Application of artificial intelligence in diagnosis and treatment of colorectal cancer: A novel Prospect

被引:44
作者
Yin, Zugang [1 ]
Yao, Chenhui [1 ]
Zhang, Limin [2 ]
Qi, Shaohua [3 ,4 ]
机构
[1] Dalian Med Univ, Affiliated Hosp 1, Dept Gen Surg, Dalian, Peoples R China
[2] Dalian Med Univ, Affiliated Hosp 1, Dept Resp, Dalian, Peoples R China
[3] Chinese Acad Med Sci, Inst Lab Anim Sci, Beijing, Peoples R China
[4] Peking Union Med Coll, Comparat Med Ctr, Beijing, Peoples R China
关键词
artificial intelligence; colorectal cancer; machine learning; deep learning; bioinformatics analysis; screening; diagnosis; therapy; COMPUTER-AIDED DETECTION; CT COLONOGRAPHY; NEURAL-NETWORK; COLONOSCOPY; TECHNOLOGY; PREDICTION; PROGNOSIS; SURGERY; LIGHT;
D O I
10.3389/fmed.2023.1128084
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
In the past few decades, according to the rapid development of information technology, artificial intelligence (AI) has also made significant progress in the medical field. Colorectal cancer (CRC) is the third most diagnosed cancer worldwide, and its incidence and mortality rates are increasing yearly, especially in developing countries. This article reviews the latest progress in AI in diagnosing and treating CRC based on a systematic collection of previous literature. Most CRCs transform from polyp mutations. The computer-aided detection systems can significantly improve the polyp and adenoma detection rate by early colonoscopy screening, thereby lowering the possibility of mutating into CRC. Machine learning and bioinformatics analysis can help screen and identify more CRC biomarkers to provide the basis for non-invasive screening. The Convolutional neural networks can assist in reading histopathologic tissue images, reducing the experience difference among doctors. Various studies have shown that AI-based high-level auxiliary diagnostic systems can significantly improve the readability of medical images and help clinicians make more accurate diagnostic and therapeutic decisions. Moreover, Robotic surgery systems such as da Vinci have been more and more commonly used to treat CRC patients, according to their precise operating performance. The application of AI in neoadjuvant chemoradiotherapy has further improved the treatment and efficacy evaluation of CRC. In addition, AI represented by deep learning in gene sequencing research offers a new treatment option. All of these things have seen that AI has a promising prospect in the era of precision medicine.
引用
收藏
页数:14
相关论文
共 157 条
[51]   Deep transfer learning based model for colorectal cancer histopathology segmentation: A comparative study of deep pre-trained models [J].
Kassani, Sara Hosseinzadeh ;
Kassani, Peyman Hosseinzadeh ;
Wesolowski, Michal J. ;
Schneider, Kevin A. ;
Deters, Ralph .
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS, 2022, 159
[52]   Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study [J].
Kather, Jakob Nikolas ;
Krisam, Johannes ;
Charoentong, Pornpimol ;
Luedde, Tom ;
Herpel, Esther ;
Weis, Cleo-Aron ;
Gaiser, Timo ;
Marx, Alexander ;
Valous, Nektarios A. ;
Ferber, Dyke ;
Jansen, Lina ;
Reyes-Aldasoro, Constantino Carlos ;
Zoernig, Inka ;
Jaeger, Dirk ;
Brenner, Hermann ;
Chang-Claude, Jenny ;
Hoffmeister, Michael ;
Halama, Niels .
PLOS MEDICINE, 2019, 16 (01)
[53]   History of artificial intelligence in medicine [J].
Kaul, Vivek ;
Enslin, Sarah ;
Gross, Seth A. .
GASTROINTESTINAL ENDOSCOPY, 2020, 92 (04) :807-812
[54]   Walking pathways with positive feedback loops reveal DNA methylation biomarkers of colorectal cancer [J].
Kel, Alexander ;
Boyarskikh, Ulyana ;
Stegmaier, Philip ;
Leskov, Leonid S. ;
Sokolov, Andrey V. ;
Yevshin, Ivan ;
Mandrik, Nikita ;
Stelmashenko, Daria ;
Koschmann, Jeannette ;
Kel-Margoulis, Olga ;
Krull, Mathias ;
Martinez-Cardus, Anna ;
Moran, Sebastian ;
Esteller, Manel ;
Kolpakov, Fedor ;
Filipenko, Maxim ;
Wingender, Edgar .
BMC BIOINFORMATICS, 2019, 20 (Suppl 4)
[55]   Preoperative evaluation of colorectal cancer using CT colonography, MRI, and PET/CT [J].
Kijima, Shigeyoshi ;
Sasaki, Takahiro ;
Nagata, Koichi ;
Utano, Kenichi ;
Lefor, Alan T. ;
Sugimoto, Hideharu .
WORLD JOURNAL OF GASTROENTEROLOGY, 2014, 20 (45) :16964-16975
[56]   Deep learning-based histopathological segmentation for whole slide images of colorectal cancer in a compressed domain [J].
Kim, Hyeongsub ;
Yoon, Hongjoon ;
Thakur, Nishant ;
Hwang, Gyoyeon ;
Lee, Eun Jung ;
Kim, Chulhong ;
Chong, Yosep .
SCIENTIFIC REPORTS, 2021, 11 (01)
[57]   Diagnostic Performance of Deep Learning-Based Lesion Detection Algorithm in CT for Detecting Hepatic Metastasis from Colorectal Cancer [J].
Kim, Kiwook ;
Kim, Sungwon ;
Han, Kyunghwa ;
Bae, Heejin ;
Shin, Jaeseung ;
Lim, Joon Seok .
KOREAN JOURNAL OF RADIOLOGY, 2021, 22 (06) :912-921
[58]   Robot-assisted Versus Laparoscopic Surgery for Rectal Cancer A Phase II Open Label Prospective Randomized Controlled Trial [J].
Kim, Min Jung ;
Park, Sung Chan ;
Park, Ji Won ;
Chang, Hee Jin ;
Kim, Dae Yong ;
Nam, Byung-Ho ;
Sohn, Dae Kyung ;
Oh, Jae Hwan .
ANNALS OF SURGERY, 2018, 267 (02) :243-251
[59]   Automated laparoscopic colorectal surgery workflow recognition using artificial intelligence: Experimental research [J].
Kitaguchi, Daichi ;
Takeshita, Nobuyoshi ;
Matsuzaki, Hiroki ;
Oda, Tatsuya ;
Watanabe, Masahiko ;
Mori, Kensaku ;
Kobayashi, Etsuko ;
Ito, Masaaki .
INTERNATIONAL JOURNAL OF SURGERY, 2020, 79 :88-94
[60]   Real-time automatic surgical phase recognition in laparoscopic sigmoidectomy using the convolutional neural network-based deep learning approach [J].
Kitaguchi, Daichi ;
Takeshita, Nobuyoshi ;
Matsuzaki, Hiroki ;
Takano, Hiroaki ;
Owada, Yohei ;
Enomoto, Tsuyoshi ;
Oda, Tatsuya ;
Miura, Hirohisa ;
Yamanashi, Takahiro ;
Watanabe, Masahiko ;
Sato, Daisuke ;
Sugomori, Yusuke ;
Hara, Seigo ;
Ito, Masaaki .
SURGICAL ENDOSCOPY AND OTHER INTERVENTIONAL TECHNIQUES, 2020, 34 (11) :4924-4931