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Cited by in CrossRef
For: Que SJ, Chen QY, Qing-Zhong, Liu ZY, Wang JB, Lin JX, Lu J, Cao LL, Lin M, Tu RH, Huang ZN, Lin JL, Zheng HL, Li P, Zheng CH, Huang CM, Xie JW. Application of preoperative artificial neural network based on blood biomarkers and clinicopathological parameters for predicting long-term survival of patients with gastric cancer. World J Gastroenterol 2019; 25(43): 6451-6464 [PMID: 31798281 DOI: 10.3748/wjg.v25.i43.6451]
URL: https://www.wjgnet.com/1007-9327/full/v25/i43/6451.htm
Number Citing Articles
1
Beat Müller-Stich, M. Wagner, A. Schulze, S. Bodenstedt, L. Maier-Hein, S. Speidel, F. Nickel, M. W. Büchler. „Cognition-Guided Surgery“ – computergestützte intelligente Assistenzsysteme für die onkologische ChirurgieForum 2022; 37(1): 32 doi: 10.1007/s12312-021-01040-w
2
M. Wagner, A. Schulze, S. Bodenstedt, L. Maier-Hein, S. Speidel, F. Nickel, F. Berlth, B. P. Müller-Stich, Peter Grimminger. Technische Innovationen und Blick in die ZukunftDer Chirurg 2022; 93(3): 217 doi: 10.1007/s00104-021-01569-5
3
Wafae Abbaoui, Sara Retal, Nassim Kharmoum, Soumia Ziti. International Conference on Advanced Intelligent Systems for Sustainable DevelopmentLecture Notes in Networks and Systems 2023; 713: 91 doi: 10.1007/978-3-031-35248-5_9
4
Beibei Hu, Guohui Yin, Xuren Sun. Identification of specific role of SNX family in gastric cancer prognosis evaluationScientific Reports 2022; 12(1) doi: 10.1038/s41598-022-14266-y
5
Naoki Kuwayama, Isamu Hoshino, Yasukuni Mori, Hajime Yokota, Yosuke Iwatate, Takashi Uno. Applying artificial intelligence using routine clinical data for preoperative diagnosis and prognosis evaluation of gastric cancerOncology Letters 2023; 26(5) doi: 10.3892/ol.2023.14087
6
Stefan Patrascu, Georgiana-Maria Cotofana-Graure, Valeriu Surlin, George Mitroi, Mircea-Sebastian Serbanescu, Cristiana Geormaneanu, Ionela Rotaru, Ana-Maria Patrascu, Costel Marian Ionascu, Sergiu Cazacu, Victor Dan Eugen Strambu, Radu Petru. Preoperative Immunocite-Derived Ratios Predict Surgical Complications Better when Artificial Neural Networks Are Used for Analysis—A Pilot Comparative StudyJournal of Personalized Medicine 2023; 13(1): 101 doi: 10.3390/jpm13010101
7
Wafae Abbaoui, Sara Retal, Brahim El Bhiri, Nassim Kharmoum, Soumia Ziti. Towards revolutionizing precision healthcare: A systematic literature review of artificial intelligence methods in precision medicineInformatics in Medicine Unlocked 2024; 46: 101475 doi: 10.1016/j.imu.2024.101475
8
Marianne Linley L. Sy-Janairo, Jose Isagani B. Janairo. Non-endoscopic Applications of Machine Learning in Gastric Cancer: A Systematic ReviewJournal of Gastrointestinal Cancer 2024; 55(1): 47 doi: 10.1007/s12029-023-00960-1
9
Michihiro Kudou, Toshiyuki Kosuga, Eigo Otsuji. Artificial intelligence in gastrointestinal cancer: Recent advances and future perspectivesArtificial Intelligence in Gastroenterology 2020; 1(4): 71-85 doi: 10.35712/aig.v1.i4.71
10
Bo Cao, Ke-Cheng Zhang, Bo Wei, Lin Chen. Status quo and future prospects of artificial neural network from the perspective of gastroenterologistsWorld Journal of Gastroenterology 2021; 27(21): 2681-2709 doi: 10.3748/wjg.v27.i21.2681
11
Beat Müller-Stich, Martin Wagner, André Schulze, Sebastian Bodenstedt, Lena Maier-Hein, Stefanie Speidel, Felix Nickel, Markus W. Büchler. „Cognition-Guided Surgery“ – computergestützte intelligente Assistenzsysteme für die onkologische ChirurgieWiener klinisches Magazin 2022; 25(3): 110 doi: 10.1007/s00740-022-00447-y
12
