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使用基于机器学习的模型预测下垂体腺瘤手术后复发:系统性审查和元分析

Ibrahim Mohammadzadeh^1,2, Bardia Hajikarimloo³, Behnaz Niroomand⁴

¹Skull Base Research Center, Loghman-Hakim Hospital,, Shahid Beheshti University of Medical Sciences, Tehran, Iran. Ibrahim.mdz7777@gmail.com.

BMC endocrine disorders

|July 2, 2025

概括

机器学习模型准确地预测下垂体腺瘤复发,显示超过80%的准确性. 需要进一步验证,以改善这些人工智能驱动的预测的临床使用.

科学领域:

神经外科神经外科
在瘤学瘤学.
医疗成像医学成像

背景情况:

预测下垂体腺瘤 (PA) 术后复发对于患者管理至关重要.
机器学习 (ML) 模型在预测PA复发方面提供了有希望的进展.
准确的预测有助于个性化治疗规划和后续策略.

研究的目的:

系统地审查和元分析基于ML的模型的诊断性能,以预测PA复发.
评估ML算法在PA复发预测中的整体准确性和可靠性.

主要方法:

在主要数据库 (PubMed,Scopus,Embase等) 的系统文献搜索. 在2024年11月之前.
遵守PRISMA指南的研究选择和数据提取.
六项符合条件的研究的元分析,采用了12种不同的ML算法.

主要成果:

聚合灵敏度:0.87,特异性:0.86,AUC:0.89,表明了高的诊断性能.
ML模型显示,预测PA复发的平均准确率超过80%.
在物流回归与其他ML算法之间没有发现统计学意义上的差异.

结论:

基于人工智能的模型显示出对垂体腺瘤复发的显著预测能力.
挑战包括需要外部验证和标准化输入数据.
多中心验证和标准化成像对于临床整合至关重要.

关键词:

人工智能的人工智能是人工智能.深度学习是一种深度学习.机器学习是机器学习.下垂体腺瘤是什么意思预测者预测者复发情况复发情况

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