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通过地面玻璃肺结节的逻辑回归来预测透风险模型:系统审查和元分析

Mengqian Li^1,2, Xiaomei Zhang², Yuxin Lai¹

¹Department of Internal Medicine of Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China.

Frontiers in oncology

|February 13, 2025

概括

这项研究确定了地面玻璃肺结节侵入的关键风险因素. 这些发现有助于早期检测和改善肺结节患者的预后.

科学领域:

肺部病理学肺部病理学
放射学放射学是一门学科.
在瘤学瘤学.

背景情况:

地玻璃肺结节 (GGNs) 需要对入侵进行准确的评估.
预测模型可以帮助区分侵袭性和非侵袭性GGN.

研究的目的:

系统地审查和元分析GGN入侵的预测模型.
为了确定与GGN侵入性相关的独立风险因素.

主要方法:

全面的数据库搜索 (CNKI,万芳,VIP,Sinomed,Pubmed,Web of Science,Embase) 时间至2024年1月.
包括所有预测模型的GGN入侵病理诊断的患者.
使用SPSS 25.0和Revman 5.4软件对独立风险因素的元分析.

主要成果:

包括29篇文章,分析了30个独立的风险因素.
对于GGN入侵的重要风险因素包括:平均CT值,最大直径,血管收标志,分叶标志,平均直径,最大CT值,尖端标志,体积,真空标志,CTR≥0.5,血管类型 (III和IV),膜缩,年龄和最小入侵的GGN (mGGN).
预测模型表现良好,AUC值在0.736到0.977.7之间.

结论:

识别的预测模型在评估GGN入侵方面表现良好.
识别这些独立的风险因素可以帮助临床医生识别高风险的GGN,促进及时干预和改善患者的治疗结果.

关键词:

磨砂玻璃肺结节肺结节独立的风险因素独立的风险因素透的潜入方式逻辑回归的逻辑回归预测模型预测模型系统审查和元分析.

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