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一个基于C-TIRADS的模型与SWE相结合,用于预测贝塞斯达I甲状腺结节

An Wei^1,2, Yu-Long Tang³, Shi-Chu Tang⁴

¹Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, China.

Frontiers in oncology

|September 16, 2024

概括

结合中国甲状腺成像报告和数据系统 (C-TIRADS),临床因素和剪切波弹性图 (SWE) 的新模型,准确预测贝塞斯达I甲状腺结节,可能减少不必要的细针向 (FNA).

科学领域:

放射学放射学是一门学科.
在瘤学瘤学.
医疗成像医学成像

背景情况:

甲状腺结节很常见,准确的手术前风险分层是至关重要的.
在精细针吸收 (FNA) 之前区分良性 (Bethesda I) 和潜在恶性结节可以优化患者管理.
当前的诊断方法可能需要提高准确性和效率.

研究的目的:

开发和评估贝塞斯达I甲状腺结节的预测模型.
整合中国甲状腺成像报告和数据系统 (C-TIRADS),临床特征和剪切波弹性学 (SWE) 以改善预测.
评估模型的表现与个别预测因素的对比.

主要方法:

一项对267名患者267个甲状腺结节的回顾性研究.
在FNA之前进行了超声波和SWE.
结节使用2020 C-TIRADS进行分类,并将贝塞斯达I和非I结节之间的特征进行比较.
后勤回归确定了独立的预测因素,并构建了一个预测模型.

主要成果:

最大结节直径 (15-20毫米),C-TIRADS类别 (<4C) 和Emax (<52.5千帕) 是贝塞斯达I结节的独立预测因素.
结合这些因素的预测模型显示曲线下的面积 (AUC) 为0.769.
该模型的AUC明显高于单个预测因素的AUC.

结论:

结合C-TIRADS,结节体大小和SWE参数的新型预测模型可以有效预测贝塞斯达I甲状腺结节.
这种模型可以帮助优化FNA策略并提高初始诊断的准确性.
实施可能会减少重复FNA程序的需要.

关键词:

中国甲状腺成像报告和数据系统弹性学弹性学弹性学贝塞斯达系统用于报告甲状腺细胞学.甲状腺结节甲状腺结节超声波超声波是指超声波的使用.

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