基于机器学习的模型用于手术前预测垂体腺瘤的一致性:系统性审查和元分析
Bardia Hajikarimloo1, Ibrahim Mohammadzadeh2, Salem M Tos3
1Department of Neurological Surgery, University of Virginia, Charlottesville, VA, USA. bardii47@yahoo.com.
Acta neurochirurgica
|January 24, 2026
概括
机器学习模型在手术前准确预测垂体腺瘤的一致性. 这有助于规划外科手术方法和改善患者的治疗结果.
科学领域:
- 神经外科 神经外科
- 放射学 放射学是一门学科.
- 人工智能的人工智能
背景情况:
- 下垂体腺瘤 (PA) 的稳定性会影响手术难度和切除程度.
- 准确的瘤牢固性的术前评估对于手术规划至关重要.
研究的目的:
- 系统地审查和元分析用于预测PA一致性的机器学习 (ML) 模型的诊断性能.
- 评估放射学和ML在评估MRI数据的瘤牢固性的实用性.
主要方法:
- 在PubMed,Embase,Scopus和Web of Science数据库中进行系统搜索.
- 包括开发或验证用于PA一致性预测的ML/深度学习模型的研究.
- 综合诊断性能指标的计算:AUC,精度,灵敏度,特异性和DOR.
主要成果:
- 分析了9项涉及1621名患者的研究.
- 包括额外树木,随机森林和SVM在内的ML模型显示出高的聚合性能.
- 聚合的AUC为0.92,准确度为0.86,灵敏度为0.80,特异性为0.85.
结论:
- ML模型在术前PA一致性预测方面表现出卓越的诊断准确性.
- 这些模型为个性化外科规划提供了显著的价值.
- 建议进行进一步的多中心研究,使用标准化成像和外部验证进行临床翻译.
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