Zeng Xian, Li Zude, Dai Lilin, Li Jiang, Liao Luqin, Chen Wei
Department of Pharmacy, Affiliated Hospital of Guilin Medical University, Guilin, China.
Faculty of Public Administration, Guilin University of Technology, Guilin, China.
Discov Oncol. 2025 May 13;16(1):755. doi: 10.1007/s12672-025-02416-3.
Ovarian 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.
We 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.
A 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.
ML 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.
卵巢癌（OC）是女性常见的恶性肿瘤，预后较差，死亡率高。卵巢癌的早期诊断、筛查和预后预测长期以来一直是该领域的重点和挑战。近年来，机器学习（ML）在包括卵巢癌在内的肿瘤早期诊断、筛查和预后预测中逐渐展现出独特优势。本研究旨在分析机器学习在卵巢癌应用中的全球发展趋势和研究热点，从而为未来的研究方向提供参考。
我们在科学网核心合集（WoSCC）中检索了2004年至2024年所有与卵巢癌和机器学习相关的出版物，并使用VOSviewer、R软件和CiteSpace进行定量分析。
共检索到777篇文章。在过去20年中，与机器学习和卵巢癌相关的出版物数量持续增长。中国以254篇文章领先。最著名的期刊包括《妇科肿瘤学》《自然》《临床癌症研究》《癌症研究》和《临床肿瘤学杂志》。研究热点包括：（a）机器学习驱动的卵巢癌生物标志物发现和个性化治疗；（b）机器学习在肿瘤微环境分析和耐药性预测中的应用；（c）机器学习在基于影像的诊断和风险分层中的应用；（d）机器学习在多中心卵巢癌研究中的应用。
机器学习在卵巢癌领域目前正处于发展阶段，未来显示出有前景的潜力。本研究为研究人员和临床医生提供了对该领域研究重点和未来发展更系统的理解。
