| For: | Liu YM, Du YY, Song Y, Xiong HT, Yu HB, Li BH, Cai L, Ma SS, Gao J, Zhang HY, Fang RY, Cai R, Zheng HG. Predicting chemotherapy-induced myelosuppression in colorectal cancer: An interpretable, machine learning-based nomogram. World J Gastroenterol 2025; 31(42): 112180 [PMID: 41278162 DOI: 10.3748/wjg.v31.i42.112180] |
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| URL: | https://www.wjgnet.com/1007-9327/full/v31/i42/112180.htm |
| Number | Citing Articles |
| 1 |
Linxian Ding, Lixia Peng, Zheng Xu, Zhangli Cui, Zhongming Wang. Interpretable machine-learning prediction of severe myelosuppression in colorectal cancer patients receiving chemotherapy using XGBoost and SHAP: a retrospective study with a web-based calculator. Frontiers in Oncology 2026; 16 doi: 10.3389/fonc.2026.1785146
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| 2 |
Xingnong Xu, Lei Ma. Haematological toxicity associated with antineoplastic drugs: a pharmacovigilance analysis based on the FDA adverse event reporting system database. Frontiers in Immunology 2026; 17 doi: 10.3389/fimmu.2026.1947521
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| 3 |
Peng Wang, Jin-Ke Sun, Dong Li, Zhen Shi, Peng-Yu Lu, Dong-Fang Jin, Sheng-Fan Huang, Yu-Hua Ruan, Wen-Ting Li, Meng-Di Shi, Zhan-Hao Ma, Zi-Hao Wang, Li-Yuan Hu, Meng-En Xue, Chang-Jiang Zhang, Zhi-Peng Li. Precision management of gastrointestinal tumor-associated osteoporosis driven by cutting-edge technologies: Current status, challenges, and future prospects. World Journal of Methodology 2026; 16(3): 121456 doi: 10.5662/wjm.121456
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| 4 |
Yuhan Yang, Xici Liu. Application of explainable artificial intelligence integrating with electronic health record in oncology. Exploration of Targeted Anti-tumor Therapy 2026; 7 doi: 10.37349/etat.2026.1002357
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| 5 |
Yi-Wei Qin, Peng-Wei Li, Xin-Yi Liang, You Mo, Da-Wei Chen. Letter to the Editor: Balancing efficacy and toxicity: The critical role of predictive models in colorectal cancer chemotherapy. World Journal of Gastroenterology 2026; 32(15): 116121 doi: 10.3748/wjg.v32.i15.116121
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