Challenges and opportunities in the development and clinical implementation of artificial intelligence based synthetic computed tomography for magnetic resonance only radiotherapy
Villegas, Fernanda (Karolinska University Hospital)
Dal Bello, Riccardo (University Hospital Zurich (Suïssa))
Alvarez-Andres, Emilie (TUD Dresden University of Technology)
Dhont, Jennifer (Université Libre De Bruxelles (ULB))
Janssen, Tomas (The Netherlands Cancer Institute (NKI) (Netherlands))
Milan, Lisa (Ente Ospedaliero Cantonale)
Robert, Charlotte (Gustave Roussy)
Salagean, Ghizela-Ana-Maria (TopMed Medical Centre)
Tejedor, Natalia (Institut de Recerca Sant Pau)
Trnková, Petra (Medical University of Vienna)
Fusella, Marco (Abano Terme Hospital)
Placidi, Lorenzo (Fondazione Policlinico Universitario Agostino Gemelli)
Cusumano, Davide (Mater Olbia Hospital)
Universitat Autònoma de Barcelona

Fecha:	2024
Resumen:	Synthetic computed tomography (sCT) generated from magnetic resonance imaging (MRI) can serve as a substitute for planning CT in radiation therapy (RT), thereby removing registration uncertainties associated with multi-modality imaging pairing, reducing costs and patient radiation exposure. CE/FDA-approved sCT solutions are nowadays available for pelvis, brain, and head and neck, while more complex deep learning (DL) algorithms are under investigation for other anatomic sites. The main challenge in achieving a widespread clinical implementation of sCT lies in the absence of consensus on sCT commissioning and quality assurance (QA), resulting in variation of sCT approaches across different hospitals. To address this issue, a group of experts gathered at the ESTRO Physics Workshop 2022 to discuss the integration of sCT solutions into clinics and report the process and its outcomes. This position paper focuses on aspects of sCT development and commissioning, outlining key elements crucial for the safe implementation of an MRI-only RT workflow.
Derechos:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Lengua:	Anglès
Documento:	Article de revisió ; recerca ; Versió publicada
Materia:	Artificial intelligence ; Clinical implementation ; Deep learning ; MR-only planning ; MR-only radiotherapy ; Synthetic CT
Publicado en:	Radiotherapy and oncology, Vol. 198 (september 2024) , p. 110387, ISSN 1879-0887

DOI: 10.1016/j.radonc.2024.110387
PMID: 38885905

12 p, 625.2 KB

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Documentos de investigación > Documentos de los grupos de investigación de la UAB > Centros y grupos de investigación (producción científica) > Ciencias de la salud y biociencias > Institut de Recerca Sant Pau
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