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Cited by in CrossRef
For: Wong PK, Chan IN, Yan HM, Gao S, Wong CH, Yan T, Yao L, Hu Y, Wang ZR, Yu HH. Deep learning based radiomics for gastrointestinal cancer diagnosis and treatment: A minireview. World J Gastroenterol 2022; 28(45): 6363-6379 [PMID: 36533112 DOI: 10.3748/wjg.v28.i45.6363]
URL: https://www.wjgnet.com/1948-5190/full/v28/i45/6363.htm
Number Citing Articles
1
Chong Lin, Yi Guo, Xu Huang, Shengxiang Rao, Jianjun Zhou. Esophageal cancer detection via non-contrast CT and deep learningFrontiers in Medicine 2024; 11 doi: 10.3389/fmed.2024.1356752
2
Santiago Cepeda. Computational NeurosurgeryAdvances in Experimental Medicine and Biology 2024; 1462: 231 doi: 10.1007/978-3-031-64892-2_14
3
Zeyu Fan, Ziju He, Wenjun Miao, Rongrong Huang. Critical Analysis of Risk Factors and Machine-Learning-Based Gastric Cancer Risk Prediction Models: A Systematic ReviewProcesses 2023; 11(8): 2324 doi: 10.3390/pr11082324
4
Konstantinos Vrettos, Matthaios Triantafyllou, Kostas Marias, Apostolos H Karantanas, Michail E Klontzas. Artificial intelligence-driven radiomics: developing valuable radiomics signatures with the use of artificial intelligenceBJR|Artificial Intelligence 2024; 1(1) doi: 10.1093/bjrai/ubae011
5
Katarzyna Wisniewska, Ervin Marku, Martina Vidova Ugurbas, Ilona Hartmane, Malika Shukurova. Innovations in cancer diagnosis and treatment: prospects and challengesHealthcare in Low-resource Settings 2024;  doi: 10.4081/hls.2024.12831
6
Chenxi Wei, Taiyan Jiang, Kai Wang, Xiaoran Gao, Hao Zhang, Xing Wang. GEP-NETs radiomics in action: a systematical review of applications and quality assessmentClinical and Translational Imaging 2024; 12(3): 287 doi: 10.1007/s40336-024-00617-4
7
Zhiqiang Wang, Weiran Li, Di Jin, Bing Fan. Radiomics in the Diagnosis of Gastric Cancer: Current Status and Future PerspectivesCurrent Medical Imaging Reviews 2023; 20(1) doi: 10.2174/0115734056246452231011042418
8
Ali S. Alyami. The Role of Radiomics in Fibrosis Crohn’s Disease: A ReviewDiagnostics 2023; 13(9): 1623 doi: 10.3390/diagnostics13091623
9
Tao Yan, Ye Ying Qin, Pak Kin Wong, Hao Ren, Chi Hong Wong, Liang Yao, Ying Hu, Cheok I Chan, Shan Gao, Pui Pun Chan. Semantic Segmentation of Gastric Polyps in Endoscopic Images Based on Convolutional Neural Networks and an Integrated Evaluation ApproachBioengineering 2023; 10(7): 806 doi: 10.3390/bioengineering10070806
10
Wenjing Jia, Fuyan Li, Yi Cui, Yong Wang, Zhengjun Dai, Qingqing Yan, Xinhui Liu, Yuting Li, Huan Chang, Qingshi Zeng. Deep Learning Radiomics Model of Contrast-Enhanced CT for Differentiating the Primary Source of Liver MetastasesAcademic Radiology 2024; 31(10): 4057 doi: 10.1016/j.acra.2024.04.012
11
Maria Chiara Brunese, Maria Rita Fantozzi, Roberta Fusco, Federica De Muzio, Michela Gabelloni, Ginevra Danti, Alessandra Borgheresi, Pierpaolo Palumbo, Federico Bruno, Nicoletta Gandolfo, Andrea Giovagnoni, Vittorio Miele, Antonio Barile, Vincenza Granata. Update on the Applications of Radiomics in Diagnosis, Staging, and Recurrence of Intrahepatic CholangiocarcinomaDiagnostics 2023; 13(8): 1488 doi: 10.3390/diagnostics13081488
12
An-du Zhang, Qing-lei Shi, Hong-tao Zhang, Wen-han Duan, Yang Li, Li Ruan, Yi-fan Han, Zhi-kun Liu, Hao-feng Li, Jia-shun Xiao, Gao-feng Shi, Xiang Wan, Ren-zhi Wang. Pairwise machine learning-based automatic diagnostic platform utilizing CT images and clinical information for predicting radiotherapy locoregional recurrence in elderly esophageal cancer patientsAbdominal Radiology 2024; 49(11): 4151 doi: 10.1007/s00261-024-04377-7