Artificial intelligence (AI) applications in improvement of IMRT and VMAT radiotherapy treatment planning processes: A systematic review.

Autor: Zadnorouzi M; Department of Physics, University of Guilan, Rasht, Iran., Abtahi SMM; Physics Department, Imam Khomeini International University, Qazvin, Iran. Electronic address: sm.abtahi@sci.ikiu.ac.ir.
Jazyk: angličtina
Zdroj: Radiography (London, England : 1995) [Radiography (Lond)] 2024 Oct; Vol. 30 (6), pp. 1530-1535. Date of Electronic Publication: 2024 Sep 24.
DOI: 10.1016/j.radi.2024.09.049
Abstrakt: Introduction: Radiotherapy is a common option in the treatment of many types of cancer. Intensity-Modulated Radiation Therapy (IMRT) and Volumetric-Modulated Arc Therapy (VMAT) are the latest radiotherapy techniques. However, clinicians face problems due to these techniques' complexity and time-consuming planning. Various studies have pointed out the importance and role of artificial intelligence (AI) in radiotherapy and accelerating and improving its quality. This research explores different AI methods in different fields of IMRT and VMAT. This study evaluated both quantitative and qualitative methods used within the reviewed articles.
Methods: Various articles were reviewed from Google Scholar, Science Direct, and PubMed databases between 2018 and 2024. According to PRISMA 2020 guidelines, study selection processes, screening, and inclusion and exclusion criteria were defined. The critical Appraisal Skill Program qualitative checklist tool was used for the qualitative evaluation of articles.
Results: 26 articles met the inclusion among the 33 articles obtained. The search procedure was displayed using the PRISMA flow diagram. The evaluation of the articles shows the automation of various treatment planning processes by AI methods and their better performance than traditional methods. The qualitative evaluation of studies has demonstrated the high quality of all studies. The lowest score obtained from the qualitative evaluation of the article is 7 out of 9.
Conclusion: AI methods used in radiotherapy reduce time and increase prediction accuracy. They also work better than other methods in different areas, such as dose prediction, treatment design, and dose delivery.
Implications for Practice: Healthcare providers should consider integrating artificial intelligence technologies into their practice to optimize treatment planning and enhance patient care in radiation therapy. Additionally, fostering collaboration between radiotherapy experts and artificial intelligence specialists can significantly improve the development and application of AI technologies in this field.
Competing Interests: Conflict of interest statement The authors have no competing interests, and there was no funding or personal relationships with other people or organizations for this research.
(Copyright © 2024 The College of Radiographers. Published by Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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