Lucia F, Lovinfosse P, Schick U, Le Pennec R, Pradier O, Salaun P-Y, Hustinx R, Bourbonne V
Radiation Oncology Department, CHU de Brest, 29200 Brest, France; LaTim, Inserm, UMR 1101, université de Brest, 29200 Brest, France; Division of Nuclear Medicine and Oncological Imaging, centre hospitalier universitaire de Liège, Liège, Belgium.
Division of Nuclear Medicine and Oncological Imaging, centre hospitalier universitaire de Liège, Liège, Belgium.
Cancer Radiother. 2023 Sep;27(6-7):542-547. doi: 10.1016/j.canrad.2023.06.001. Epub 2023 Jul 21.
Over the last decades, the refinement of radiation therapy techniques has been associated with an increasing interest for individualized radiation therapy with the aim of increasing or maintaining tumor control and reducing radiation toxicity. Developments in artificial intelligence (AI), particularly machine learning and deep learning, in imaging sciences, including nuclear medecine, have led to significant enthusiasm for the concept of "rapid learning health system". AI combined with radiomics applied to (F)-fluorodeoxyglucose positron emission tomography/computed tomography ([F]-FDG PET/CT) offers a unique opportunity for the development of predictive models that can help stratify each patient's risk and guide treatment decisions for optimal outcomes and quality of life of patients treated with radiation therapy. Here we present an overview of the current contribution of AI and radiomics-based machine learning models applied to (F)-FDG PET/CT in the management of cancer treated by radiation therapy.
在过去几十年中，放射治疗技术的改进与人们对个体化放射治疗的兴趣日益增加相关，目的是提高或维持肿瘤控制并降低放射毒性。人工智能（AI）的发展，特别是机器学习和深度学习在包括核医学在内的成像科学中的应用，引发了人们对“快速学习健康系统”概念的极大热情。AI与应用于（F）-氟脱氧葡萄糖正电子发射断层扫描/计算机断层扫描（[F]-FDG PET/CT）的放射组学相结合，为开发预测模型提供了独特的机会，这些模型可以帮助对每位患者的风险进行分层，并指导治疗决策，以实现接受放射治疗患者的最佳治疗效果和生活质量。在此，我们概述了基于AI和放射组学的机器学习模型应用于（F）-FDG PET/CT在放射治疗癌症管理中的当前贡献。
