在贝塞斯达III和IV甲状腺结节中的良性和恶性病变通过双能量计算机断层扫描量化参数和形态特征进行区分
Xiaofang Ren1, Zuhua Song1, Dan Zhang1
1Department of Radiology, Chongqing General Hospital, Chongqing, China.
Quantitative imaging in medicine and surgery
|July 18, 2024
概括
结合双能计算断层扫描 (DECT) 的定量参数和形态特征的新模型可以准确地区分贝塞斯达类III和IV的良性和恶性甲状腺结节 (TNs),减少不必要的手术.
科学领域:
- 放射学 放射学是一门学科.
- 在瘤学瘤学.
- 内分泌学 在内分泌学.
背景情况:
- 作为贝塞斯达III和IV分类的甲状腺结节 (TNs) 存在诊断挑战,往往导致对良性病例不必要的甲状腺叶切除术.
- 开发非侵入性方法来区分良性和恶性TN对于患者管理至关重要.
研究的目的:
- 评估一个整合双能计算断层扫描 (DECT) 定量参数和形态特征的模型,以区分良性和恶性Bethesda III和IV TNs.
- 评估基于DECT的模型在减少甲状腺结节不必要的手术方面的诊断性能.
主要方法:
- 从77名贝塞斯达III和IV型TNs接受手术的患者的DECT扫描进行了回顾性分析.
- 在动脉和静脉阶段测量了定量DECT参数 (正常化的度,光谱Hounsfield单位曲线斜率,正常化的有效原子数).
- 用接收器操作特征 (ROC) 曲线分析来比较单个参数,形态特征和组合模型的诊断性能.
主要成果:
- 结合动脉相正常化度 (AP-NIC) 和增强模糊的模型实现了最高的诊断性能 (AUC: 0.808,灵敏度: 0.854,特异性: 0.655).
- 从这种最佳模型构建的名图表显示了良好的校准 (预测差异<5%) 和跨特定风险值的正净收益.
- 像AP-NIC这样的个别DECT参数显示出中等的诊断能力 (AUC高达0.749).
结论:
- 结合DECT定量参数和形态特征模型,特别是增强模糊的AP-NIC,可靠地将良性与恶性Bethesda III和IV TNs区分开来.
- 这种基于DECT的方法显示了改善手术前诊断和指导甲状腺结节手术决策的潜力.
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