Machine Learning-driven Histotype Diagnosis of Ovarian Carcinoma: Insights from the OCEAN AI Challenge

Ovarian cancer poses a significant health burden as one of the deadliest malignancies affecting women globally. Histotype assignment of epithelial ovarian cancers can be challenging due to morphologic overlap, inter-observer variability, and the lack of ancillary diagnostic techniques in some areas of the world. Moreover, rare cancers can pose particular diagnostic difficulties because of a relative lack of familiarity with them, underscoring the necessity for robust diagnostic methodologies. The emergence of Artificial Intelligence (AI) has brought promising prospects to the realm of ovarian cancer diagnosis. While various studies have underscored AI's promise, its validation across multiple healthcare centers and hospitals has been limited. Inspired by innovations in medical imaging driven by public competitions, we initiated the Ovarian Cancer subtypE clAssification and outlier detectioN (OCEAN) challenge, the most extensive histopathology competition to date. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work was supported by BC Cancer Foundation, CIHR (No 418734), NSERC (RGPIN-2019-04896), and Health Research BC grants to Dr. Ali Bashashati. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The Declaration of Helsinki and the International Ethical Guidelines for Biomedical Research Involving Human Subjects were strictly adhered throughout the course of this study. All study protocols have been approved by the University of British Columbia/BC Cancer Research Ethics Board. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data can be accessed and downloaded from the Kaggle challenge page (https://www.kaggle.com/competitions/UBC-OCEAN).