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
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
    Sena, Paola
    Fioresi, Rita
    Faglioni, Francesco
    Losi, Lorena
    Faglioni, Giovanni
    Roncucci, Luca
    [J]. ONCOLOGY LETTERS, 2019, 18 (06) : 6101 - 6107
  • [72] Neo-adjuvant chemoradiotherapy response prediction using MRI based ensemble learning method in rectal cancer patients
    Shayesteh, Sajad P.
    Alikhassi, Afsaneh
    Esfahani, Armaghan Fard
    Miraie, M.
    Geramifar, Parham
    Bitarafan-rajabi, Ahmad
    Haddad, Peiman
    [J]. 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
    Shi, Liming
    Zhang, Yang
    Nie, Ke
    Sun, Xiaonan
    Niu, Tianye
    Yue, Ning
    Kwong, Tiffany
    Chang, Peter
    Chow, Daniel
    Chen, Jeon-Hor
    Su, Min-Ying
    [J]. MAGNETIC RESONANCE IMAGING, 2019, 61 : 33 - 40
  • [74] Histopathological characteristics and artificial intelligence for predicting tumor mutational burden-high colorectal cancer
    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
    [J]. JOURNAL OF GASTROENTEROLOGY, 2021, 56 (06) : 547 - 559
  • [75] Cancer Statistics, 2021
    Siegel, Rebecca L.
    Miller, Kimberly D.
    Fuchs, Hannah E.
    Jemal, Ahmedin
    [J]. 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
    Song, Bowen
    Zhang, Guopeng
    Lu, Hongbing
    Wang, Huafeng
    Zhu, Wei
    Pickhardt, Perry J.
    Liang, Zhengrong
    [J]. 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
    Song, Cai Li
    Vardaki, Martha Z.
    Goldin, Robert D.
    Kazarian, Sergei G.
    [J]. ANALYTICAL AND BIOANALYTICAL CHEMISTRY, 2019, 411 (26) : 6969 - 6981
  • [79] Robotic proctectomy for rectal cancer: analysis of 71 patients from a single institution
    Spanheimer, Philip M.
    Armstrong, John G.
    Fu, Sunyang
    Liao, Junlin
    Regenbogen, Scott E.
    Byrn, John C.
    [J]. INTERNATIONAL JOURNAL OF MEDICAL ROBOTICS AND COMPUTER ASSISTED SURGERY, 2017, 13 (04)
  • [80] Computer Aided Diagnosis for Confocal Laser Endomicroscopy in Advanced Colorectal Adenocarcinoma
    Stefanescu, Daniela
    Streba, Costin
    Cartana, Elena Tatiana
    Saftoiu, Adrian
    Gruionu, Gabriel
    Gruionu, Lucian Gheorghe
    [J]. PLOS ONE, 2016, 11 (05):
234567891011