开发和验证基于常规和对比增强超声波的诺姆图,以区分恶性和良性甲状腺结节
Qi-Guo Wang1, Mei Li2, Guang-Xiu Deng1
1Department of Medical Ultrasound, the First People's Hospital of Qinzhou, Qinzhou, China.
Quantitative imaging in medicine and surgery
|May 19, 2025
概括
这项研究开发了一种使用超声波特征的预测模型,以准确区分恶性和良性甲状腺结节. 该模型显著减少了不必要的细针吸收活检,提高了诊断效率.
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:
- 传统的超声波 (美国) 在区分甲状腺结节方面存在局限性.
- 准确的诊断对于适当的患者管理至关重要.
研究的目的:
- 开发和验证一个预测诺莫格拉姆模型,用于区分恶性和良性甲状腺结节.
- 用传统的美国和对比增强超声波 (CEUS) 功能来评估模型的性能.
主要方法:
- 追溯收集了815个甲状腺结节,包括病理学结果和美国/CEUS数据.
- 使用后勤回归分析开发一个预测名ogram.
- 验证模型的诊断性能和对不必要的细针吸收活检 (FNAB) 率的影响.
主要成果:
- 确定的主要预测因素包括不规则的边缘,面积比>1,微化,低增强和环增强.
- 在训练和验证队列中,名图实现了高诊断性能 (AUC>0.94).
- 与中国甲状腺成像报告和数据系统相比,不必要的FNAB率大幅降低.
结论:
- 集成常规US和CEUS特征的预测名录有效地区分恶性和良性甲状腺结节.
- 该模型提供了更好的诊断准确性,并减少了不必要的侵入性手术.
- 诺莫图显示了甲状腺结节评估的良好临床实用性.
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