Extranodal Spread in the Neck: MRI Detection on the Basis of Pixel-Based Time-Signal Intensity Curve Analysis
Extranodal Spread in the Neck: MRI Detection on the Basis of Pixel-Based Time-Signal Intensity Curve Analysis
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DOI：
10.1002/jmri.22454
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发表时间：
2011-04-01
影响因子：
4.4
通讯作者：
Nakamura, Takashi
中科院分区：
文献类型：
--
作者：
Sumi, Misa;Nakamura, Takashi
目的：我们评估动态对比增强（DCE）磁共振成像（MRI）术前检测颈部转移淋巴结结外扩散（ENS）的效果。材料和方法：回顾性分析43例头颈部鳞状细胞癌（SCC）患者的54例组织学证实的转移淋巴结（26例ens阳性，28例ens阴性）的时间信号强度曲线（TIC）特征，以确定ens阳性淋巴结的有效TIC标准。在逐像素的基础上，将tic半自动地分为四种不同的模式（平坦、缓慢摄取、快速摄取低洗净率和快速摄取高洗净率）。结果:然而，多变量logistic回归分析显示，只有短轴直径和TIC模式缓慢摄取的区域是ens存在的显著且独立的指标。淋巴结大小（>25 mm）或TIC谱（>44%的淋巴结面积具有缓慢摄取的TIC模式）的MRI联合标准是区分ens阳性和阴性淋巴结的最佳标准。提供96%的灵敏度，100%的特异性，98%的准确度，100%的阳性预测值和97%的阴性预测值。结论：结合尺寸标准，基于像素的磁共振因子分析可能是检测ENS的有效工具。
Purpose: We evaluated dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) for the preoperative detection of extranodal spread (ENS) in metastatic nodes in the neck.Materials and Methods: The time-signal intensity curve (TIC) profiles of 54 histologically proven metastatic nodes (26 ENS-positive and 28 ENS-negative) from 43 patients with head and neck squamous cell carcinoma (SCC) were retrospectively analyzed to determine the effective TIC criteria for ENS-positive nodes. The TICs were semiautomatically classified into four distinctive patterns (flat, slow uptake, rapid uptake with low washout ratio, and rapid uptake with high washout ratio) on a pixel-by-pixel basis.Results: A number of the MRI findings were significantly correlated with ENS. However, multivariate logistic regression analysis revealed that only a short-axis diameter and an area with slow uptake TIC patterns were significantly and independently indicative of the presence of ENS. The combined MRI criteria of nodal size (>25 mm) or TIC profile (>44% nodal areas with slow-uptake TIC patterns) yielded the best results for differentiation between ENS-positive and ENS-negative nodes, providing 96% sensitivity, 100% specificity, 98% accuracy, and 100% positive, and 97% negative predictive values.Conclusion: When combined with size criteria, pixelbased MR factor analysis may be a promising tool for detecting ENS.