许多基于机器学习技术的计算机辅助预后（CAP）系统已在妇科肿瘤领域提出。本系统评价的目的是评估和批判性地评估使用CAP预测妇科癌症预后的方法和方法论。电子数据库被用于系统地搜索使用机器学习方法研究妇科癌症的文章。使用 PROBAST 工具评估研究风险偏倚度 (ROB) 和适用性。符合包含标准的文章共有139篇，其中71篇预测卵巢癌患者的预后结果，41篇预测颈癌患者的预后结果，28篇预测子宫癌患者的预后结果，2篇预测妇科恶性肿瘤的预后结果。随机森林（22.30%）和支持向量机（21.58%）分类器最常用。48.20％，51.08％和17.27％的文章中分别使用临床病理、基因组和放射组学数据作为预测指标，并且其中一些文章使用多个模式。21.58% 的文章得到了外部验证。23篇文章比较了 ML 和非-ML 方法。研究质量差异很大，方法论、统计报告和结果测量也不一致，这些都阻碍了对性能结果的归纳性评论或元分析。在预测妇科恶性肿瘤方面，模型开发存在显著的可变性，涉及到变量选择、机器学习（ML）方法和终点选择。这种异质性阻碍了元分析和关于 ML 方法优越性的结论。此外，PROBAST 又介绍 ROB 和适用性分析，表示现有模型的可转化性值得关注。本文识别了未来工作中如何改进这一有前途的领域内的强健、临床适用的模型的方法。版权所有 © 2023 Elsevier B.V.。

Many Computer Aided Prognostic (CAP) systems based on machine learning techniques have been proposed in the field of oncology. The objective of this systematic review was to assess and critically appraise the methodologies and approaches used in predicting the prognosis of gynecological cancers using CAPs.Electronic databases were used to systematically search for studies utilizing machine learning methods in gynecological cancers. Study risk of bias (ROB) and applicability were assessed using the PROBAST tool. 139 studies met the inclusion criteria, of which 71 predicted outcomes for ovarian cancer patients, 41 predicted outcomes for cervical cancer patients, 28 predicted outcomes for uterine cancer patients, and 2 predicted outcomes for gynecological malignancies broadly.Random forest (22.30 %) and support vector machine (21.58 %) classifiers were used most commonly. Use of clinicopathological, genomic and radiomic data as predictors was observed in 48.20 %, 51.08 % and 17.27 % of studies, respectively, with some studies using multiple modalities. 21.58 % of studies were externally validated. Twenty-three individual studies compared ML and non-ML methods. Study quality was highly variable and methodologies, statistical reporting and outcome measures were inconsistent, preventing generalized commentary or meta-analysis of performance outcomes.There is significant variability in model development when prognosticating gynecological malignancies with respect to variable selection, machine learning (ML) methods and endpoint selection. This heterogeneity prevents meta-analysis and conclusions regarding the superiority of ML methods. Furthermore, PROBAST-mediated ROB and applicability analysis demonstrates concern for the translatability of existing models. This review identifies ways that this can be improved upon in future works to develop robust, clinically translatable models within this promising field.Copyright © 2023 Elsevier B.V. All rights reserved.