S2161
Physics - Inter-fraction motion management and daily adaptive radiotherapy
ESTRO 2026
6 Medical Physics Unit, Ospedale S. Chiara APSS Trento, Trento, Italy. 7 UOSD Fisica Sanitaria, ASL2 Lanciano- Vasto-Chieti, Chieti, Italy. 8 Radiation Oncology Department, Policlinico Abano Terme, Abano Terme, Italy. 9 Medical Physics Unit, Città della salute e della Scienza di Torino, Torino, Italy. 10 Unit of Radiation Research( IEO), European Institute of Oncology IRCCS, Milano, Italy. 11 Medical Physics Uni, , Azienda Ospedaliero Universitaria Pisana, Pisa, Italy. 12 Medical Physics Unit,, ASLCN2 di Alba e Bra, Alba e Bra, Italy. 13 Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milano, Italy. 14 Medical Physics Unit, Department of Radiotherapy and Radiosurgery,, IRCCS Humanitas Research Hospital, Rozzano (Milano), Italy. 15 Medical Physics Unit, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) “Dino Amadori”, Meldola, Italy. 16 Nuclear Medicine and PET/CT Centre, Imaging Institute of Southern Switzerland, Ente Ospedaliero Cantonale, Lugano, Switzerland. 17 Medical Physics Team, RadiQA Services, PURA, Switzerland. 18 Radiotherapy service, CHU Tivoli, La Louvière, Belgium. 19 Medical Physics Department, ASL TO4, Ivrea, Italy. 20 Medical Physics Unit Florence, Department of Hospitals Network, Azienda USL Toscana Centro, Firenze, Italy. 21 Medical Physics, Vicenza General Hospital, Vicenza, Italy. 22 Medical Physics Unit, IRCCS Istituto Nazionale Tumori Regina Elena, Roma, Italy
Surface-guided radiotherapy (SGRT) was used in 5 studies. Reported pre-treatment displacements ranged from 0.8 ± 0.6 mm to 5 ± 5.5 mm, while intra- treatment motion ranged from 0.06 ± 0.72 mm to 1.04 ± 0.31 mm. Variability was influenced by target site, immobilization strategy, treatment platform, and imaging modality. Conclusion: Margin selection in Spine SBRT must be tailored to the available imaging and delivery technologies, with careful consideration of spinal target location and setup verification strategy to ensure optimal treatment precision and safety. Keywords: Margin Selection, Spine SBRT, imaging chain Digital Poster 3766 Diffusion-based synthetic-CT generation from breast CBCT Lorenzo Colombo 1 , Audrey Duran 2 , Quentin Spinat 2 , Pierre Olléon 2 , Sami Romdhani 2 , Nikolaos Paragyios 3 , Pauline Maury 4 , Pascal Fenoglietto 5 1 Clinical affairs, TheraPanacea, Paris, France. 2 AI engineering, TheraPanacea, Paris, France. 3 CEO, TheraPanacea, Paris, France. 4 Department of radiaition oncology, Institut Gustave Roussy, Villejuif, France. 5 Department of radiation oncology, Institut du Cancer de Montpellier, Montpellier, France Purpose/Objective: Cone-beam CT (CBCT) imaging is routinely employed in radiation therapy for patient positioning, but its limited image quality, spatial resolution, and field of view restrict its broader clinical use. Extending its role beyond positioning could accelerate the adoption of adaptive radiotherapy. To achieve this, improvements enabling organ-at-risk delineation, dose calculation, and treatment replanning are essential. This study clinically evaluates an AI–based solution that generates synthetic-CTs (sCTs) from CBCT images, addressing major technical constraints and demonstrating the potential to fully exploit CBCT for adaptive radiotherapy in breast cancer management. Material/Methods: .Training a 3D multimodal translation model for medical imaging presents several challenges, including the large computational burden of volumetric data, the scarcity of high-quality paired datasets, and the need to preserve quantitative fidelity for clinical use. To overcome these, a 3D latent diffusion model was developed. Its variational autoencoder (VAE) component achieves strong compression with high reconstruction quality and computational efficiency. The diffusion model was first trained in an unsupervised manner on unpaired data, then fine-
Purpose/Objective: The AIFM SBRT (Spine Stereotactic Body
Radiotherapy) subgroup conducted a systematic review (2019–2024) to evaluate how imaging modalities and recent technological innovations influence treatment accuracy in SBRT, focusing on uncertainties along the imaging chain. Material/Methods: A PROSPERO-registered systematic review was performed across PubMed, Embase, Scopus, Web of Science, and Google Scholar using the terms SBRT, stereotactic, SABR, spine, imaging, IGRT, SGRT, accuracy, precision, margin, uncertainties, set-up error, and phantom. References were managed with EndNote and Rayyan. For each study, imaging modality for target delineation and patient positioning, fusion techniques, target volumes, margins, and dose prescriptions were extracted, with particular attention to CTV-to-PTV and PRV margin definitions. Results: Of 807 records identified, 25 studies met inclusion criteria (3 phantom, 4 planning, and 19 clinical—16 retrospective, 3 prospective). Five linear accelerator models were analyzed, employing 6–10FFF MV photon beams with IMRT, VMAT, or non-isocentric delivery. Imaging protocols included pre-treatment (n=19: 6 kV, 10 CBCT, 3 both), intra-treatment (n=17: 13 kV, 4 CBCT, 2 both), and post-treatment (n=7: 1 kV, 6 CBCT).
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