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 (4170)
All Articles published online
|
Featured Article
|
Publishing Process of This Article
|
Aug 21, 2022 (publication date) through Apr 27, 2024
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 |
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
|
| 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 cancer. Radiation 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-analysis. Frontiers in Oncology 2024; 14 doi: 10.3389/fonc.2024.1267596
|
| 7 |
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; doi: 10.1007/s10278-024-01059-0
|