Academic Content and Language Evaluation of This Article
CrossCheck and Google Search of This Article
Academic Rules and Norms of This Article
Supplementary Materials of This Article
Citation of this article
Corresponding Author of This Article
Research Domain of This Article
Article-Type of This Article
Open-Access Policy of This Article
Times Cited Counts in Google of This Article
Number of Hits and Downloads for This Article
- Total Article Views (7614)
All Articles published online
|
Featured Article
|
Publishing Process of This Article
|
Aug 21, 2022 (publication date) through Jul 18, 2025
Times Cited of This Article
Journal Information of This Article
Publication Name
World Journal of Gastroenterology
ISSN
1007-9327
Publisher of This Article
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
| 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 Junction. Advances 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 minireview. World Journal of Gastroenterology 2022; 28(45): 6363-6379 doi: 10.3748/wjg.v28.i45.6363
|
| 3 |
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 cancer. Radiation Medicine and Protection 2023; 4(4): 182 doi: 10.1016/j.radmp.2023.10.009
|
| 4 |
Yuan Hong, Hanlin Wang, Qi Zhang, Peng Zhang, Kang Cheng, Guodong Cao, Renquan Zhang, Bo Chen. Machine Learning and Deep Learning Hybrid Approach Based on Muscle Imaging Features for Diagnosis of Esophageal Cancer. Diagnostics 2025; 15(14): 1730 doi: 10.3390/diagnostics15141730
|
| 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 |
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 carcinoma. Abdominal Radiology 2024; 49(12): 4324 doi: 10.1007/s00261-024-04516-0
|
| 7 |
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 Experience. Cancers 2024; 16(15): 2664 doi: 10.3390/cancers16152664
|
| 8 |
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 patients. Future Oncology 2023; 19(23): 1613 doi: 10.2217/fon-2022-1025
|
| 9 |
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-analysis. Frontiers in Oncology 2024; 14 doi: 10.3389/fonc.2024.1267596
|
| 10 |
Muna Alsallal, Hanan Hassan Ahmed, Radhwan Abdul Kareem, Anupam Yadav, Subbulakshmi Ganesan, Aman Shankhyan, Sofia Gupta, Kamal Kant Joshi, Hayder Naji Sameer, Ahmed Yaseen, Zainab H. Athab, Mohaned Adil, Bagher Farhood. A novel framework for esophageal cancer grading: combining CT imaging, radiomics, reproducibility, and deep learning insights. BMC Gastroenterology 2025; 25(1) doi: 10.1186/s12876-025-03952-6
|
| 11 |
Yousif Widaatalla, Tom Wolswijk, Muhammad Danial Khan, Iva Halilaj, Klara Mosterd, Henry C. Woodruff, Philippe Lambin. Radiomics in Dermatological Optical Coherence Tomography (OCT): Feature Repeatability, Reproducibility, and Integration into Diagnostic Models in a Prospective Study. Cancers 2025; 17(5): 768 doi: 10.3390/cancers17050768
|
| 12 |
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 Tuberculosis. Journal of Imaging Informatics in Medicine 2024; 37(4): 1516 doi: 10.1007/s10278-024-01059-0
|
| 13 |
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 Study. Cancers 2024; 16(10): 1873 doi: 10.3390/cancers16101873
|
| 14 |
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 advances. European Journal of Radiology 2024; 177: 111577 doi: 10.1016/j.ejrad.2024.111577
|