# Artificial Intelligence in Ovarian Cancer Histopathology: A Systematic Review

arXiv: https://arxiv.org/abs/2303.18005 · HTML: https://mlsanity.com/paper/2303.18005/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 81 references that are in the map.
- First posted on arXiv 31 Mar 2023.

## Builds on (most cited first, 7 of 7)

- [Attention-based Deep Multiple Instance Learning](https://mlsanity.com/paper/1802.04712/) (2018)
- [Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images](https://mlsanity.com/paper/2004.09666/) (2020)
- [Transformers in Medical Image Analysis: A Review](https://mlsanity.com/paper/2202.12165/) (2022)
- [Pan-Cancer Diagnostic Consensus Through Searching Archival Histopathology Images Using Artificial Intelligence](https://mlsanity.com/paper/1911.08736/) (2019)
- [Deep Interactive Learning-based ovarian cancer segmentation of H&E-stained whole slide images to study morphological patterns of BRCA mutation](https://mlsanity.com/paper/2203.15015/) (2022)
- [Digital pathology-based study of cell- and tissue-level morphologic features in serous borderline ovarian tumor and high-grade serous ovarian cancer](https://mlsanity.com/paper/2008.12479/) (2020)
- [EOCSA: Predicting Prognosis of Epithelial Ovarian Cancer with Whole Slide Histopathological Images](https://mlsanity.com/paper/2210.05258/) (2022)

## Built on by (most cited first, 4 of 4)

- [A Comprehensive Evaluation of Histopathology Foundation Models for Ovarian Cancer Subtype Classification](https://mlsanity.com/paper/2405.09990/) (2024)
- [Generative Adversarial Networks for Stain Normalisation in Histopathology](https://mlsanity.com/paper/2308.02851/) (2023)
- [Multi-Resolution Histopathology Patch Graphs for Ovarian Cancer Subtyping](https://mlsanity.com/paper/2407.18105/) (2024)
- [Predicting Ovarian Cancer Treatment Response in Histopathology using Hierarchical Vision Transformers and Multiple Instance Learning](https://mlsanity.com/paper/2310.12866/) (2023)

## Abstract (arXiv)

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.