优化双源双能计算断层扫描 nomogram 模型 整合背景正常化 改善固体甲状腺结节 诊断
Qian Wang1,2, Yi Xin1,2, Yongli Feng1,2
1Department of Radiology, The First People's Hospital of Lianyungang, Lianyungang, China.
Gland surgery
|February 11, 2026
概括
双源双能量计算机断层扫描 (DS-DECT) 视图有效地使用定量和定性成像特征预测恶性甲状腺结节. 这种工具有助于医生区分良性和恶性结节,改善患者管理.
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:
- 准确的甲状腺结节的术前诊断对于个性化治疗至关重要.
- 区分良性和恶性结节会影响患者的治疗策略.
研究的目的:
- 开发一种基于双源双能计算断层扫描 (DS-DECT) 的名录模型.
- 预测甲状腺结节中恶性瘤的风险.
主要方法:
- 总共包括263名患有288个甲状腺结节的患者.
- 分析了放射性特征和DS-DECT定量参数.
- 使用后勤回归构建了一个名图,并在内部验证.
主要成果:
- 恶性瘤的独立预测因素包括度,正常度,有效原子数,甲状腺边缘中断和增强模糊.
- 诺米图实现了高诊断性能,AUC为0.932 (训练) 和0.908 (验证).
- 该模型表现出良好的适应性和校准性,通过决策曲线分析表明具有显著的临床实用性.
结论:
- 开发的诺米图是区分良性和恶性甲状腺结节的有效工具.
- 它集成了多相定量DS-DECT参数和定性成像特征.
- 该模型支持影像医生在手术前诊断和患者管理.
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