Purpose - To characterise and assess the quality of published research evaluating artificial intelligence (AI) methods for ovarian cancer diagnosis or prognosis using histopathology data. Methods - A search of PubMed, Scopus, Web of Science, CENTRAL, and WHO-ICTRP was conducted up to 19/05/2023. The inclusion criteria required that research evaluated AI on histopathology images for diagnostic or prognostic inferences in ovarian cancer. The risk of bias was assessed using PROBAST. Information about each model of interest was tabulated and summary statistics were reported. PRISMA 2020 reporting guidelines were followed. Results - 1573 records were identified, of which 45 were eligible for inclusion. There were 80 models of interest, including 37 diagnostic models, 22 prognostic models, and 21 models with other diagnostically relevant outcomes. Models were developed using 1-1375 slides from 1-776 ovarian cancer patients. Model outcomes included treatment response (11/80), malignancy status (10/80), stain quantity (9/80), and histological subtype (7/80). All models were found to be at high or unclear risk of bias overall, with most research having a high risk of bias in the analysis and a lack of clarity regarding participants and predictors in the study. Research frequently suffered from insufficient reporting and limited validation using small sample sizes. Conclusion - Limited research has been conducted on the application of AI to histopathology images for diagnostic or prognostic purposes in ovarian cancer, and none of the associated models have been demonstrated to be ready for real-world implementation. Key aspects to help ensure clinical translation include more transparent and comprehensive reporting of data provenance and modelling approaches, as well as improved quantitative performance evaluation using cross-validation and external validations.

翻译：目的 - 描述并评估利用组织病理学数据对卵巢癌进行诊断或预后的人工智能（AI）方法已发表研究的质量。方法 - 对PubMed、Scopus、Web of Science、CENTRAL及WHO-ICTRP数据库进行检索，截止日期为2023年5月19日。纳入标准要求研究基于组织病理学图像评估AI在卵巢癌诊断或预后推断中的表现。使用PROBAST工具评估偏倚风险。将各感兴趣模型的信息制成表格，并报告汇总统计量。遵循PRISMA 2020报告指南。结果 - 共识别1573条记录，其中45项符合纳入标准。共包含80个感兴趣模型，包括37个诊断模型、22个预后模型及21个其他诊断相关结局模型。模型开发基于1-776例卵巢癌患者的1-1375张切片。模型预测结局包括治疗反应（11/80）、恶性状态（10/80）、染色量（9/80）及组织学亚型（7/80）。所有模型整体均存在高或不明确的偏倚风险，多数研究在分析环节存在高偏倚风险，且对研究对象和预测因子的描述缺乏清晰性。研究普遍存在报告不充分、验证样本量有限的问题。结论 - 目前关于AI应用于卵巢癌组织病理学图像进行诊断或预后的研究有限，且尚无相关模型被证实可实际应用于临床。确保临床转化的关键因素包括：更透明全面地报告数据来源与建模方法，以及通过交叉验证和外部验证改进定量性能评估。