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Artificial Intelligence in magnetic Resonance guided Radiotherapy: Medical and physical considerations on state of art and future perspectives.
Cusumano, Davide; Boldrini, Luca; Dhont, Jennifer; Fiorino, Claudio; Green, Olga; Güngör, Görkem; Jornet, Núria; Klüter, Sebastian; Landry, Guillaume; Mattiucci, Gian Carlo; Placidi, Lorenzo; Reynaert, Nick; Ruggieri, Ruggero; Tanadini-Lang, Stephanie; Thorwarth, Daniela; Yadav, Poonam; Yang, Yingli; Valentini, Vincenzo; Verellen, Dirk; Indovina, Luca.
Afiliación
  • Cusumano D; Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome, Italy.
  • Boldrini L; Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome, Italy.
  • Dhont J; Maastro Clinic, Maastrict, the Netherlands.
  • Fiorino C; Medical Physics, San Raffaele Scientific Institute, Milan, Italy.
  • Green O; Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO, USA.
  • Güngör G; Acibadem MAA University, School of Medicine, Department of Radiation Oncology, Maslak Istanbul, Turkey.
  • Jornet N; Servei de Radiofísica i Radioprotecció, Hospital de la Santa Creu i Sant Pau, Spain.
  • Klüter S; Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany.
  • Landry G; Department of Radiation Oncology, LMU Munich, Munich, Germany; German Cancer Consortium (DKTK), Munich, Germany.
  • Mattiucci GC; Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome, Italy.
  • Placidi L; Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome, Italy. Electronic address: lorenzo.placidi@policlinicogemelli.it.
  • Reynaert N; Department of Medical Physics, Institut Jules Bordet, Belgium.
  • Ruggieri R; Dipartimento di Radioterapia Oncologica Avanzata, IRCCS "Sacro cuore - don Calabria", Negrar di Valpolicella (VR), Italy.
  • Tanadini-Lang S; Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland.
  • Thorwarth D; Section for Biomedical Physics, Department of Radiation Oncology, University Hospital Tüebingen, Tübingen, Germany.
  • Yadav P; Department of Human Oncology School of Medicine and Public Heath University of Wisconsin - Madison, USA.
  • Yang Y; Department of Radiation Oncology, David Geffen School of Medicine, University of California Los Angeles, USA.
  • Valentini V; Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome, Italy.
  • Verellen D; Department of Medical Physics, Iridium Cancer Network, Belgium; Faculty of Medicine and Health Sciences, Antwerp University, Antwerp, Belgium.
  • Indovina L; Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome, Italy.
Phys Med ; 85: 175-191, 2021 May.
Article en En | MEDLINE | ID: mdl-34022660
Over the last years, technological innovation in Radiotherapy (RT) led to the introduction of Magnetic Resonance-guided RT (MRgRT) systems. Due to the higher soft tissue contrast compared to on-board CT-based systems, MRgRT is expected to significantly improve the treatment in many situations. MRgRT systems may extend the management of inter- and intra-fraction anatomical changes, offering the possibility of online adaptation of the dose distribution according to daily patient anatomy and to directly monitor tumor motion during treatment delivery by means of a continuous cine MR acquisition. Online adaptive treatments require a multidisciplinary and well-trained team, able to perform a series of operations in a safe, precise and fast manner while the patient is waiting on the treatment couch. Artificial Intelligence (AI) is expected to rapidly contribute to MRgRT, primarily by safely and efficiently automatising the various manual operations characterizing online adaptive treatments. Furthermore, AI is finding relevant applications in MRgRT in the fields of image segmentation, synthetic CT reconstruction, automatic (on-line) planning and the development of predictive models based on daily MRI. This review provides a comprehensive overview of the current AI integration in MRgRT from a medical physicist's perspective. Medical physicists are expected to be major actors in solving new tasks and in taking new responsibilities: their traditional role of guardians of the new technology implementation will change with increasing emphasis on the managing of AI tools, processes and advanced systems for imaging and data analysis, gradually replacing many repetitive manual tasks.
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Texto completo: 1 Base de datos: MEDLINE Asunto principal: Inteligencia Artificial / Radioterapia Guiada por Imagen Tipo de estudio: Guideline / Prognostic_studies Idioma: En Revista: Phys Med Asunto de la revista: BIOFISICA / BIOLOGIA / MEDICINA Año: 2021 Tipo del documento: Article

Texto completo: 1 Base de datos: MEDLINE Asunto principal: Inteligencia Artificial / Radioterapia Guiada por Imagen Tipo de estudio: Guideline / Prognostic_studies Idioma: En Revista: Phys Med Asunto de la revista: BIOFISICA / BIOLOGIA / MEDICINA Año: 2021 Tipo del documento: Article