神经肿瘤学中的人工智能反应评估（AI-RANO），第 1 部分：当前进展综述。
Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 1: review of current advancements.
机构信息
Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA; Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA.
Division of Computational Pathology, Department of Pathology & Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Radiology & Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Biostatistics & Health Data Science, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Neurological Surgery, Indiana University School of Medicine, Indianapolis, IN, USA; Indiana University Melvin and Bren Simon Comprehensive Cancer Center, Indianapolis, IN, USA; Department of Computer Science, Luddy School of Informatics, Computing, and Engineering, Indiana University, Indianapolis, IN, USA.
出版信息
Lancet Oncol. 2024 Nov;25(11):e581-e588. doi: 10.1016/S1470-2045(24)00316-4.
The development, application, and benchmarking of artificial intelligence (AI) tools to improve diagnosis, prognostication, and therapy in neuro-oncology are increasing at a rapid pace. This Policy Review provides an overview and critical assessment of the work to date in this field, focusing on diagnostic AI models of key genomic markers, predictive AI models of response before and after therapy, and differentiation of true disease progression from treatment-related changes, which is a considerable challenge based on current clinical care in neuro-oncology. Furthermore, promising future directions, including the use of AI for automated response assessment in neuro-oncology, are discussed.
人工智能（AI）工具在神经肿瘤学中的开发、应用和基准测试正以前所未有的速度发展，以改善诊断、预后和治疗。本政策综述概述并批判性评估了该领域迄今为止的工作，重点是关键基因组标记的诊断 AI 模型、治疗前后反应的预测性 AI 模型，以及真正的疾病进展与治疗相关变化的区分，这是神经肿瘤学中当前临床护理面临的一项重大挑战。此外，还讨论了 AI 在神经肿瘤学中用于自动反应评估的有前途的未来方向。
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