Gokuldas (Vedant) Sarvesh Raikar, Amisha Sarvesh Raikar, Sandesh Narayan Somnache. Advancements in artificial intelligence and machine learning in revolutionising biomarker discoveryBrazilian Journal of Pharmaceutical Sciences 2023; 59 doi: 10.1590/s2175-97902023e23146
13
Paul T Kröner, Megan ML Engels, Benjamin S Glicksberg, Kipp W Johnson, Obaie Mzaik, Jeanin E van Hooft, Michael B Wallace, Hashem B El-Serag, Chayakrit Krittanawong. Artificial intelligence in gastroenterology: A state-of-the-art reviewWorld Journal of Gastroenterology 2021; 27(40): 6794-6824 doi: 10.3748/wjg.v27.i40.6794
14
Ayrton Bangolo, Nikita Wadhwani, Vignesh K Nagesh, Shraboni Dey, Hadrian Hoang-Vu Tran, Izage Kianifar Aguilar, Auda Auda, Aman Sidiqui, Aiswarya Menon, Deborah Daoud, James Liu, Sai Priyanka Pulipaka, Blessy George, Flor Furman, Nareeman Khan, Adewale Plumptre, Imranjot Sekhon, Abraham Lo, Simcha Weissman. Impact of artificial intelligence in the management of esophageal, gastric and colorectal malignanciesArtificial Intelligence in Gastrointestinal Endoscopy 2024; 5(2): 90704 doi: 10.37126/aige.v5.i2.90704
15
Xiaoyuan Ma, Eric Pierce, Harsh Anand, Natalie Aviles, Paul Kunk, Negin Alemazkoor. Early prediction of response to palliative chemotherapy in patients with stage-IV gastric and esophageal cancerBMC Cancer 2023; 23(1) doi: 10.1186/s12885-023-11422-z
16
Kuldeep Rajpoot, Muktika Tekade, Bhakti Pawar, Nupur Vasdev, Tanisha Gupta, Rakesh Kumar Tekade. Essentials of Pharmatoxicology in Drug Research, Volume 12023; : 551 doi: 10.1016/B978-0-443-15840-7.00009-9
17
Martin Wagner, André Schulze, Sebastian Bodenstedt, Lena Maier-Hein, Stefanie Speidel, Felix Nickel, Felix Berlth, Beat P. Müller-Stich, Peter Grimminger. Technische Innovationen und Blick in die ZukunftWiener klinisches Magazin 2022; 25(5-6): 194 doi: 10.1007/s00740-022-00468-7
18
Peng Jin, Xiaoyan Ji, Wenzhe Kang, Yang Li, Hao Liu, Fuhai Ma, Shuai Ma, Haitao Hu, Weikun Li, Yantao Tian. Artificial intelligence in gastric cancer: a systematic reviewJournal of Cancer Research and Clinical Oncology 2020; 146(9): 2339 doi: 10.1007/s00432-020-03304-9
19
Hemant Goyal, Syed A. A. Sherazi, Rupinder Mann, Zainab Gandhi, Abhilash Perisetti, Muhammad Aziz, Saurabh Chandan, Jonathan Kopel, Benjamin Tharian, Neil Sharma, Nirav Thosani. Scope of Artificial Intelligence in Gastrointestinal OncologyCancers 2021; 13(21): 5494 doi: 10.3390/cancers13215494
20
Takashi Sakamoto, Tadahiro Goto, Michimasa Fujiogi, Alan Kawarai Lefor. Machine learning in gastrointestinal surgerySurgery Today 2022; 52(7): 995 doi: 10.1007/s00595-021-02380-9
21
Bowen Zhang, Long Cheng, Yuzhen Niu, Aming Wang, Pengyi Zhang, Tiantian Shen, Lili Xi, Dekui Zhang, Shuyan Li. Identification Tool for Gastric Cancer Based on Integration of 33 Clinical Available Blood Indices Through Deep LearningIEEE Access 2022; 10: 106081 doi: 10.1109/ACCESS.2022.3172477
22
Jun Lu, Zhen Xue, Bin-Bin Xu, Dong Wu, Hua-Long Zheng, Jian-Wei Xie, Jia-Bin Wang, Jian-Xian Lin, Qi-Yue Chen, Ping Li, Chang-Ming Huang, Chao-Hui Zheng. Application of an artificial neural network for predicting the potential chemotherapy benefit of patients with gastric cancer after radical surgerySurgery 2022; 171(4): 955 doi: 10.1016/j.surg.2021.08.055
23
Yaning Wang, Zihao Wang, Xiaopeng Guo, Yaning Cao, Hao Xing, Yuekun Wang, Bing Xing, Yu Wang, Yong Yao, Wenbin Ma. Artificial neural network identified a 20‐gene panel in predicting immunotherapy response and survival benefits after anti‐PD1/PD‐L1 treatment in glioblastoma patientsCancer Medicine 2024; 13(9) doi: 10.1002/cam4.7218