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©The Author(s) 2025.
World J Gastrointest Oncol. May 15, 2025; 17(5): 103667
Published online May 15, 2025. doi: 10.4251/wjgo.v17.i5.103667
Published online May 15, 2025. doi: 10.4251/wjgo.v17.i5.103667
Table 1 Top ten countries in terms of number of publications
Rank | Country | Documents |
1 | China | 371 |
2 | United States | 265 |
3 | Japan | 155 |
4 | South Korea | 121 |
5 | United Kingdom | 101 |
6 | Germany | 96 |
7 | Italy | 71 |
8 | Spain | 52 |
9 | India | 49 |
10 | Netherlands | 45 |
Table 2 Top ten institutions in terms of number of publications
Rank | Institution | Country | Count | Centrality | Proportion (%) |
1 | Sun Yat-sen University | China | 32 | 0.1 | 2.5 |
2 | Chinese Academy of Sciences | China | 20 | 0.06 | 1.5 |
3 | Harvard Medical School | America | 20 | 0.12 | 1.5 |
4 | Seoul National University | Korea | 20 | 0.01 | 1.5 |
5 | Zhejiang University | China | 19 | 0.03 | 1.4 |
6 | Catholic University of Korea | Korea | 18 | 0.08 | 1.4 |
7 | Southern Medical University | China | 18 | 0.01 | 1.4 |
8 | German Cancer Research Center | Germany | 17 | 0.06 | 1.3 |
9 | National Cancer Centre | Japan | 16 | 0.00 | 1.2 |
10 | Rheinisch-Westfälische Technische Hochschule Aachen | Germany | 16 | 0.02 | 1.2 |
Table 3 Top ten journals and co-cited journals
Rank | Journals | Counts | Citations | IF | JCR | Co-cited journals | Co-citations | IF | JCR |
1 | Scientific Reports | 34 | 1212 | 3.8 | Q1 | Gastrointestinal Endoscopy | 1053 | 6.7 | Q1 |
2 | IEEE Access | 31 | 643 | 3.4 | Q2 | Scientific Reports | 969 | 3.8 | Q1 |
3 | Frontiers in Oncology | 29 | 254 | 3.5 | Q2 | Proceedings of the IEEE | 946 | 23.2 | Q1 |
4 | Cancers | 28 | 453 | 4.5 | Q1 | Gastroenterology | 923 | 25.7 | Q1 |
5 | Diagnostics | 27 | 267 | 3.0 | Q1 | Lecture Notes in Computer Science | 886 | / | / |
6 | World Journal of Gastroenterology | 22 | 454 | 4.3 | Q1 | Journal of Clinical Oncology | 762 | 42.1 | Q1 |
7 | Medical Image Analysis | 16 | 927 | 10.7 | Q1 | New England Journal of Medicine | 689 | 96.2 | Q1 |
8 | Applied Sciences-Basel | 15 | 187 | 2.5 | Q1 | IEEE Transactions on Medical Imaging | 671 | 8.9 | Q1 |
9 | Computers in Biology and Medicine | 15 | 516 | 7.0 | Q1 | Gut | 633 | 23.0 | Q1 |
10 | International Journal of Colorectal Disease | 13 | 418 | 2.5 | Q1 | Endoscopy | 621 | 11.5 | Q1 |
Table 4 Top ten journal publishers
Rank | Journal publishers | Counts |
1 | Elsevier | 239 |
2 | Springer Nature | 239 |
3 | MDPI | 135 |
4 | Wiley Inter Science | 99 |
5 | IEEE | 68 |
6 | Frontiers Media S.A. | 51 |
7 | Nature Portfolio | 51 |
8 | LWW | 37 |
9 | Baishideng Publishing Group Inc | 29 |
10 | Taylor & Francis | 24 |
Table 5 Top ten authors and co-cited authors in terms of number of publications and citations
Rank | Author | Country | Documents | Co-cited author | Frequency |
1 | Kather JN | Germany | 12 | Kather JN | 287 |
2 | Lee SH | Korea | 9 | Bernal J | 198 |
3 | Liu Z | China | 9 | He KM | 196 |
4 | Pickhardt PJ | America | 9 | Wang P | 193 |
5 | Brenner H | Germany | 8 | Jha D | 162 |
6 | Brinker TJ | Germany | 8 | Jemal A | 134 |
7 | Hoffmeister M | Germany | 8 | Ronneberger O | 130 |
8 | Aaito Y | Japan | 8 | Siegel RL | 130 |
9 | Zhao K | China | 8 | Szegedy C | 130 |
10 | Ishihara S | Japan | 7 | Mori Y | 123 |
Table 6 Top ten most cited references
Rank | Title | Journal | First author | Year | Citations |
1 | Deep residual learning for image recognition | 2016 IEEE Conference on Computer Vision and Pattern Recognition | Kaiming He | 2016 | 149 |
2 | U-Net: Convolutional networks for biomedical image segmentation | Medical Image Computing and Computer | Olaf Ronneberger | 2015 | 124 |
3 | Global cancer statistics | CA: A Cancer Journal for Clinicians | Ahmedin Jemal DVM | 2011 | 117 |
4 | Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study | PLoS Medicine | Jakob Nikolas Kather | 2019 | 87 |
5 | Very deep convolutional networks for large-scale image recognition | Computer Science | Karen Simonyan | 2015 | 87 |
6 | WM-DOVA maps for accurate polyp highlighting in colonoscopy: Validation vs saliency maps from physicians | Computerized Medical Imaging and Graphics | Jorge Bernal | 2015 | 83 |
7 | ImageNet classification with deep convolutional neural networks | Research-Article | Alex Krizhevsky | 2017 | 81 |
8 | Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer | Brief Communication | Jakob Nikolas Kather | 2019 | 79 |
9 | Deep learning localizes and identifies polyps in real time with 96% accuracy in screening colonoscopy | Gastroenterology | Gregor Urban | 2018 | 75 |
10 | Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries | CA: A Cancer Journal for Clinicians | Hyuna Sung | 2021 | 72 |
Table 7 Top ten high-frequency keywords
Rank | Keywords | Counts |
1 | Deep learning | 354 |
2 | Colorectal-cancer | 297 |
3 | Colorectal cancer | 293 |
4 | Cancer | 172 |
5 | Classification | 147 |
6 | Artificial intelligence | 144 |
7 | Colonoscopy | 136 |
8 | Survival | 121 |
9 | Risk | 106 |
10 | Diagnosis | 98 |
- Citation: Qi LY, Li BW, Chen JQ, Bian HP, Xue JN, Zhao HX. Research status and trends of deep learning in colorectal cancer (2011-2023): Bibliometric analysis and visualization. World J Gastrointest Oncol 2025; 17(5): 103667
- URL: https://www.wjgnet.com/1948-5204/full/v17/i5/103667.htm
- DOI: https://dx.doi.org/10.4251/wjgo.v17.i5.103667