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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
Pooria Mazaheri, Azam Asilian Bidgoli, Shahryar Rahnamayan, H.R. Tizhoosh. Ranking loss and sequestering learning for reducing image search bias in histopathologyApplied Soft Computing 2023; 142: 110346 doi: 10.1016/j.asoc.2023.110346
2
Mai Tharwat, Nehal A. Sakr, Shaker El-Sappagh, Hassan Soliman, Kyung-Sup Kwak, Mohammed Elmogy. Colon Cancer Diagnosis Based on Machine Learning and Deep Learning: Modalities and Analysis TechniquesSensors 2022; 22(23): 9250 doi: 10.3390/s22239250
3
Mohammad Rizwan Alam, Kyung Jin Seo, Jamshid Abdul-Ghafar, Kwangil Yim, Sung Hak Lee, Hyun-Jong Jang, Chan Kwon Jung, Yosep Chong. Recent application of artificial intelligence on histopathologic image-based prediction of gene mutation in solid cancersBriefings in Bioinformatics 2023; 24(3) doi: 10.1093/bib/bbad151
4
Hyun-Jong Jang, Jai-Hyang Go, Younghoon Kim, Sung Hak Lee. Deep Learning for the Pathologic Diagnosis of Hepatocellular Carcinoma, Cholangiocarcinoma, and Metastatic Colorectal CancerCancers 2023; 15(22): 5389 doi: 10.3390/cancers15225389
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
Sophia J. Wagner, Christian Matek, Sayedali Shetab Boushehri, Melanie Boxberg, Lorenz Lamm, Ario Sadafi, Dominik J.E. Winter, Carsten Marr, Tingying Peng. Built to Last? Reproducibility and Reusability of Deep Learning Algorithms in Computational PathologyModern Pathology 2024; 37(1): 100350 doi: 10.1016/j.modpat.2023.100350
7
Camilla Nero, Luca Boldrini, Jacopo Lenkowicz, Maria Teresa Giudice, Alessia Piermattei, Frediano Inzani, Tina Pasciuto, Angelo Minucci, Anna Fagotti, Gianfranco Zannoni, Vincenzo Valentini, Giovanni Scambia. Deep-Learning to Predict BRCA Mutation and Survival from Digital H&E Slides of Epithelial Ovarian CancerInternational Journal of Molecular Sciences 2022; 23(19): 11326 doi: 10.3390/ijms231911326
8
José Escorcia-Gutierrez, Margarita Gamarra, Paola Patricia Ariza-Colpas, Gisella Borja Roncallo, Nallig Leal, Roosvel Soto-Diaz, Romany F. Mansour. Galactic swarm optimization with deep transfer learning driven colorectal cancer classification for image guided interventionComputers and Electrical Engineering 2022; 104: 108462 doi: 10.1016/j.compeleceng.2022.108462
9
Louis-Oscar Morel, Valentin Derangère, Laurent Arnould, Sylvain Ladoire, Nathan Vinçon. Preliminary evaluation of deep learning for first-line diagnostic prediction of tumor mutational statusScientific Reports 2023; 13(1) doi: 10.1038/s41598-023-34016-y
10
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
11
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
12
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
13
Peter Schüffler, Katja Steiger, Wilko Weichert. How to use AI in pathologyGenes, Chromosomes and Cancer 2023; 62(9): 564 doi: 10.1002/gcc.23178
14
Theo Guitton, Pierre Allaume, Noémie Rabilloud, Nathalie Rioux-Leclercq, Sébastien Henno, Bruno Turlin, Marie-Dominique Galibert-Anne, Astrid Lièvre, Alexandra Lespagnol, Thierry Pécot, Solène-Florence Kammerer-Jacquet. Artificial Intelligence in Predicting Microsatellite Instability and KRAS, BRAF Mutations from Whole-Slide Images in Colorectal Cancer: A Systematic ReviewDiagnostics 2023; 14(1): 99 doi: 10.3390/diagnostics14010099
15
Hardeep Kaur, Anil Kumar, Varinder Kaur Attri. Innovations in VLSI, Signal Processing and Computational TechnologiesLecture Notes in Electrical Engineering 2024; 1095: 395 doi: 10.1007/978-981-99-7077-3_39
16
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
17
Swati B. Bhonde, Sharmila K. Wagh, Jayashree R. Prasad. Identification of cancer types from gene expressions using learning techniquesComputer Methods in Biomechanics and Biomedical Engineering 2023; 26(16): 1951 doi: 10.1080/10255842.2022.2160243
