Ito Kotaro, Muraoka Hirotaka, Hirahara Naohisa, Sawada Eri, Tokunaga Satoshi, Kaneda Takashi
Nihon University School of Dentistry at Matsudo, Matsudo, Chiba, Japan.
Pol J Radiol. 2022 Sep 5;87:e494-e499. doi: 10.5114/pjr.2022.119473. eCollection 2022.
It is challenging for radiologists to distinguish between venous malformations (VMs) and lymphatic malformations (LMs) using magnetic resonance imaging (MRI). Thus, this study aimed to differentiate VMs from LMs using non-contrast-enhanced MRI texture analysis.
This retrospective case-control study included 12 LM patients (6 men and 6 women; mean age 43.58, range 7-85 years) and 29 VM patients (7 men and 22 women; mean age 53.10, range 19-76 years) who underwent MRI for suspected vascular malformations. LM and VM patients were identified by histopathological examination of tissues excised during surgery. The texture features of VM and LM were analysed using the open-access software MaZda version 3.3. Seventeen texture features were selected using the Fisher and probability of error and average correlation coefficient methods in MaZda from 279 original parameters calculated for VM and LM.
Among 17 selected texture features, the patients with LM and VM revealed significant differences in 1 histogram feature, 8 grey-level co-occurrence matrix (GLCM) features, and 1 grey-level run-length matrix feature. At the cut-off values of the histogram feature [skewness ≤ -0.131], and the GLCM features [S(0, 2) correlation ≥ 0.667, S(0, 3) correlation ≥ 0.451, S(0, 4) correlation ≥ 0.276, S(0, 5) correlation ≥ 0.389, S(1, 1) correlation ≥ 0.739, S(2, 2) correlation ≥ 0.446, S(2, -2) correlation ≥ 0.299, S(3, -3) correlation ≥ 0.091] had area under the curves of 0.724, 0.764, 0.773, 0.747, 0.733, 0.759, 0.730, 0.744 and 0.727, respectively.
Non-contrast-enhanced MRI texture analysis allows us to differentiate between LMs and VMs.
放射科医生利用磁共振成像（MRI）区分静脉畸形（VMs）和淋巴管畸形（LMs）具有挑战性。因此，本研究旨在通过非增强MRI纹理分析来区分VMs和LMs。
这项回顾性病例对照研究纳入了12例LM患者（6例男性和6例女性；平均年龄43.58岁，范围7 - 85岁）和29例VM患者（7例男性和22例女性；平均年龄53.10岁，范围19 - 76岁），这些患者因疑似血管畸形接受了MRI检查。通过手术切除组织的组织病理学检查来确定LM和VM患者。使用开源软件MaZda 3.3版分析VM和LM的纹理特征。在MaZda中，从为VM和LM计算的279个原始参数中，采用Fisher法、错误概率法和平均相关系数法选择了17个纹理特征。
在所选的17个纹理特征中，LM和VM患者在1个直方图特征、8个灰度共生矩阵（GLCM）特征和1个灰度游程长度矩阵特征上存在显著差异。在直方图特征[偏度≤ - 0.131]以及GLCM特征[S(0, 2)相关性≥ 0.667、S(0, 3)相关性≥ 0.451、S(0, 4)相关性≥ 0.276、S(0, 5)相关性≥ 0.389、S(1, 1)相关性≥ 0.739、S(2, 2)相关性≥ 0.446、S(2, - 2)相关性≥ 0.299、S(3, - 3)相关性≥ 0.091]的截断值下，曲线下面积分别为0.724、0.764、0.773、0.747、0.733、0.759、0.730、0.744和0.727。
非增强MRI纹理分析使我们能够区分LMs和VMs。
