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Applications of artificial intelligence in stereotactic body radiation therapy.
Mancosu, Pietro; Lambri, Nicola; Castiglioni, Isabella; Dei, Damiano; Iori, Mauro; Loiacono, Daniele; Russo, Serenella; Talamonti, Cinzia; Villaggi, Elena; Scorsetti, Marta; Avanzo, Michele.
Afiliación
  • Mancosu P; IRCCS Humanitas Research Hospital, Medical Physics Unit, via Manzoni 56, I-20089 Rozzano, Milan, Italy.
  • Lambri N; IRCCS Humanitas Research Hospital, Medical Physics Unit, via Manzoni 56, I-20089 Rozzano, Milan, Italy.
  • Castiglioni I; Department of Biomedical Sciences, Humanitas University, via Rita Levi Montalcini 4, I-20072 Pieve Emanuele, Milan, Italy.
  • Dei D; University of Milan-Bicocca, Department of Physics 'G. Occhialini', piazza della Scienza 2, I-20126 Milano, Italy.
  • Iori M; DeepTrace Technologies S.R.L., via Conservatorio 17, I-20122, Milano, Italy.
  • Loiacono D; Department of Biomedical Sciences, Humanitas University, via Rita Levi Montalcini 4, I-20072 Pieve Emanuele, Milan, Italy.
  • Russo S; IRCCS Humanitas Research Hospital, Radiotherapy and Radiosurgery Department, via Manzoni 56, I-20089 Rozzano, Milan, Italy.
  • Talamonti C; Medical Physics Unit, Azienda USL-IRCCS di Reggio Emilia, I-42122 Reggio Emilia, Italy.
  • Villaggi E; Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy.
  • Scorsetti M; Medical Physics Unit, Azienda USL Toscana Centro, Firenze, Italy.
  • Avanzo M; Department Biomedical Experimental and Clinical Science 'Mario Serio', University of Florence, I-50134 Florence, Italy.
Phys Med Biol ; 67(16)2022 08 08.
Article en En | MEDLINE | ID: mdl-35785778
ABSTRACT
This topical review focuses on the applications of artificial intelligence (AI) tools to stereotactic body radiation therapy (SBRT). The high dose per fraction and the limited number of fractions in SBRT require stricter accuracy than standard radiation therapy. The intent of this review is to describe the development and evaluate the possible benefit of AI tools integration into the radiation oncology workflow for SBRT automation. The selected papers were subdivided into four sections, representative of the whole radiotherapy process 'AI in SBRT target and organs at risk contouring', 'AI in SBRT planning', 'AI during the SBRT delivery', and 'AI for outcome prediction after SBRT'. Each section summarises the challenges, as well as limits and needs for improvement to achieve better integration of AI tools in the clinical workflow.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Radiocirugia Tipo de estudio: Etiology_studies / Prognostic_studies Idioma: En Revista: Phys Med Biol Año: 2022 Tipo del documento: Article País de afiliación: Italia

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Radiocirugia Tipo de estudio: Etiology_studies / Prognostic_studies Idioma: En Revista: Phys Med Biol Año: 2022 Tipo del documento: Article País de afiliación: Italia