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.

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. - In: PHYSICS IN MEDICINE AND BIOLOGY. - ISSN 0031-9155. - STAMPA. - 67:(2022), pp. 16TR01.0-16TR01.0. [10.1088/1361-6560/ac7e18]

Applications of artificial intelligence in stereotactic body radiation therapy

Russo, Serenella;Talamonti, Cinzia;
2022

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.
2022
67
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Mancosu, Pietro; Lambri, Nicola; Castiglioni, Isabella; Dei, Damiano; Iori, Mauro; Loiacono, Daniele; Russo, Serenella; Talamonti, Cinzia; Villaggi, E...espandi
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1286511
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