Applications of Artificial Intelligence in Screening, Diagnosis, Treatment, and Prognosis of Colorectal Cancer

被引:35
作者
Qiu, Hang [1 ,2 ]
Ding, Shuhan [3 ]
Liu, Jianbo [4 ,5 ]
Wang, Liya [1 ]
Wang, Xiaodong [4 ,5 ]
机构
[1] Univ Elect Sci & Technol China, Big Data Res Ctr, Chengdu 611731, Peoples R China
[2] Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 611731, Peoples R China
[3] Cornell Univ, Sch Elect & Comp Engn, Ithaca, NY 14853 USA
[4] Sichuan Univ, West China Sch Med, Chengdu 610041, Peoples R China
[5] Sichuan Univ, West China Hosp, Dept Gastrointestinal Surg, Chengdu 610041, Peoples R China
基金
中国国家自然科学基金;
关键词
colorectal cancer; artificial intelligence; machine learning; deep learning; diagnosis; prognosis; treatment; screening; RECTAL-CANCER; CT COLONOGRAPHY; PREDICTION; RISK; COLONOSCOPY; VALIDATION; RECURRENCE; SURVIVAL; SURGERY; LESIONS;
D O I
10.3390/curroncol29030146
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Colorectal cancer (CRC) is one of the most common cancers worldwide. Accurate early detection and diagnosis, comprehensive assessment of treatment response, and precise prediction of prognosis are essential to improve the patients' survival rate. In recent years, due to the explosion of clinical and omics data, and groundbreaking research in machine learning, artificial intelligence (AI) has shown a great application potential in clinical field of CRC, providing new auxiliary approaches for clinicians to identify high-risk patients, select precise and personalized treatment plans, as well as to predict prognoses. This review comprehensively analyzes and summarizes the research progress and clinical application value of AI technologies in CRC screening, diagnosis, treatment, and prognosis, demonstrating the current status of the AI in the main clinical stages. The limitations, challenges, and future perspectives in the clinical implementation of AI are also discussed.
引用
收藏
页码:1773 / 1795
页数:23
相关论文
共 109 条
[101]   Machine Learning Algorithms for Predicting the Recurrence of Stage IV Colorectal Cancer After Tumor Resection [J].
Xu, Yucan ;
Ju, Lingsha ;
Tong, Jianhua ;
Zhou, Cheng-Mao ;
Yang, Jian-Jun .
SCIENTIFIC REPORTS, 2020, 10 (01)
[102]   Development of a real-time endoscopic image diagnosis support system using deep learning technology in colonoscopy [J].
Yamada, Masayoshi ;
Saito, Yutaka ;
Imaoka, Hitoshi ;
Saiko, Masahiro ;
Yamada, Shigemi ;
Kondo, Hiroko ;
Takamaru, Hiroyuki ;
Sakamoto, Taku ;
Sese, Jun ;
Kuchiba, Aya ;
Shibata, Taro ;
Hamamoto, Ryuji .
SCIENTIFIC REPORTS, 2019, 9 (1)
[103]   Tumor Identification in Colorectal Histology Images Using a Convolutional Neural Network [J].
Yoon, Hongjun ;
Lee, Joohyung ;
Oh, Ji Eun ;
Kim, Hong Rae ;
Lee, Seonhye ;
Chang, Hee Jin ;
Sohn, Dae Kyung .
JOURNAL OF DIGITAL IMAGING, 2019, 32 (01) :131-140
[104]   The role of AI technology in prediction, diagnosis and treatment of colorectal cancer [J].
Yu, Chaoran ;
Helwig, Ernest Johann .
ARTIFICIAL INTELLIGENCE REVIEW, 2022, 55 (01) :323-343
[105]  
Yu Tian, 2019, 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), P70, DOI 10.1109/ISBI.2019.8759521
[106]  
Yue X., 2019, P P 11 INT C BIOINF
[107]   Automatic Detection and Classification of Colorectal Polyps by Transferring Low-Level CNN Features From Nonmedical Domain [J].
Zhang, Ruikai ;
Zheng, Yali ;
Mak, Tony Wing Chung ;
Yu, Ruoxi ;
Wong, Sunny H. ;
Lau, James Y. W. ;
Poon, Carmen C. Y. .
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, 2017, 21 (01) :41-47
[108]   Causal Interpretations of Black-Box Models [J].
Zhao, Qingyuan ;
Hastie, Trevor .
JOURNAL OF BUSINESS & ECONOMIC STATISTICS, 2021, 39 (01) :272-281
[109]   The Application of Deep Learning in Cancer Prognosis Prediction [J].
Zhu, Wan ;
Xie, Longxiang ;
Han, Jianye ;
Guo, Xiangqian .
CANCERS, 2020, 12 (03)