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黑色素瘤中哨戒节点阳性的风险预测模型:系统性审查和元分析
Bryan Ma1, Maharshi Gandhi2, Sonia Czyz2
1Division of Dermatology, Department of Medicine, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
JAMA dermatology
|March 12, 2025
概括
这次审查确定了有效的黑色素瘤风险预测模型,用于哨兵淋巴结活检 (SLNB) 的阳性. 几种模型显示强有力的外部验证,有助于临床决定SLNB阳性.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
背景情况:
- 黑色素瘤的分期和治疗决策通常依赖于哨兵淋巴结活检 (SLNB) 的阳性.
- 准确的风险预测模型对于识别可以从SLNB中受益的患者至关重要.
研究的目的:
- 系统地审查和评估黑色素瘤SLNB阳性现有风险预测模型的性能.
- 确定用于临床应用的表现最佳的模型.
主要方法:
- 在Embase和MEDLINE数据库中进行了全面的文献搜索,截至2024年5月1日.
- 包括的研究开发或验证了黑色素瘤SLNB阳性风险预测模型,以模型歧视为关键指标.
- 数据的提取和合成遵循了既定的指导方针 (DSReview,TRIPOD,PRISMA),并使用元分析进行聚合估计.
主要成果:
- 确定了21种不同的风险预测模型,对8种模型进行了20次外部验证.
- 所有模型的整体聚合加权C-统计值为0.78,表明适度的区分能力,尽管注意到了显著的异质性.
- 澳大利亚纪念斯隆凯特林癌症中心和黑色素瘤研究所的模型表现出强大且可比的外部验证性能 (C-统计分别为0.73和0.70).
- 结合基因表达特征的模型没有显示出比仅使用临床病理特征的模型显著更好的歧视.
结论:
- 一些外部验证的黑色素瘤SLNB阳性风险预测模型表现出强大的歧视性表现.
- 这些经过验证的模型为临床实践中的程序前风险评估提供了有价值的工具.
- 需要进一步的研究来评估实施这些模型对患者护理和结果的影响.
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