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Apr 15, 2020 (publication date) through Nov 27, 2024
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Publication Name
World Journal of Gastrointestinal Oncology
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1948-5204
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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
| For: | Song BI. Nomogram using F-18 fluorodeoxyglucose positron emission tomography/computed tomography for preoperative prediction of lymph node metastasis in gastric cancer. World J Gastrointest Oncol 2020; 12(4): 447-456 [PMID: 32368322 DOI: 10.4251/wjgo.v12.i4.447] |
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| URL: | https://www.wjgnet.com/1948-5204/full/v12/i4/447.htm |
| Number | Citing Articles |
| 1 |
Yilin Li, Fengjiao Xie, Qin Xiong, Honglin Lei, Peimin Feng. Machine learning for lymph node metastasis prediction of in patients with gastric cancer: A systematic review and meta-analysis. Frontiers in Oncology 2022; 12 doi: 10.3389/fonc.2022.946038
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| 2 |
Xiu-qing Xue, Wen-Ji Yu, Xiao-Liang Shao, Yue-Tao Wang. Incremental value of PET primary lesion-based radiomics signature to conventional metabolic parameters and traditional risk factors for preoperative prediction of lymph node metastases in gastric cancer. Abdominal Radiology 2022; 48(2): 510 doi: 10.1007/s00261-022-03738-4
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| 3 |
Danyu Ma, Ying Zhang, Xiaoliang Shao, Chen Wu, Jun Wu. PET/CT for Predicting Occult Lymph Node Metastasis in Gastric Cancer. Current Oncology 2022; 29(9): 6523 doi: 10.3390/curroncol29090513
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| 4 |
Yan Li, Dong Han, Cong Shen, Xiaoyi Duan. Construction of a comprehensive predictive model for axillary lymph node metastasis in breast cancer: a retrospective study. BMC Cancer 2023; 23(1) doi: 10.1186/s12885-023-11498-7
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| 5 |
Rosie Kwon, Hannah Kim, Keun Soo Ahn, Bong-Il Song, Jinny Lee, Hae Won Kim, Kyoung Sook Won, Hye Won Lee, Tae-Seok Kim, Yonghoon Kim, Koo Jeong Kang. A Machine Learning-Based Clustering Using Radiomics of F-18 Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography for the Prediction of Prognosis in Patients with Intrahepatic Cholangiocarcinoma. Diagnostics 2024; 14(19): 2245 doi: 10.3390/diagnostics14192245
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| 6 |
Bong-Il Song. A machine learning-based radiomics model for the prediction of axillary lymph-node metastasis in breast cancer. Breast Cancer 2021; 28(3): 664 doi: 10.1007/s12282-020-01202-z
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| 7 |
Brandon A. Howard, Terence Z. Wong. 18F-FDG-PET/CT Imaging for Gastrointestinal Malignancies. Radiologic Clinics of North America 2021; 59(5): 737 doi: 10.1016/j.rcl.2021.06.001
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| 8 |
Xiu-Qing Xue, Bing Wang, Wen-Ji Yu, Fei-Fei Zhang, Rong Niu, Xiao-Liang Shao, Yun-Mei Shi, Yan-Song Yang, Jian-Feng Wang, Xiao-Feng Li, Yue-Tao Wang. Relationship between total lesion glycolysis of primary lesions based on 18F-FDG PET/CT and lymph node metastasis in gastric adenocarcinoma: a cross-sectional preliminary study. Nuclear Medicine Communications 2022; 43(1): 114 doi: 10.1097/MNM.0000000000001475
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| 9 |
Sung Hoon Kim, Bong-Il Song, Hae Won Kim, Kyoung Sook Won, Young-Gil Son, Seung Wan Ryu, Yoo Na Kang. Prognostic value of the metabolic score obtained via [18F]FDG PET/CT and a new prognostic staging system for gastric cancer. Scientific Reports 2022; 12(1) doi: 10.1038/s41598-022-24877-0
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| 10 |
Xiu-qing Xue, Wen-Ji Yu, Xun Shi, Xiao-Liang Shao, Yue-Tao Wang. 18F-FDG PET/CT-based radiomics nomogram for the preoperative prediction of lymph node metastasis in gastric cancer. Frontiers in Oncology 2022; 12 doi: 10.3389/fonc.2022.911168
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