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, De Spirito Marco, Sala Evis, Tagliaferri Luca
Dipartimento di Diagnostica per Immagini e Radioterapia Oncologica, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy.
Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, The Netherlands.
Clin Transl Radiat Oncol. 2024 Sep 22;49:100865. doi: 10.1016/j.ctro.2024.100865. eCollection 2024 Nov.
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.
本综述探讨了人工智能(AI)在介入放射治疗(IRT)中的整合,强调其简化工作流程和提升患者护理的潜力。通过对2002年至2024年期间78篇相关论文的系统分析,我们确定了在轮廓勾画、治疗计划、结果预测和质量保证方面的重大进展。人工智能驱动的方法有望减少手术时间、实现个性化治疗并改善肿瘤患者的治疗效果。然而,临床验证和质量保证协议等挑战依然存在。尽管如此,人工智能为优化介入放射治疗和满足不断变化的患者需求提供了变革性机遇。