Artificial intelligence in interventional radiotherapy (brachytherapy): Enhancing patient-centered care and addressing patients’ needs
Publication date
2024-11
Authors
Fionda, Bruno
Placidi, Elisa
de Ridder, Mischa
Strigari, Lidia
Patarnello, Stefano
Tanderup, Kari
Hannoun-Levi, Jean Michel
Siebert, Frank André
Boldrini, Luca
Antonietta Gambacorta, Maria
Editors
Advisors
Supervisors
Document Type
Article
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cc_by_nc_nd
Abstract
This review explores the integration of artificial intelligence (AI) in interventional radiotherapy (IRT), emphasizing its potential to streamline workflows and enhance patient care. Through a systematic analysis of 78 relevant papers spanning from 2002 to 2024, we identified significant advancements in contouring, treatment planning, outcome prediction, and quality assurance. AI-driven approaches offer promise in reducing procedural times, personalizing treatments, and improving treatment outcomes for oncological patients. However, challenges such as clinical validation and quality assurance protocols persist. Nonetheless, AI presents a transformative opportunity to optimize IRT and meet evolving patient needs.
Keywords
Artificial intelligence, Brachytherapy, Deep learning, Interventional radiotherapy, Machine learning, Oncology, Radiology Nuclear Medicine and imaging
Citation
Fionda, B, Placidi, E, de Ridder, M, Strigari, L, Patarnello, S, Tanderup, K, Hannoun-Levi, J M, Siebert, F A, Boldrini, L, Antonietta Gambacorta, M, De Spirito, M, Sala, E & Tagliaferri, L 2024, 'Artificial intelligence in interventional radiotherapy (brachytherapy) : Enhancing patient-centered care and addressing patients’ needs', Clinical and translational radiation oncology, vol. 49, 100865. https://doi.org/10.1016/j.ctro.2024.100865