18
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
19
Pei-Chen Tsai, Tsung-Hua Lee, Kun-Chi Kuo, Fang-Yi Su, Tsung-Lu Michael Lee, Eliana Marostica, Tomotaka Ugai, Melissa Zhao, Mai Chan Lau, Juha P. Väyrynen, Marios Giannakis, Yasutoshi Takashima, Seyed Mousavi Kahaki, Kana Wu, Mingyang Song, Jeffrey A. Meyerhardt, Andrew T. Chan, Jung-Hsien Chiang, Jonathan Nowak, Shuji Ogino, Kun-Hsing Yu. Histopathology images predict multi-omics aberrations and prognoses in colorectal cancer patientsNature Communications 2023; 14(1) doi: 10.1038/s41467-023-37179-4
20
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
21
Jan Moritz Niehues, Philip Quirke, Nicholas P. West, Heike I. Grabsch, Marko van Treeck, Yoni Schirris, Gregory P. Veldhuizen, Gordon G.A. Hutchins, Susan D. Richman, Sebastian Foersch, Titus J. Brinker, Junya Fukuoka, Andrey Bychkov, Wataru Uegami, Daniel Truhn, Hermann Brenner, Alexander Brobeil, Michael Hoffmeister, Jakob Nikolas Kather. Generalizable biomarker prediction from cancer pathology slides with self-supervised deep learning: A retrospective multi-centric studyCell Reports Medicine 2023; 4(4): 100980 doi: 10.1016/j.xcrm.2023.100980
22
Yujie Jing, Chen Li, Tianming Du, Tao Jiang, Hongzan Sun, Jinzhu Yang, Liyu Shi, Minghe Gao, Marcin Grzegorzek, Xiaoyan Li. A comprehensive survey of intestine histopathological image analysis using machine vision approachesComputers in Biology and Medicine 2023; 165: 107388 doi: 10.1016/j.compbiomed.2023.107388
23
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 2022; 30(3): 633 doi: 10.3233/THC-213096
24
Jana Lipkova, Richard J. Chen, Bowen Chen, Ming Y. Lu, Matteo Barbieri, Daniel Shao, Anurag J. Vaidya, Chengkuan Chen, Luoting Zhuang, Drew F.K. Williamson, Muhammad Shaban, Tiffany Y. Chen, Faisal Mahmood. Artificial intelligence for multimodal data integration in oncologyCancer Cell 2022; 40(10): 1095 doi: 10.1016/j.ccell.2022.09.012
25
Neeraj Kumar, Ruchika Verma, Chuheng Chen, Cheng Lu, Pingfu Fu, Joseph Willis, Anant Madabhushi. Computer‐extracted features of nuclear morphology in hematoxylin and eosin images distinguish stage II and IV colon tumorsThe Journal of Pathology 2022; 257(1): 17 doi: 10.1002/path.5864
26
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
27
Shuhei Ishii, Manabu Takamatsu, Hironori Ninomiya, Kentaro Inamura, Takeshi Horai, Akira Iyoda, Naoko Honma, Rira Hoshi, Yuko Sugiyama, Noriko Yanagitani, Mingyon Mun, Hitoshi Abe, Tetuo Mikami, Kengo Takeuchi. Machine learning‐based gene alteration prediction model for primary lung cancer using cytologic imagesCancer Cytopathology 2022; 130(10): 812 doi: 10.1002/cncy.22609
28
Zengxin Liu, Caiwen Ma, Wenji She, Meilin Xie. Biomedical Image Segmentation Using Denoising Diffusion Probabilistic Models: A Comprehensive Review and AnalysisApplied Sciences 2024; 14(2): 632 doi: 10.3390/app14020632
29
Sung Hak Lee, Yujin Lee, Hyun‐Jong Jang. Deep learning captures selective features for discrimination of microsatellite instability from pathologic tissue slides of gastric cancerInternational Journal of Cancer 2023; 152(2): 298 doi: 10.1002/ijc.34251
30
Ming Cai, Ke Zhao, Lin Wu, Yanqi Huang, Minning Zhao, Qingru Hu, Qicong Chen, Su Yao, Zhenhui Li, Xinjuan Fan, Zaiyi Liu. Artificial intelligence-based analysis of tumor-infiltrating lymphocyte spatial distribution for colorectal cancer prognosisChinese Medical Journal 2024;  doi: 10.1097/CM9.0000000000002964
31
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
32
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
33
