Artificial intelligence applied to image-guided radiation therapy (IGRT): a systematic review by the Young Group of the Italian Association of Radiotherapy and Clinical Oncology (yAIRO).

التفاصيل البيبلوغرافية
العنوان:	Artificial intelligence applied to image-guided radiation therapy (IGRT): a systematic review by the Young Group of the Italian Association of Radiotherapy and Clinical Oncology (yAIRO).
المؤلفون:	Boldrini L; UOC Radioterapia Oncologica, Fondazione Policlinico Universitario IRCCS 'A. Gemelli', Rome, Italy.; Università Cattolica del Sacro Cuore, Rome, Italy., D'Aviero A; Radiation Oncology, Mater Olbia Hospital, Olbia, Sassari, Italy., De Felice F; Radiation Oncology, Department of Radiological, Policlinico Umberto I, Rome, Italy.; Oncological and Pathological Sciences, 'Sapienza' University of Rome, Rome, Italy., Desideri I; Radiation Oncology Unit, Azienda Ospedaliero-Universitaria Careggi, Department of Experimental and Clinical Biomedical Sciences, University of Florence, Florence, Italy., Grassi R; Department of Precision Medicine, University of Campania 'L. Vanvitelli', Naples, Italy., Greco C; Department of Radiation Oncology, Università Campus Bio-Medico di Roma, Fondazione Policlinico Universitario Campus Bio-Medico, Rome, Italy., Iorio GC; Department of Oncology, Radiation Oncology, University of Turin, Turin, Italy., Nardone V; Department of Precision Medicine, University of Campania 'L. Vanvitelli', Naples, Italy., Piras A; UO Radioterapia Oncologica, Villa Santa Teresa, Bagheria, Palermo, Italy. antoniopiras88@gmail.com., Salvestrini V; Radiation Oncology Unit, Azienda Ospedaliero-Universitaria Careggi, Department of Experimental and Clinical Biomedical Sciences, University of Florence, Florence, Italy.; Cyberknife Center, Istituto Fiorentino di Cura e Assistenza (IFCA), 50139, Florence, Italy.
المصدر:	La Radiologia medica [Radiol Med] 2024 Jan; Vol. 129 (1), pp. 133-151. Date of Electronic Publication: 2023 Sep 23.
نوع المنشور:	Systematic Review; Journal Article; Review
اللغة:	English
بيانات الدورية:	Publisher: Springer Milan Country of Publication: Italy NLM ID: 0177625 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1826-6983 (Electronic) Linking ISSN: 00338362 NLM ISO Abbreviation: Radiol Med Subsets: MEDLINE
أسماء مطبوعة:	Publication: Milan : Springer Milan Original Publication: Torino [etc.] Minerva medica.
مواضيع طبية MeSH:	Radiotherapy, Image-Guided/methods , Radiation Oncology/methods, Humans ; Artificial Intelligence ; Retrospective Studies ; Radiotherapy Planning, Computer-Assisted/methods ; Italy
مستخلص:	Introduction: The advent of image-guided radiation therapy (IGRT) has recently changed the workflow of radiation treatments by ensuring highly collimated treatments. Artificial intelligence (AI) and radiomics are tools that have shown promising results for diagnosis, treatment optimization and outcome prediction. This review aims to assess the impact of AI and radiomics on modern IGRT modalities in RT. Methods: A PubMed/MEDLINE and Embase systematic review was conducted to investigate the impact of radiomics and AI to modern IGRT modalities. The search strategy was "Radiomics" AND "Cone Beam Computed Tomography"; "Radiomics" AND "Magnetic Resonance guided Radiotherapy"; "Radiomics" AND "on board Magnetic Resonance Radiotherapy"; "Artificial Intelligence" AND "Cone Beam Computed Tomography"; "Artificial Intelligence" AND "Magnetic Resonance guided Radiotherapy"; "Artificial Intelligence" AND "on board Magnetic Resonance Radiotherapy" and only original articles up to 01.11.2022 were considered. Results: A total of 402 studies were obtained using the previously mentioned search strategy on PubMed and Embase. The analysis was performed on a total of 84 papers obtained following the complete selection process. Radiomics application to IGRT was analyzed in 23 papers, while a total 61 papers were focused on the impact of AI on IGRT techniques. Discussion: AI and radiomics seem to significantly impact IGRT in all the phases of RT workflow, even if the evidence in the literature is based on retrospective data. Further studies are needed to confirm these tools' potential and provide a stronger correlation with clinical outcomes and gold-standard treatment strategies. (© 2023. Italian Society of Medical Radiology.)
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فهرسة مساهمة:	Keywords: Artificial intelligence; Deep learning; Image-guided radiation therapy; Machine learning; Radiomics
تواريخ الأحداث:	Date Created: 20230923 Date Completed: 20240125 Latest Revision: 20240125
رمز التحديث:	20240125
DOI:	10.1007/s11547-023-01708-4
PMID:	37740838
قاعدة البيانات:	MEDLINE

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DOI:	10.1007/s11547-023-01708-4