Artificial Intelligence in Ovarian Cancer Histopathology: A Systematic Review

• URL: http://arxiv.org/abs/2303.18005v2
• Date: Fri, 16 Jun 2023 15:02:38 GMT
• Title: Artificial Intelligence in Ovarian Cancer Histopathology: A Systematic Review
• Authors: Jack Breen, Katie Allen, Kieran Zucker, Pratik Adusumilli, Andy Scarsbrook, Geoff Hall, Nicolas M. Orsi, Nishant Ravikumar
• Abstract summary: Methods: A search of PubMed, Scopus, Web of Science, CENTRAL, and WHO-ICTRP was conducted. Risk of bias was assessed using PROBAST. There were 80 models of interest, including 37 diagnostic models, 22 prognostic models, and 21 models with other diagnostically relevant outcomes. 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.
• Score: 1.832300121391956
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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