Kim Hyun Su, Kim Jae-Hun, Yoon Young Cheol, Choe Bong Keun
Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, #50 Irwon-dong, Gangnam-gu, Seoul, Republic of Korea.
Department of Preventive Medicine, Medical College, Kyung Hee University, Seoul, Republic of Korea.
PLoS One. 2017 Jul 14;12(7):e0181339. doi: 10.1371/journal.pone.0181339. eCollection 2017.
The objective of this study was to examine the tumor spatial heterogeneity in myxoid-containing soft-tissue tumors (STTs) using texture analysis of diffusion-weighted imaging (DWI). A total of 40 patients with myxoid-containing STTs (23 benign and 17 malignant) were included in this study. The region of interest (ROI) was manually drawn on the apparent diffusion coefficient (ADC) map. For texture analysis, the global (mean, standard deviation, skewness, and kurtosis), regional (intensity variability and size-zone variability), and local features (energy, entropy, correlation, contrast, homogeneity, variance, and maximum probability) were extracted from the ADC map. Student's t-test was used to test the difference between group means. Analysis of covariance (ANCOVA) was performed with adjustments for age, sex, and tumor volume. The receiver operating characteristic (ROC) analysis was performed to compare diagnostic performances. Malignant myxoid-containing STTs had significantly higher kurtosis (P = 0.040), energy (P = 0.034), correlation (P<0.001), and homogeneity (P = 0.003), but significantly lower contrast (P<0.001) and variance (P = 0.001) compared with benign myxoid-containing STTs. Contrast showed the highest area under the curve (AUC = 0.923, P<0.001), sensitivity (94.12%), and specificity (86.96%). Our results reveal the potential utility of texture analysis of ADC maps for differentiating benign and malignant myxoid-containing STTs.
本研究的目的是利用扩散加权成像(DWI)的纹理分析来检测含黏液的软组织肿瘤(STT)中的肿瘤空间异质性。本研究共纳入40例含黏液的STT患者(23例良性和17例恶性)。在表观扩散系数(ADC)图上手动绘制感兴趣区域(ROI)。对于纹理分析,从ADC图中提取全局特征(均值、标准差、偏度和峰度)、区域特征(强度变异性和大小区域变异性)和局部特征(能量、熵、相关性、对比度、均匀性、方差和最大概率)。采用学生t检验来检验组间均值的差异。进行协方差分析(ANCOVA),并对年龄、性别和肿瘤体积进行校正。进行受试者操作特征(ROC)分析以比较诊断性能。与良性含黏液的STT相比,恶性含黏液的STT具有显著更高的峰度(P = 0.040)、能量(P = 0.034)、相关性(P<0.001)和均匀性(P = 0.003),但对比度(P<0.001)和方差(P = 0.001)显著更低。对比度的曲线下面积(AUC)最高(AUC = 0.923,P<0.001),敏感性为94.12%,特异性为86.96%。我们的结果揭示了ADC图纹理分析在鉴别良性和恶性含黏液的STT方面的潜在效用。