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比较人工智能模型在预测卵巢癌存活率方面的有效性:一个系统性审查

Farkhondeh Asadi¹, Milad Rahimi¹, Nahid Ramezanghorbani²

¹Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

Cancer reports (Hoboken, N.J.)

|March 19, 2025

概括

机器学习 (ML) 模型对预测卵巢癌 (OC) 存活率充满希望,但准确性和解释性仍然存在挑战. 整合不同类型的数据是提高预测精度的关键.

科学领域:

在瘤学瘤学.
医疗信息学医疗信息学
数据科学数据科学数据科学

背景情况:

卵巢癌 (OC) 生存预测对于患者管理至关重要.
机器学习 (ML) 为预后建模提供了先进的分析能力.
系统性审查,以评估OC生存预测中的ML算法的有效性.

研究的目的:

评估ML算法来预测卵巢癌的整体存活率 (OS),无复发存活率 (RFS),无进展存活率 (PFS) 和治疗反应.
确定影响OC ML模型预测准确性的关键特征.
评估ML在卵巢癌预后中的当前情况和未来方向.

主要方法:

在PubMed,Scopus,Web of Science和Cochrane数据库中的系统文献搜索.
包括过去十年内发表的32项研究,重点关注2021年后的最新进展.
分析常用的ML算法 (如随机森林,SVM,深度学习) 和评估指标 (AUC,C指数,准确性).

主要成果:

常见的ML算法包括随机森林,支持向量机,后勤回归,XGBoost和深度学习.
曲线下的面积 (AUC),一致性指数 (C指数) 和准确性是经常使用的评估指标.
确定的显著预测因素包括诊断时的年龄,瘤阶段,CA-125水平和治疗因素.

结论:

ML模型显示了预测卵巢癌生存结果的巨大潜力.
需要解决模型准确性和可解释性的挑战.
整合多种数据类型 (临床,成像,分子) 与多式机器学习方法的整合对于提高预后精度至关重要.

关键词:

人工智能是一种人工智能.癌症癌症癌症癌症癌症机器学习是机器学习.卵巢卵巢是一个卵巢.幸存率幸存率生存率

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