Machine learning in ovarian cancer: a bibliometric and visual analysis from 2004 to 2024

ObjectiveOvarian cancer (OC) is a common malignant tumor in women, with poor prognosis and high mortality rates. Early diagnosis, screening, and prognostic prediction of OC have long been focal points and challenges in this field. In recent years, machine learning (ML) has gradually demonstrated its unique advantages in the early diagnosis, screening, and prognostic prediction of tumors, including OC.This study aims to analyze global development trends and research hotspots in the application of ML for OC, thereby providing a reference for future research directions.MethodsWe searched the Web of Science Core Collection (WoSCC) for all publications related to OC and ML from 2004 to 2024, conducting a quantitative analysis using VOSviewer, R software, and CiteSpace.ResultsA total of 777 articles were retrieved.The number of publications related to ML and OC has grown continuously over the past 20 years.China led with 254 articles.The most prominent journals include Gynecologic Oncology, Nature, Clinical Cancer Research, Cancer Research, and Journal of Clinical Oncology.Research hotspots are: (a) ML-driven OC biomarker discovery and personalized treatment; (b) ML in tumor microenvironment analysis and resistance prediction; (c) ML in imaging-based diagnosis and risk stratification; (d) ML in multicenter OC studies.ConclusionML in OC is currently in a developmental phase and shows promising potential for the future. This study provides researchers and clinicians with a more systematic understanding of research priorities and forthcoming developments in this area.