Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, 510060, P. R. China.
Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, 510080, P. R. China.
Cancer Commun (Lond). 2021 Nov;41(11):1100-1115. doi: 10.1002/cac2.12215. Epub 2021 Oct 6.
Over the past decade, artificial intelligence (AI) has contributed substantially to the resolution of various medical problems, including cancer. Deep learning (DL), a subfield of AI, is characterized by its ability to perform automated feature extraction and has great power in the assimilation and evaluation of large amounts of complicated data. On the basis of a large quantity of medical data and novel computational technologies, AI, especially DL, has been applied in various aspects of oncology research and has the potential to enhance cancer diagnosis and treatment. These applications range from early cancer detection, diagnosis, classification and grading, molecular characterization of tumors, prediction of patient outcomes and treatment responses, personalized treatment, automatic radiotherapy workflows, novel anti-cancer drug discovery, and clinical trials. In this review, we introduced the general principle of AI, summarized major areas of its application for cancer diagnosis and treatment, and discussed its future directions and remaining challenges. As the adoption of AI in clinical use is increasing, we anticipate the arrival of AI-powered cancer care.
在过去的十年中,人工智能(AI)在解决各种医学问题方面做出了重大贡献,包括癌症。深度学习(DL)是 AI 的一个分支,其特点是能够进行自动特征提取,并且在同化和评估大量复杂数据方面具有强大的功能。基于大量的医学数据和新型计算技术,人工智能,特别是深度学习,已经应用于肿瘤学研究的各个方面,并且有可能增强癌症的诊断和治疗。这些应用涵盖了从早期癌症检测、诊断、分类和分级、肿瘤的分子特征、预测患者的结果和治疗反应、个性化治疗、自动放射治疗工作流程、新型抗癌药物的发现以及临床试验等多个方面。在这篇综述中,我们介绍了 AI 的一般原理,总结了其在癌症诊断和治疗方面的主要应用领域,并讨论了其未来的方向和存在的挑战。随着 AI 在临床应用中的采用不断增加,我们预计 AI 驱动的癌症治疗将成为现实。