Sophia J. Wagner, Daniel Reisenbüchler, Nicholas P. West, Jan Moritz Niehues, Jiefu Zhu, Sebastian Foersch, Gregory Patrick Veldhuizen, Philip Quirke, Heike I. Grabsch, Piet A. van den Brandt, Gordon G.A. Hutchins, Susan D. Richman, Tanwei Yuan, Rupert Langer, Josien C.A. Jenniskens, Kelly Offermans, Wolfram Mueller, Richard Gray, Stephen B. Gruber, Joel K. Greenson, Gad Rennert, Joseph D. Bonner, Daniel Schmolze, Jitendra Jonnagaddala, Nicholas J. Hawkins, Robyn L. Ward, Dion Morton, Matthew Seymour, Laura Magill, Marta Nowak, Jennifer Hay, Viktor H. Koelzer, David N. Church, Christian Matek, Carol Geppert, Chaolong Peng, Cheng Zhi, Xiaoming Ouyang, Jacqueline A. James, Maurice B. Loughrey, Manuel Salto-Tellez, Hermann Brenner, Michael Hoffmeister, Daniel Truhn, Julia A. Schnabel, Melanie Boxberg, Tingying Peng, Jakob Nikolas Kather, David Church, Enric Domingo, Joanne Edwards, Bengt Glimelius, Ismail Gogenur, Andrea Harkin, Jen Hay, Timothy Iveson, Emma Jaeger, Caroline Kelly, Rachel Kerr, Noori Maka, Hannah Morgan, Karin Oien, Clare Orange, Claire Palles, Campbell Roxburgh, Owen Sansom, Mark Saunders, Ian Tomlinson. Transformer-based biomarker prediction from colorectal cancer histology: A large-scale multicentric studyCancer Cell 2023; 41(9): 1650 doi: 10.1016/j.ccell.2023.08.002
34
Tobias Schulz, Christoph Becker, Gian Kayser. Ein Vergleich von 4 konvolutionalen neuronalen Netzen in der histopathologischen Diagnostik von SpeicheldrüsenkarzinomenHNO 2023; 71(3): 170 doi: 10.1007/s00106-023-01276-z
35
Taher Dehkharghanian, Azam Asilian Bidgoli, Abtin Riasatian, Pooria Mazaheri, Clinton J. V. Campbell, Liron Pantanowitz, H. R. Tizhoosh, Shahryar Rahnamayan. Biased data, biased AI: deep networks predict the acquisition site of TCGA imagesDiagnostic Pathology 2023; 18(1) doi: 10.1186/s13000-023-01355-3
36
Sandra Orsulic, Joshi John, Ann E. Walts, Arkadiusz Gertych. Computational pathology in ovarian cancerFrontiers in Oncology 2022; 12 doi: 10.3389/fonc.2022.924945
37
Yeojin Jeong, Cristina Eunbee Cho, Ji-Eon Kim, Jonghyun Lee, Namkug Kim, Woon Yong Jung, Joohon Sung, Ju Han Kim, Yoo Jin Lee, Jiyoon Jung, Juyeon Pyo, Jisun Song, Jihwan Park, Kyoung Min Moon, Sangjeong Ahn. Deep learning model to predict Epstein–Barr virus associated gastric cancer in histologyScientific Reports 2022; 12(1) doi: 10.1038/s41598-022-22731-x
38
Matthew G. Hanna, Orly Ardon. Digital pathology systems enabling quality patient careGenes, Chromosomes and Cancer 2023; 62(11): 685 doi: 10.1002/gcc.23192
39
Didem Cifci, Sebastian Foersch, Jakob Nikolas Kather. Artificial intelligence to identify genetic alterations in conventional histopathologyThe Journal of Pathology 2022; 257(4): 430 doi: 10.1002/path.5898
40
Xuejie Li, Xianda Chi, Pinjie Huang, Qiong Liang, Jianpei Liu. Deep neural network for the prediction of KRAS, NRAS, and BRAF genotypes in left-sided colorectal cancer based on histopathologic imagesComputerized Medical Imaging and Graphics 2024; 115: 102384 doi: 10.1016/j.compmedimag.2024.102384
41
Sarah Fremond, Viktor Hendrik Koelzer, Nanda Horeweg, Tjalling Bosse. The evolving role of morphology in endometrial cancer diagnostics: From histopathology and molecular testing towards integrative data analysis by deep learningFrontiers in Oncology 2022; 12 doi: 10.3389/fonc.2022.928977
42
Athena Davri, Effrosyni Birbas, Theofilos Kanavos, Georgios Ntritsos, Nikolaos Giannakeas, Alexandros T. Tzallas, Anna Batistatou. Deep Learning on Histopathological Images for Colorectal Cancer Diagnosis: A Systematic ReviewDiagnostics 2022; 12(4): 837 doi: 10.3390/diagnostics12040837
43
Sung Hak Lee, Hyun-Jong Jang. Deep learning-based prediction of molecular cancer biomarkers from tissue slides: A new tool for precision oncologyClinical and Molecular Hepatology 2022; 28(4): 754 doi: 10.3350/cmh.2021.0394
44
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