在胃癌中使用细胞外体积分数和双能量CT定量参数进行外围神经侵入和生存的术前预测:一项双中心研究
Mengyue Zhang1, Mimi Mao2, Haipeng Gong2
1Department of Radiology, The Second Affiliated Hospital of Nantong University, Nantong First People's Hospital, Nantong, Jiangsu Province, 226001, China (M.Z., D.J., X.F., T.W.).
Academic radiology
|January 21, 2026
概括
一个新的名图有效地使用成像功能预测胃癌 (GC) 中的周围神经入侵 (PNI). 该工具还可以预测无复发生存率 (RFS),有助于患者管理.
科学领域:
- 放射学和医学成像学 医学成像学
- 在瘤学瘤学.
- 计算医学是一种计算医学.
背景情况:
- 周围神经侵入 (PNI) 是胃癌 (GC) 进展和复发的关键因素.
- 准确预测PNI及其对无复发生存率 (RFS) 的影响对于有效的患者管理至关重要.
研究的目的:
- 开发和验证用于预测GC中的PNI的nomogram.
- 将细胞外体积分数 (ECV),双能CT (DECT) 定量参数和形态特征集成到名ogram中.
- 评估诺米图的预测GC患者RFS的能力.
主要方法:
- 来自两个中心的GC患者数据的回顾性分析.
- 放射学家对ECV,DECT参数和形态特征的评估.
- 使用多变量逻辑回归来构建PNI的预测名录.
- 使用校准曲线,ROC曲线和DCA验证名图的验证.
- 用多变量考克斯回归来确定RFS的预后因素.
主要成果:
- 该名录集成ECV,NICdelay和ctEMVI来预测PNI.
- 诺米图表显示了PNI的良好预测性能 (AUC 0.822培训,0.810验证).
- 诺莫图预测的PNI是RFS的独立预后因素,预测PNI阳性病例的存活率较低.
结论:
- 结合ECV,DECT和形态特征的nomogram有效地预测GC中的PNI.
- 开发的诺米图谱对GC患者的术后RFS具有显著的预后价值.
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