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一种针对性蛋白质组学试验,用于手术前诊断甲状腺结核

Xue Cai¹, Yaoting Sun¹, Dingcun Luo²

¹Affiliated Hangzhou First People's Hospital, State Key Laboratory of Medical Proteomics, School of Medicine, Westlake University, Hangzhou, Zhejiang Province, China; Westlake Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China; Research Center for Industries of the Future, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang, China.

Cell reports. Medicine

|March 11, 2026

概括

一个新的诊断分类器,ThyroProt,使用蛋白质学和临床数据改善了甲状腺结节的手术前诊断. 它准确地识别了不确定的甲状腺细胞学,有助于临床决策.

科学领域:

蛋白质组学是指蛋白质组学.
在瘤学瘤学.
医学诊断医学诊断医学诊断

背景情况:

精细针吸收 (FNA) 活检在准确诊断甲状腺结节方面存在挑战,特别是在不确定的细胞学方面.
现有的诊断方法往往缺乏足够的准确性,对于关键的患者管理决策.

研究的目的:

开发和验证ThyroProt,这是一种用于手术前甲状腺结节诊断的新型诊断分类器.
评估ThyroProt的性能,特别是在未确定的甲状腺细胞学 (Bethesda III/IV) 的情况下.

主要方法:

一项前性多中心研究,涉及837个精细针吸收 (FNA) 样本,用于开发和验证.
ThyroProt集成了基于质谱的向蛋白质量测量 (3-蛋白质签名) 与临床数据 (年龄,性别,BRAF V600E突变状态).
对322个样本进行前性评估,并在两个独立的队列中进行验证.

主要成果:

在预期的测试组中,分类器在曲线下的面积 (AUC) 达到0.94和90.7%的整体准确性.
对于贝塞斯达III/IV结节,ThyroProt的准确率为88.0%,灵敏度为82.4%,特异性为100%.
独立的队列验证证明了强大的性能,AUC为0.87-0.91和准确率为84.3%-85.7%.

结论:

基于质谱的向蛋白质组学为改善甲状腺结节的术前诊断提供了显著的临床实用性.
ThyroProt显示出高精度和特异性,特别有利于管理未确定甲状腺细胞学的患者.
这种方法提高了诊断的信心,并支持更好的临床决策在甲状腺结节管理.

关键词:

绝对量化的绝对量化.人工智能的人工智能是人工智能.根据贝塞斯达分类的分类.精细针的吸收方式不确定的甲状腺结节.质谱测量质谱测量质谱测量质谱测量质量测量质谱测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量质量测量分子分类器模型分子分类器模型多中心临床研究.预期的诊断是一个潜在的诊断.有针对性的蛋白质组学.

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