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
[71]   Deep learning techniques for detecting preneoplastic and neoplastic lesions in human colorectal histological images [J].
Sena, Paola ;
Fioresi, Rita ;
Faglioni, Francesco ;
Losi, Lorena ;
Faglioni, Giovanni ;
Roncucci, Luca .
ONCOLOGY LETTERS, 2019, 18 (06) :6101-6107
[72]   Neo-adjuvant chemoradiotherapy response prediction using MRI based ensemble learning method in rectal cancer patients [J].
Shayesteh, Sajad P. ;
Alikhassi, Afsaneh ;
Esfahani, Armaghan Fard ;
Miraie, M. ;
Geramifar, Parham ;
Bitarafan-rajabi, Ahmad ;
Haddad, Peiman .
PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS, 2019, 62 :111-119
[73]   Machine learning for prediction of chemoradiation therapy response in rectal cancer using pre-treatment and mid-radiation multi-parametric MRI [J].
Shi, Liming ;
Zhang, Yang ;
Nie, Ke ;
Sun, Xiaonan ;
Niu, Tianye ;
Yue, Ning ;
Kwong, Tiffany ;
Chang, Peter ;
Chow, Daniel ;
Chen, Jeon-Hor ;
Su, Min-Ying .
MAGNETIC RESONANCE IMAGING, 2019, 61 :33-40
[74]   Histopathological characteristics and artificial intelligence for predicting tumor mutational burden-high colorectal cancer [J].
Shimada, Yoshifumi ;
Okuda, Shujiro ;
Watanabe, Yu ;
Tajima, Yosuke ;
Nagahashi, Masayuki ;
Ichikawa, Hiroshi ;
Nakano, Masato ;
Sakata, Jun ;
Takii, Yasumasa ;
Kawasaki, Takashi ;
Homma, Kei-ichi ;
Kamori, Tomohiro ;
Oki, Eiji ;
Ling, Yiwei ;
Takeuchi, Shiho ;
Wakai, Toshifumi .
JOURNAL OF GASTROENTEROLOGY, 2021, 56 (06) :547-559
[75]   Cancer Statistics, 2021 [J].
Siegel, Rebecca L. ;
Miller, Kimberly D. ;
Fuchs, Hannah E. ;
Jemal, Ahmedin .
CA-A CANCER JOURNAL FOR CLINICIANS, 2021, 71 (01) :7-33
[76]  
Skrede OJ, 2020, LANCET, V395, P350, DOI 10.1016/S0140-6736(19)32998-8
[77]   Volumetric texture features from higher-order images for diagnosis of colon lesions via CT colonography [J].
Song, Bowen ;
Zhang, Guopeng ;
Lu, Hongbing ;
Wang, Huafeng ;
Zhu, Wei ;
Pickhardt, Perry J. ;
Liang, Zhengrong .
INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY, 2014, 9 (06) :1021-1031
[78]   Fourier transform infrared spectroscopic imaging of colon tissues: evaluating the significance of amide I and C-H stretching bands in diagnostic applications with machine learning [J].
Song, Cai Li ;
Vardaki, Martha Z. ;
Goldin, Robert D. ;
Kazarian, Sergei G. .
ANALYTICAL AND BIOANALYTICAL CHEMISTRY, 2019, 411 (26) :6969-6981
[79]   Robotic proctectomy for rectal cancer: analysis of 71 patients from a single institution [J].
Spanheimer, Philip M. ;
Armstrong, John G. ;
Fu, Sunyang ;
Liao, Junlin ;
Regenbogen, Scott E. ;
Byrn, John C. .
INTERNATIONAL JOURNAL OF MEDICAL ROBOTICS AND COMPUTER ASSISTED SURGERY, 2017, 13 (04)
[80]   Computer Aided Diagnosis for Confocal Laser Endomicroscopy in Advanced Colorectal Adenocarcinoma [J].
Stefanescu, Daniela ;
Streba, Costin ;
Cartana, Elena Tatiana ;
Saftoiu, Adrian ;
Gruionu, Gabriel ;
Gruionu, Lucian Gheorghe .
PLOS ONE, 2016, 11 (05)
234567891011