| For: | Zhou XC, Guan SW, Ke FY, Dhamija G, Wang Q, Chen BF. Construction of a nomogram model to predict technical difficulty in performing laparoscopic sphincter-preserving radical resection for rectal cancer. World J Gastroenterol 2024; 30(18): 2418-2439 [PMID: 38764764 DOI: 10.3748/wjg.v30.i18.2418] |
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| URL: | https://www.wjgnet.com/1007-9327/full/v30/i18/2418.htm |
| Number | Citing Articles |
| 1 |
Zhen Wang, Lei Huang, Liang He, Shuang Li, Siyu Peng, Yang Gong, Dongmei Mu, Quan Wang. Machine learning model for predicting a high comprehensive complication index following rectal cancer surgery. Updates in Surgery 2025; doi: 10.1007/s13304-025-02401-z
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| 2 |
Wenbo Shang, Fazheng Luo, Wei Wang, Xiaoqin Liu, Bokai Li. A Microservice Association Model Approach. 2024 IEEE 7th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) 2024; doi: 10.1109/ITNEC60942.2024.10733022
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| 3 |
Haoran Mao, Shuai Ma, Yang Li, Xuan Sun, Yongqi Fu, Huaju Zhang, Quanbo Zhou, Shihao Guo, Xiaofei Duan, Tengyu Li, Haifeng Sun, Hairong Zhang, Zhiyong Zhang, Guixian Wang, Junhong Hu, Zhen Li, Zhenqiang Sun, Changqing Jing, Quan Wang, Weitang Yuan, Hongwei Yao, Yugui Lian. Interpretable machine learning model for predicting operative difficulty in robotic total mesorectal excision for mid-low rectal cancer. Journal of Robotic Surgery 2026; 20(1) doi: 10.1007/s11701-026-03522-2
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