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Cited by in CrossRef
For: Zhang YC, Li M, Jin YM, Xu JX, Huang CC, Song B. Radiomics for differentiating tumor deposits from lymph node metastasis in rectal cancer. World J Gastroenterol 2022; 28(29): 3960-3970 [PMID: 36157536 DOI: 10.3748/wjg.v28.i29.3960]
URL: https://www.wjgnet.com/1007-9327/full/v28/i29/3960.htm
Number Citing Articles
1
Özge Vural Topuz, Ayşegül Aksu, Müveddet Banu Yılmaz Özgüven. A different perspective on 18F-FDG PET radiomics in colorectal cancer patients: The relationship between intra & peritumoral analysis and pathological findingsRevista Española de Medicina Nuclear e Imagen Molecular (English Edition) 2023; 42(6): 359 doi: 10.1016/j.remnie.2023.04.005
2
Ö. Vural Topuz, A. Aksu, M.B. Yılmaz Özgüven. Una perspectiva diferente sobre la radiómica con 18F-FDG-PET en pacientes con cáncer colorrectal; la relación entre el análisis intra y peritumoral y los hallazgos patológicosRevista Española de Medicina Nuclear e Imagen Molecular 2023; 42(6): 359 doi: 10.1016/j.remn.2023.04.002
3
Elahe Abbaspour, Sahand Karimzadhagh, Abbas Monsef, Farahnaz Joukar, Fariborz Mansour-Ghanaei, Soheil Hassanipour. Application of radiomics for preoperative prediction of lymph node metastasis in colorectal cancer: a systematic review and meta-analysisInternational Journal of Surgery 2024; 110(6): 3795 doi: 10.1097/JS9.0000000000001239
4
Yong-Xia Ye, Liu Yang, Zheng Kang, Mei-Qin Wang, Xiao-Dong Xie, Ke-Xin Lou, Jun Bao, Mei Du, Zhe-Xuan Li. Magnetic resonance imaging-based lymph node radiomics for predicting the metastasis of evaluable lymph nodes in rectal cancerWorld Journal of Gastrointestinal Oncology 2024; 16(5): 1849-1860 doi: 10.4251/wjgo.v16.i5.1849
5
Yumei Jin, Yewu Wang, Yonghua Zhu, Wenzhi Li, Fengqiong Tang, Shengmei Liu, Bin Song. A nomogram for preoperative differentiation of tumor deposits from lymph node metastasis in rectal cancer: A retrospective studyMedicine 2023; 102(41): e34865 doi: 10.1097/MD.0000000000034865
6
Dawei Wang, Xiao He, Chunming Huang, Wenqiang Li, Haosen Li, Cicheng Huang, Chuanyu Hu. Magnetic resonance imaging-based radiomics and deep learning models for predicting lymph node metastasis of squamous cell carcinoma of the tongueOral Surgery, Oral Medicine, Oral Pathology and Oral Radiology 2024; 138(1): 214 doi: 10.1016/j.oooo.2024.01.016
7
Hui Qu, Huan Zhai, Shuairan Zhang, Wenjuan Chen, Hongshan Zhong, Xiaoyu Cui. Dynamic radiomics for predicting the efficacy of antiangiogenic therapy in colorectal liver metastasesFrontiers in Oncology 2023; 13 doi: 10.3389/fonc.2023.992096
8
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
9
Binbin Han, Fuliang Zhang, Zhenyun Chang, Fang Feng. Optimising Deep Neural Networks for Tumour Diagnosis Algorithms Based on Improved MRFO AlgorithmEAI Endorsed Transactions on Pervasive Health and Technology 2024; 10 doi: 10.4108/eetpht.10.5147
10
Joao Miranda, Natally Horvat, Jose A. B. Araujo-Filho, Kamila S. Albuquerque, Charlotte Charbel, Bruno M. C. Trindade, Daniel L. Cardoso, Lucas de Padua Gomes de Farias, Jayasree Chakraborty, Cesar Higa Nomura. The Role of Radiomics in Rectal CancerJournal of Gastrointestinal Cancer 2023; 54(4): 1158 doi: 10.1007/s12029-022-00909-w
11
Weili Ma, Bo Chen, Fandong Zhu, Chen Yang, Jianfeng Yang. Diagnostic role of F-18 FDG PET/CT in determining preoperative Lymph node status of patients with rectal cancer: a meta-analysisAbdominal Radiology 2024; 49(6): 2125 doi: 10.1007/s00261-023-04140-4