  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.
