| For: | Peng CM, Chen CW, Hsieh CH, Cheng YY, Liao CH, Hsieh MF, Lin SC, Liu MC, Liu YJ. Radiomics meets sarcopenia: Machine learning-based multimodal modeling for esophageal cancer outcomes. World J Gastrointest Oncol 2025; 17(10): 111399 [PMID: 41114100 DOI: 10.4251/wjgo.v17.i10.111399] |
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| URL: | https://www.wjgnet.com/1948-5204/full/v17/i10/111399.htm |
| Number | Citing Articles |
| 1 |
Ming-Cheng Liu, Yung-Yin Cheng, Shao-Chieh Lin, Chih-Hung Lin, Cheng-Yen Chuang, Wen-Hsien Chen, Chun-Han Liao, Chia-Hong Hsieh, Mei-Fang Hsieh, Yi-Jui Liu. Machine learning survival prediction in esophageal cancer using radiomics and body composition from pretreatment and follow-up T12-level computed tomography. World Journal of Gastrointestinal Oncology 2025; 17(12): 112873 doi: 10.4251/wjgo.v17.i12.112873
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| 2 |
Md. Nasir Uddin, Shafiqul Islam, Ashfaqul Islam, Abdul Kadar Muhammad Masum, Khandaker Mohammad Mohi Uddin. Interpretable LightGBM framework for predicting esophageal cancer using clinical data: Integrating ensemble feature selection and data balancing. Intelligent Hospital 2026; doi: 10.1016/j.inhs.2026.100088
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| 3 |
Sivan Sathish, Ankita Jain, Kratee Sharma, Karthika B. Artificial intelligence in quantitative imaging of esophageal cancer: A review on radiomics, sarcopenia, and survival modeling. World Journal of Gastrointestinal Oncology 2026; 18(7): 119986 doi: 10.4251/wjgo.v18.i7.119986
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