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For: Ladbury C, Li R, Shiao J, Liu J, Cristea M, Han E, Dellinger T, Lee S, Wang E, Fisher C, Chen YJ, Amini A, Robin T, Glaser S. Characterizing impact of positive lymph node number in endometrial cancer using machine-learning: A better prognostic indicator than FIGO staging? Gynecol Oncol 2022;164:39-45. [PMID: 34794840 DOI: 10.1016/j.ygyno.2021.11.007] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
Number Citing Articles
1 Jiang C, Xiu Y, Qiao K, Yu X, Zhang S, Huang Y. Prediction of lymph node metastasis in patients with breast invasive micropapillary carcinoma based on machine learning and SHapley Additive exPlanations framework. Front Oncol 2022;12:981059. [DOI: 10.3389/fonc.2022.981059] [Reference Citation Analysis]
2 Piedimonte S, Feigenberg T, Drysdale E, Kwon J, Gotlieb WH, Cormier B, Plante M, Lau S, Helpman L, Renaud MC, May T, Vicus D. Predicting recurrence and recurrence-free survival in high-grade endometrial cancer using machine learning. J Surg Oncol 2022. [PMID: 35819161 DOI: 10.1002/jso.27008] [Reference Citation Analysis]