Breen Jack, Allen Katie, Zucker Kieran, Adusumilli Pratik, Scarsbrook Andrew, Hall Geoff, Orsi Nicolas M, Ravikumar Nishant
Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), School of Computing, University of Leeds, Leeds, UK.
Leeds Institute of Medical Research at St James's, School of Medicine, University of Leeds, Leeds, UK.
NPJ Precis Oncol. 2023 Aug 31;7(1):83. doi: 10.1038/s41698-023-00432-6.
This study evaluates the quality of published research using artificial intelligence (AI) for ovarian cancer diagnosis or prognosis using histopathology data. A systematic search of PubMed, Scopus, Web of Science, Cochrane CENTRAL, and WHO-ICTRP was conducted up to May 19, 2023. Inclusion criteria required that AI was used for prognostic or diagnostic inferences in human ovarian cancer histopathology images. Risk of bias was assessed using PROBAST. Information about each model was tabulated and summary statistics were reported. The study was registered on PROSPERO (CRD42022334730) and PRISMA 2020 reporting guidelines were followed. Searches identified 1573 records, of which 45 were eligible for inclusion. These studies contained 80 models of interest, including 37 diagnostic models, 22 prognostic models, and 21 other diagnostically relevant models. Common tasks included treatment response prediction (11/80), malignancy status classification (10/80), stain quantification (9/80), and histological subtyping (7/80). Models were developed using 1-1375 histopathology slides from 1-776 ovarian cancer patients. A high or unclear risk of bias was found in all studies, most frequently due to limited analysis and incomplete reporting regarding participant recruitment. 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 models have been demonstrated to be ready for real-world implementation. Key aspects to accelerate clinical translation include transparent and comprehensive reporting of data provenance and modelling approaches, and improved quantitative evaluation using cross-validation and external validations. This work was funded by the Engineering and Physical Sciences Research Council.
本研究评估了利用人工智能（AI）通过组织病理学数据进行卵巢癌诊断或预后的已发表研究的质量。截至2023年5月19日，对PubMed、Scopus、Web of Science、Cochrane CENTRAL和WHO-ICTRP进行了系统检索。纳入标准要求在人类卵巢癌组织病理学图像中使用AI进行预后或诊断推断。使用PROBAST评估偏倚风险。将每个模型的信息制成表格并报告汇总统计数据。该研究已在PROSPERO上注册（CRD42022334730），并遵循PRISMA 2020报告指南。检索共识别出1573条记录，其中45条符合纳入标准。这些研究包含80个感兴趣的模型，包括37个诊断模型、22个预后模型和21个其他与诊断相关的模型。常见任务包括治疗反应预测（11/80）、恶性状态分类（10/80）、染色定量（9/80）和组织学亚型分类（7/80）。模型是使用来自1至776名卵巢癌患者的1至1375张组织病理学切片开发的。在所有研究中均发现高或不明确的偏倚风险，最常见的原因是关于参与者招募的分析有限和报告不完整。关于将AI应用于卵巢癌组织病理学图像进行诊断或预后目的的研究有限，并且没有一个模型已被证明可用于实际应用。加速临床转化的关键方面包括透明和全面地报告数据来源和建模方法，以及使用交叉验证和外部验证改进定量评估。本研究由工程和物理科学研究委员会资助。
