Differentiating Benign and Malignant Soft Tissue Masses by Magnetic Resonance Imaging: Role of Tissue Component Analysis
Differentiating Benign and Malignant Soft Tissue Masses by Magnetic Resonance Imaging: Role of Tissue Component Analysis
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DOI：
10.1016/s1726-4901(09)70053-x
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发表时间：
2009-04-01
影响因子：
3
通讯作者：
Chen, Wei-Ming
中科院分区：
文献类型：
--
作者：
Chen, Chun-Ku;Wu, Hung-Ta;Chen, Wei-Ming
背景资料：磁共振成像（MRI）根据信号强度和形态学特征区分软组织肿块的良恶性有不同程度的准确性。方法：分析118例经病理证实的软组织肿块的MRI表现：（1）信号特征：（a）高T1基质;（B）低T2基质;（c）纤维组织信号;（d）钙化;（e）粘液样信号组织;（f）脂肪信号组织;（g）囊性信号;（h）坏死信号;（i）分隔;（j）血管信号空信号;（k）脂肪边缘;和（1）出血;以及根据（2）形态学评估：（a）病变大小（最大直径），单位为厘米（cm）;（B）病变深度，单位为cm;（c）边缘;（d）瘤周水肿;（e）骨受累;（f）边缘包膜或假包膜;和（g）神经血管束受累。单因素和多因素分析，然后逐步Logistic回归的组合的影像学特征进行。结果：单因素分析中，良恶性肿块T2低信号基质、纤维组织、钙化、坏死、间隔、脂肪环征、瘤周水肿、出血等影像学特征的预测价值差异有统计学意义（P < 0. 05），良恶性肿块T2低信号基质、纤维组织、钙化、坏死、间隔、脂肪环征、瘤周水肿、出血等影像学特征的预测价值差异有统计学意义（P < 0. 05）。
Background: There is a variable degree of accuracy in discriminating benign from malignant soft tissue masses based on signal intensity and morphologic characteristics by magnetic resonance imaging (MRI). The aim of this study was to determine the utility of detailed component pattern assessment, in addition to morphologic study, for differentiating benign from malignant soft tissue masses by MRI.Methods: The imaging features of 118 histologically proven soft tissue masses were analyzed according to: (1) signal characteristics: (a) high T1 matrix; (b) low T2 matrix; (c) fibrous tissue signal; (d) calcification; (e) myxoid signal tissue; (f) fatty signal tissue; (g) cystic signal; (h) necrotic signal; (i) septations; (j) vascular signal void signal; (k) fat rim; and (1) hemorrhage; and according to (2) morphologic assessment: (a) lesion size (maximal diameter) in centimeters (cm); (b) lesion depth in cm; (c) margins; (d) peritumoral edema; (e) bone involvement; (f) marginal capsule or pseudocapsule; and (g) neurovascular bundle involvement. Univariate and multivariate analyses followed by stepwise logistic regression of combination of imaging features were performed. The predictive value of each imaging feature and various combinations of imaging features were determined.Results: In univariate analysis, T2 low signal matrix, fibrous tissue, calcification, necrosis, septum, fat rim sign, peritumoral edema, and hemorrhage showed statistically significant differences between benign and malignant masses (p