卵巢癌中的机器学习:从2004年到2024年的文献计量和视觉分析
Xian Zeng1, Zude Li2, Lilin Dai1
1Department of Pharmacy, Affiliated Hospital of Guilin Medical University, Guilin, China.
Discover oncology
|May 13, 2025
概括
机器学习 (ML) 显示了卵巢癌 (OC) 早期诊断和治疗的潜力越来越大. 本研究分析了OC研究的ML应用的全球趋势和热点,指导了未来的方向.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 生物信息学是一种生物信息学.
背景情况:
- 由于预后不佳和死亡率高,卵巢癌 (OC) 存在重大挑战.
- 早期诊断,查和预后预测是OC管理中的关键未满足需求.
- 机器学习 (ML) 正在成为推动瘤诊断和预测的强大工具.
研究的目的:
- 分析全球发展趋势和研究热点在OC应用ML的应用.
- 为OC提供ML的未来研究方向提供参考.
主要方法:
- 对2004-2024年出版物进行定量文献计量分析.
- 数据来源于网络科学核心集合 (WoSCC).
- 使用VOSviewer,R软件和CiteSpace进行的分析.
主要成果:
- 总共有777篇文章被检索,显示了20年来出版物的持续增长.
- 中国在出版物方面处于领先地位,主要期刊包括妇科瘤学和自然.
- 研究热点包括生物标志物发现的ML,个性化治疗,瘤微环境分析,基于成像的诊断和风险分层.
结论:
- 在OC中ML的应用处于发展阶段,具有显著的未来潜力.
- 这项研究为研究人员和临床医生提供了研究优先事项和新兴发展的系统概述.
- 了解这些趋势可以加速ML驱动的卵巢癌研究的进展.
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