV mapping accuracy was not significantly different between MANIV-BH and FB. In liver SABR, MANIV-BH significantly improves CBCT quality, reduces interoperator IGRT variability, and enhances OAR auto-segmentation. Beyond being safe and effective for respiratory motion mitigation, MANIV increases accuracy during treatment delivery, although its implementation requires resources.

Topics

Journal Article

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