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Cited by in CrossRef
For: Du KP, Huang WP, Liu SY, Chen YJ, Li LM, Liu XN, Han YJ, Zhou Y, Liu CC, Gao JB. Application of computed tomography-based radiomics in differential diagnosis of adenocarcinoma and squamous cell carcinoma at the esophagogastric junction. World J Gastroenterol 2022; 28(31): 4363-4375 [PMID: 36159013 DOI: 10.3748/wjg.v28.i31.4363]
URL: https://www.wjgnet.com/1007-9327/full/v28/i31/4363.htm
Number Citing Articles
1
俊超 袁. The Diagnosis and Treatment Status of Carcinoma of the Esophagogastric JunctionAdvances in Clinical Medicine 2024; 14(04): 623 doi: 10.12677/acm.2024.1441069
2
Pak Kin Wong, In Neng Chan, Hao-Ming Yan, Shan Gao, Chi Hong Wong, Tao Yan, Liang Yao, Ying Hu, Zhong-Ren Wang, Hon Ho Yu. Deep learning based radiomics for gastrointestinal cancer diagnosis and treatment: A minireviewWorld Journal of Gastroenterology 2022; 28(45): 6363-6379 doi: 10.3748/wjg.v28.i45.6363
3
Ping Wang, Kaige Chen, Ying Han, Min Zhao, Nanding Abiyasi, Haiyong Peng, Shaolei Yan, Jiming Shang, Naijian Shang, Wei Meng. Prediction model based on radiomics and clinical features for preoperative lymphovascular invasion in gastric cancer patientsFuture Oncology 2023; 19(23): 1613 doi: 10.2217/fon-2022-1025
4
Jinling Yi, Yibo Wu, Boda Ning, Ji Zhang, Maksim Pleshkov, Ivan Tolmachev, Xiance Jin. The application of machine learning and deep learning radiomics in the treatment of esophageal cancerRadiation Medicine and Protection 2023; 4(4): 182 doi: 10.1016/j.radmp.2023.10.009
5
Yang Li, Li Yang, Xiaolong Gu, Qi Wang, Gaofeng Shi, Andu Zhang, Meng Yue, Mingbo Wang, Jialiang Ren. Computed tomography radiomics identification of T1–2 and T3–4 stages of esophageal squamous cell carcinoma: two-dimensional or three-dimensional?Abdominal Radiology 2023; 49(1): 288 doi: 10.1007/s00261-023-04070-1
6
Liangsen Liu, Hai Liao, Yang Zhao, Jiayu Yin, Chen Wang, Lixia Duan, Peihan Xie, Wupeng Wei, Meihai Xu, Danke Su. CT-based radiomics for predicting lymph node metastasis in esophageal cancer: a systematic review and meta-analysisFrontiers in Oncology 2024; 14 doi: 10.3389/fonc.2024.1267596
7
Wei Wei, Shigeng Wang, Mengting Hu, Xiaoyu Tong, Yong Fan, Jingyi Zhang, Qiye Cheng, Deshuo Dong, Lei Liu. Impact of multi-parameter images obtained from dual-energy CT on radiomics to predict pathological grading of bladder urothelial carcinomaAbdominal Radiology 2024; 49(12): 4324 doi: 10.1007/s00261-024-04516-0
8
Ming Cheng, Hanyue Zhang, Wenpeng Huang, Fei Li, Jianbo Gao. Deep Learning Radiomics Analysis of CT Imaging for Differentiating Between Crohn’s Disease and Intestinal TuberculosisJournal of Imaging Informatics in Medicine 2024; 37(4): 1516 doi: 10.1007/s10278-024-01059-0
9
Giovanni Maria Garbarino, Michela Polici, Damiano Caruso, Andrea Laghi, Paolo Mercantini, Emanuela Pilozzi, Mark I. van Berge Henegouwen, Suzanne S. Gisbertz, Nicole C. T. van Grieken, Eva Berardi, Gianluca Costa. Radiomics in Oesogastric Cancer: Staging and Prediction of Preoperative Treatment Response: A Narrative Review and the Results of Personal ExperienceCancers 2024; 16(15): 2664 doi: 10.3390/cancers16152664
10
Ricarda Hinzpeter, Seyed Ali Mirshahvalad, Vanessa Murad, Lisa Avery, Roshini Kulanthaivelu, Andres Kohan, Claudia Ortega, Elena Elimova, Jonathan Yeung, Andrew Hope, Ur Metser, Patrick Veit-Haibach. The [18F]F-FDG PET/CT Radiomics Classifier of Histologic Subtypes and Anatomical Disease Origins across Various Malignancies: A Proof-of-Principle StudyCancers 2024; 16(10): 1873 doi: 10.3390/cancers16101873
11
Ping-Fan Jia, Yu-Ru Li, Lu-Yao Wang, Xiao-Rui Lu, Xing Guo. Radiomics in esophagogastric junction cancer: A scoping review of current status and advancesEuropean Journal of Radiology 2024; 177: 111577 doi: 10.1016/j.ejrad.2024.111577