为了回顾有关机器学习在子宫内膜癌中的文献、报告最常用的算法，并与传统预测模型进行比较，进行了系统的文献回顾，时间范围为1985年1月至2021年3月，对电子数据库进行了广泛的搜索。首先按标题筛选，然后按全文筛选。使用MINORS（非随机研究方法学指数）标准对质量进行评估。使用JMP 15.0中的Pearson'sΧ2检验推导P值。在筛选的4295篇文章中，包括了30项关于机器学习在子宫内膜癌中的研究。最常见的应用领域是患者数据集（33.3%，n=10）、术前诊断（30%，n=9）、基因组学（23.3%，n=7）和血清生物标志物（13.3%，n=4）。最常用的模型是神经网络（n=10，33.3%）和支持向量机（n=6，20%）。关于机器学习在子宫内膜癌中的出版物数量从2010年的1篇增加到2021年的29篇。有8项研究比较了机器学习和传统统计学。在患者数据集研究中，两个机器学习模型（20%）与 logistic 回归模型表现相似（准确度：0.85 vs 0.82，p=0.16）。机器学习算法在基于 MRI 定位子宫内膜癌方面表现与传统方法相似（准确度：0.87 vs 0.82，p=0.24），而在一个血清生物标志物研究中在预测子宫外疾病方面优于传统方法（准确度：0.81 vs 0.61）。关于生存结果，一项研究将机器学习与Kaplan-Meier进行比较，报告了在一致性指数方面没有差异（83.8% vs 83.1%）。尽管机器学习是一种创新和新兴技术，但在子宫内膜癌中表现与传统回归模型相似。需要更多的研究来评估其在子宫内膜癌中的作用。CRD42021269565.© IGCS and ESGO 2023. 禁止商业再使用。请参阅权限和许可。由BMJ出版。

To review the literature on machine learning in endometrial cancer, report the most commonly used algorithms, and compare performance with traditional prediction models.This is a systematic review of the literature from January 1985 to March 2021 on the use of machine learning in endometrial cancer. An extensive search of electronic databases was conducted. Four independent reviewers screened studies initially by title then full text. Quality was assessed using the MINORS (Methodological Index for Non-Randomized Studies) criteria. P values were derived using the Pearson's Χ2 test in JMP 15.0.Among 4295 articles screened, 30 studies on machine learning in endometrial cancer were included. The most frequent applications were in patient datasets (33.3%, n=10), pre-operative diagnostics (30%, n=9), genomics (23.3%, n=7), and serum biomarkers (13.3%, n=4). The most commonly used models were neural networks (n=10, 33.3%) and support vector machine (n=6, 20%).The number of publications on machine learning in endometrial cancer increased from 1 in 2010 to 29 in 2021.Eight studies compared machine learning with traditional statistics. Among patient dataset studies, two machine learning models (20%) performed similarly to logistic regression (accuracy: 0.85 vs 0.82, p=0.16). Machine learning algorithms performed similarly to detect endometrial cancer based on MRI (accuracy: 0.87 vs 0.82, p=0.24) while outperforming traditional methods in predicting extra-uterine disease in one serum biomarker study (accuracy: 0.81 vs 0.61). For survival outcomes, one study compared machine learning with Kaplan-Meier and reported no difference in concordance index (83.8% vs 83.1%).Although machine learning is an innovative and emerging technology, performance is similar to that of traditional regression models in endometrial cancer. More studies are needed to assess its role in endometrial cancer.CRD42021269565.© IGCS and ESGO 2023. No commercial re-use. See rights and permissions. Published by BMJ.