Artificial intelligence for assisting cancer diagnosis and treatment in the era of precision medicine

被引:69
|
作者
Chen, Zi-Hang [1 ,2 ]
Lin, Li [1 ]
Wu, Chen-Fei [1 ]
Li, Chao-Feng [3 ]
Xu, Rui-Hua [4 ]
Sun, Ying [1 ]
机构
[1] Sun Yat Sen Univ, Guangdong Key Lab Nasopharyngeal Carcinoma Diag &, State Key Lab Oncol South China,Canc Ctr, Collaborat Innovat Ctr Canc Med,Dept Radiat Oncol, Guangzhou 510060, Guangdong, Peoples R China
[2] Sun Yat Sen Univ, Zhongshan Sch Med, Guangzhou 510080, Guangdong, Peoples R China
[3] Sun Yat Sen Univ, Guangdong Key Lab Nasopharyngeal Carcinoma Diag &, Collaborat Innovat Ctr Canc Med,Canc Ctr, State Key Lab Oncol South China,Artificial Intell, Guangzhou 510060, Guangdong, Peoples R China
[4] Sun Yat Sen Univ, Guangdong Key Lab Nasopharyngeal Carcinoma Diag &, Collaborat Innovat Ctr Canc Med,Canc Ctr, State Key Lab Oncol South China,Dept Med Oncol, Guangzhou 510060, Guangdong, Peoples R China
关键词
artificial intelligence; cancer diagnosis; cancer research; cancer treatment; convolutional neural network; deep learning; deep neural network; oncology; CLINICAL TARGET VOLUME; CONVOLUTIONAL NEURAL-NETWORK; DEEP-LEARNING ALGORITHM; COLORECTAL-CANCER; MICROSATELLITE INSTABILITY; MODEL; SEGMENTATION; PREDICTION; ORGANS; VALIDATION;
D O I
10.1002/cac2.12215
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
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.
引用
收藏
页码:1100 / 1115
页数:16
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