Department of Nuclear Medicine, University Hospital Essen; West German Cancer Center; German Cancer Consortium (DKTK), Essen, Germany.
Department of Nuclear Medicine, Assistance Publique - Hôpitaux de Paris, Paris, France.
Semin Nucl Med. 2023 Sep;53(5):687-693. doi: 10.1053/j.semnuclmed.2023.03.003. Epub 2023 Apr 8.
This review provides an overview of the current opportunities for integrating artificial intelligence methods into the field of preclinical imaging research in nuclear medicine. The growing demand for imaging agents and therapeutics that are adapted to specific tumor phenotypes can be excellently served by the evolving multiple capabilities of molecular imaging and theranostics. However, the increasing demand for rapid development of novel, specific radioligands with minimal side effects that excel in diagnostic imaging and achieve significant therapeutic effects requires a challenging preclinical pipeline: from target identification through chemical, physical, and biological development to the conduct of clinical trials, coupled with dosimetry and various pre, interim, and post-treatment staging images to create a translational feedback loop for evaluating the efficacy of diagnostic or therapeutic ligands. In virtually all areas of this pipeline, the use of artificial intelligence and in particular deep-learning systems such as neural networks could not only address the above-mentioned challenges, but also provide insights that would not have been possible without their use. In the future, we expect that not only the clinical aspects of nuclear medicine will be supported by artificial intelligence, but that there will also be a general shift toward artificial intelligence-assisted in silico research that will address the increasingly complex nature of identifying targets for cancer patients and developing radioligands.
这篇综述概述了将人工智能方法整合到核医学临床前成像研究领域的当前机会。分子成像和治疗学的多种功能不断发展，为适应特定肿瘤表型的成像剂和治疗剂提供了极好的服务。然而，对开发具有最小副作用、在诊断成像中表现出色并能实现显著治疗效果的新型、特异性放射性配体的需求不断增加，这需要一个具有挑战性的临床前管道：从靶标识别到化学、物理和生物开发，再到临床试验的进行，再加上剂量测定和各种预处理、中期和后期治疗分期图像，以创建一个用于评估诊断或治疗配体疗效的转化反馈循环。在该管道的几乎所有领域，人工智能的使用，特别是神经网络等深度学习系统，不仅可以解决上述挑战，还可以提供没有它们的使用就不可能获得的见解。未来，我们预计不仅核医学的临床方面将得到人工智能的支持，而且还将出现向人工智能辅助的计算机研究的普遍转变，以解决为癌症患者确定靶标和开发放射性配体的日益复杂的性质。
