Mechanically assisted non-invasive ventilation for liver SABR: Improve CBCT, treat more accurately
Year: 2025
Type: article
Abstract: Background and purpose: 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. Materials and methods: 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. Results: 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. Conclusion: 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.
Authors: Julien Pierrard, Nicolas Audag, Christel Abdel Massih, Maria Alvear Garcia, Enrique Alvarez Moreno, Andrea Colot, Simon Jardinet, Romain Mony, Ana Francisca Nevez Marques, Lola Servaes, Thaïs Tison, Valentin Van den Bossche, Aniko Wale Etume, Lamyae Zouheir, Geneviève Van Ooteghem
Institutions: Cliniques Universitaires Saint-Luc, UCLouvain
Language: English
Subfield: Radiation
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,430
Award: G1057654993
OpenAlex ID: W4410615634
