肺结节CT导向活检的非恶性病理结果:用于识别假阴性结果的预测模型
Xu-Zhou Wang1, Jing-Ya Wang2, Tao Meng3
1Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China.
Journal of cardiothoracic surgery
|June 26, 2024
概括
这项研究开发了一种预测模型,用于识别肺结节 (PNs) 的计算机断层成像指导活检 (CTB) 的假阴性结果. 该模型准确地预测了非恶性CTB发现,实际上可能是恶性的,提高了PNS的诊断准确性.
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:
- 计算机断层扫描 (CT) 引导的活检 (CTB) 对于诊断肺结节 (PNs) 至关重要.
- 当初发现表明非恶性时,准确识别假阴性CTB结果至关重要.
- 需要一个预测模型来识别PNS患者的错误阴性CTB结果.
研究的目的:
- 构建和验证一个预测模型,用于识别PNS患者的错误阴性CTB结果.
- 为了提高肺结节的CTB的诊断准确度.
主要方法:
- 来自两个中心 (2016年1月至2020年12月) 的连续患者的回顾性分析,对PN的非恶性CTB结果.
- 使用培训队列开发预测模型,以确定预测假负结果的特征.
- 使用单独的测试队列验证模型的准确性.
主要成果:
- 疑似恶性细胞的更高标准化最大摄取值 (SUVmax) 和CTB发现预测了训练队列 (102名患者) 中的错误阴性结果.
- 预测模型在训练队列中实现了0.945的接收器操作特征曲线 (AUC) 下的面积,灵敏度为88.2%,特异性为87.1%.
- 该模型在测试队列 (62名患者) 中表现良好,AUC为0.851.
结论:
- 开发的预测模型在识别肺结节的错误阴性CTB结果方面显示出显著的诊断效果.
- 这个模型可以帮助临床医生更好地管理潜在的非恶性CTB发现的患者.
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