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Cited by in CrossRef
For: Jang HJ, Lee A, Kang J, Song IH, Lee SH. Prediction of clinically actionable genetic alterations from colorectal cancer histopathology images using deep learning. World J Gastroenterol 2020; 26(40): 6207-6223 [PMID: 33177794 DOI: 10.3748/wjg.v26.i40.6207]
URL: https://www.wjgnet.com/1007-9327/full/v26/i40/6207.htm
Number Citing Articles
1
Linyan Chen, Hao Zeng, Yu Xiang, Yeqian Huang, Yuling Luo, Xuelei Ma. Histopathological Images and Multi-Omics Integration Predict Molecular Characteristics and Survival in Lung AdenocarcinomaFrontiers in Cell and Developmental Biology 2021; 9 doi: 10.3389/fcell.2021.720110
2
Amelie Echle, Narmin Ghaffari Laleh, Peter L. Schrammen, Nicholas P. West, Christian Trautwein, Titus J. Brinker, Stephen B. Gruber, Roman D. Buelow, Peter Boor, Heike I. Grabsch, Philip Quirke, Jakob N. Kather. Deep learning for the detection of microsatellite instability from histology images in colorectal cancer: A systematic literature reviewImmunoInformatics 2021; : 100008 doi: 10.1016/j.immuno.2021.100008
3
Huu-Giao Nguyen, Oxana Lundström, Annika Blank, Heather Dawson, Alessandro Lugli, Maria Anisimova, Inti Zlobec. Image-based assessment of extracellular mucin-to-tumor area predicts consensus molecular subtypes (CMS) in colorectal cancerModern Pathology 2022; 35(2): 240 doi: 10.1038/s41379-021-00894-8
4
Jia-Sheng Cao, Zi-Yi Lu, Ming-Yu Chen, Bin Zhang, Sarun Juengpanich, Jia-Hao Hu, Shi-Jie Li, Win Topatana, Xue-Yin Zhou, Xu Feng, Ji-Liang Shen, Yu Liu, Xiu-Jun Cai. Artificial intelligence in gastroenterology and hepatology: Status and challengesWorld Journal of Gastroenterology 2021; 27(16): 1664-1690 doi: 10.3748/wjg.v27.i16.1664
5
Sung Hak Lee, In Hye Song, Hyun‐Jong Jang. Feasibility of deep learning‐based fully automated classification of microsatellite instability in tissue slides of colorectal cancerInternational Journal of Cancer 2021; 149(3): 728 doi: 10.1002/ijc.33599
6
Hyun-Jong Jang, Ahwon Lee, Jun Kang, In Hye Song, Sung Hak Lee. Prediction of genetic alterations from gastric cancer histopathology images using a fully automated deep learning approachWorld Journal of Gastroenterology 2021; 27(44): 7687-7704 doi: 10.3748/wjg.v27.i44.7687
7
Hyun-Jong Jang, In Hye Song, Sung Hak Lee. Generalizability of Deep Learning System for the Pathologic Diagnosis of Various CancersApplied Sciences 2021; 11(2): 808 doi: 10.3390/app11020808
8
Yulan Ma, Jiawen Wang, Kai Song, Yan Qiang, Xiong Jiao, Juanjuan Zhao. Spatial-Frequency dual-branch attention model for determining KRAS mutation status in colorectal cancer with T2-weighted MRIComputer Methods and Programs in Biomedicine 2021; 209: 106311 doi: 10.1016/j.cmpb.2021.106311
9
Hyun-Jong Jang, In-Hye Song, Sung-Hak Lee. Deep Learning for Automatic Subclassification of Gastric Carcinoma Using Whole-Slide Histopathology ImagesCancers 2021; 13(15): 3811 doi: 10.3390/cancers13153811
10
Pierre Murchan, Cathal Ó’Brien, Shane O’Connell, Ciara S. McNevin, Anne-Marie Baird, Orla Sheils, Pilib Ó Broin, Stephen P. Finn. Deep Learning of Histopathological Features for the Prediction of Tumour Molecular GeneticsDiagnostics 2021; 11(8): 1406 doi: 10.3390/diagnostics11081406
11
Mamdouh M. Shawki, Mohamed Moustafa Azmy, Mohammed Salama, Sanaa Shawki. Mathematical and deep learning analysis based on tissue dielectric properties at low frequencies predict outcome in human breast cancerTechnology and Health Care 2021; : 1 doi: 10.3233/THC-213096
12
Omer A.M. Saeed, Steven A. Mann, Claudio Luchini, Kun Huang, Shaobo Zhang, Joyashree D. Sen, Maria L. Piredda, Mingsheng Wang, Lee Ann Baldrige, R. Matthew Sperling, Kendra L. Curless, Liang Cheng. Evaluating mismatch repair deficiency for solid tumor immunotherapy eligibility: immunohistochemistry versus microsatellite molecular testingHuman Pathology 2021; 115: 10 doi: 10.1016/j.humpath.2021.05.009