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

被引：47
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
来源：
CURRENT ONCOLOGY | 2022年 / 29卷 / 03期
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
[91]   Deep ensemble learning of virtual endoluminal views for polyp detection in CT colonography [J].
Umehara, Kensuke ;
Nappi, Janne J. ;
Hironaka, Toru ;
Regge, Daniele ;
Ishida, Takayuki ;
Yoshida, Hiroyuki .
MEDICAL IMAGING 2017: COMPUTER-AIDED DIAGNOSIS, 2017, 10134
[92]   Deep Learning Localizes and Identifies Polyps in Real Time With 96% Accuracy in Screening Colonoscopy [J].
Urban, Gregor ;
Tripathi, Priyam ;
Alkayali, Talal ;
Mittal, Mohit ;
Jalali, Farid ;
Karnes, William ;
Baldi, Pierre .
GASTROENTEROLOGY, 2018, 155 (04) :1069-+
[93]   Polyp miss rate determined by tandem colonoscopy: A systematic review [J].
van Rijn, JC ;
Reitsma, JB ;
Stoker, J ;
Bossuyt, PM ;
van Deventer, SJ ;
Dekker, E .
AMERICAN JOURNAL OF GASTROENTEROLOGY, 2006, 101 (02) :343-350
[94]   Machine learning enables detection of early-stage colorectal cancer by whole-genome sequencing of plasma cell-free DNA [J].
Wan, Nathan ;
Weinberg, David ;
Liu, Tzu-Yu ;
Niehaus, Katherine ;
Ariazi, Eric A. ;
Delubac, Daniel ;
Kannan, Ajay ;
White, Brandon ;
Bailey, Mitch ;
Bertin, Marvin ;
Boley, Nathan ;
Bowen, Derek ;
Cregg, James ;
Drake, Adam M. ;
Ennis, Riley ;
Fransen, Signe ;
Gafni, Erik ;
Hansen, Loren ;
Liu, Yaping ;
Otte, Gabriel L. ;
Pecson, Jennifer ;
Rice, Brandon ;
Sanderson, Gabriel E. ;
Sharma, Aarushi ;
St John, John ;
Tang, Catherina ;
Tzou, Abraham ;
Young, Leilani ;
Putcha, Girish ;
Haque, Imran S. .
BMC CANCER, 2019, 19 (01)
[95]   A comprehensive literatures update of clinical researches of superparamagnetic resonance iron oxide nanoparticles for magnetic resonance imaging [J].
Wang, Yi Xiang J. ;
Idee, Jean-Marc .
QUANTITATIVE IMAGING IN MEDICINE AND SURGERY, 2017, 7 (01) :88-122
[96]   Development of Deep Learning Algorithm for Detection of Colorectal Cancer in EHR Data [J].
Wang, Yu-Hsiang ;
Nguyen, Phung-Anh ;
Islam, Mohaimenul ;
Li, Yu-Chuan ;
Yang, Hsuan-Chia .
MEDINFO 2019: HEALTH AND WELLBEING E-NETWORKS FOR ALL, 2019, 264 :438-441
[97]   A tree ensemble-based two-stage model for advanced-stage colorectal cancer survival prediction [J].
Wang, Yuyan ;
Wang, Dujuan ;
Ye, Xin ;
Wang, Yanzhang ;
Yin, Yunqiang ;
Jin, Yaochu .
INFORMATION SCIENCES, 2019, 474 :106-124
[98]  
Wei B, 2018, J ADV OXID TECHNOL, V21, P2017, DOI [10.26802/jaots.2018.03208, DOI 10.26802/JAOTS.2018.03208]
[99]   Individualized prediction of colon cancer recurrence using a nomogram [J].
Weiser, Martin R. ;
Landmann, Ron G. ;
Kattan, Michael W. ;
Gonen, Mithat ;
Shia, Jinru ;
Chou, Joanne ;
Paty, Philip B. ;
Guillem, Jose G. ;
Temple, Larissa K. ;
Schrag, Deborah ;
Saltz, Leonard B. ;
Wong, W. Douglas .
JOURNAL OF CLINICAL ONCOLOGY, 2008, 26 (03) :380-385
[100]   Large scale tissue histopathology image classification, segmentation, and visualization via deep convolutional activation features [J].
Xu, Yan ;
Jia, Zhipeng ;
Wang, Liang-Bo ;
Ai, Yuqing ;
Zhang, Fang ;
lai, Maode ;
Chang, Eric I-Chao .
BMC BIOINFORMATICS, 2017, 18
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