Zhang Zhe, Wei Xiawei
Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu 610041, PR China; State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, and Collaborative Innovation Center for Biotherapy, Chengdu 610041, PR China.
Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu 610041, PR China.
Semin Cancer Biol. 2023 May;90:57-72. doi: 10.1016/j.semcancer.2023.02.005. Epub 2023 Feb 14.
The rapid development of artificial intelligence (AI) technologies in the context of the vast amount of collectable data obtained from high-throughput sequencing has led to an unprecedented understanding of cancer and accelerated the advent of a new era of clinical oncology with a tone of precision treatment and personalized medicine. However, the gains achieved by a variety of AI models in clinical oncology practice are far from what one would expect, and in particular, there are still many uncertainties in the selection of clinical treatment options that pose significant challenges to the application of AI in clinical oncology. In this review, we summarize emerging approaches, relevant datasets and open-source software of AI and show how to integrate them to address problems from clinical oncology and cancer research. We focus on the principles and procedures for identifying different antitumor strategies with the assistance of AI, including targeted cancer therapy, conventional cancer therapy, and cancer immunotherapy. In addition, we also highlight the current challenges and directions of AI in clinical oncology translation. Overall, we hope this article will provide researchers and clinicians with a deeper understanding of the role and implications of AI in precision cancer therapy, and help AI move more quickly into accepted cancer guidelines.
在从高通量测序获得的大量可收集数据的背景下，人工智能（AI）技术的迅速发展带来了对癌症前所未有的理解，并加速了临床肿瘤学新时代的到来，这个新时代以精准治疗和个性化医疗为基调。然而，各种AI模型在临床肿瘤学实践中取得的成果远未达到人们的预期，特别是在临床治疗方案的选择上仍存在许多不确定性，这给AI在临床肿瘤学中的应用带来了重大挑战。在本综述中，我们总结了AI的新兴方法、相关数据集和开源软件，并展示了如何将它们整合起来以解决临床肿瘤学和癌症研究中的问题。我们重点关注在AI辅助下识别不同抗肿瘤策略的原则和程序，包括靶向癌症治疗、传统癌症治疗和癌症免疫治疗。此外，我们还强调了AI在临床肿瘤学转化方面当前面临的挑战和方向。总体而言，我们希望本文能让研究人员和临床医生更深入地理解AI在精准癌症治疗中的作用和意义，并帮助AI更快地纳入公认的癌症指南。
