Yaltırık Bilgin Ezel, Ünal Özkan, Törenek Şahap, Çiledağ Nazan
Radiology, Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital, Ankara, TUR.
Cureus. 2023 Jul 16;15(7):e41945. doi: 10.7759/cureus.41945. eCollection 2023 Jul.
This study evaluated the differences between arachnoid and epidermoid cysts in computerized tomography (CT) texture analysis (TA).
The study included 12 patients with intracranial epidermoid cysts and 26 patients with intracranial arachnoid cysts who were diagnosed with diffusion-weighted magnetic resonance imaging (DW-MRI) and who had undergone an unenhanced CT examination before treatment. The LIFEx application software was used to obtain texture features. Eighty-two texture features from 38 lesions were automatically calculated for each lesion. The Shapiro-Wilk test was used to test the normality of the scores, and the Mann-Whitney U Test was used to test the difference between the groups. Receiver operating characteristic (ROC) curves and multivariate logistic regression modeling examined the parameters' diagnostic performances.
The median age of the patients was 53 years (range: 19-88 years). Eighty-two texture parameters were evaluated in the first order: gray-level co-occurrence matrix (GLCM), gray-level run length matrix (GLRLM), neighbor gray-tone difference matrix (NGTDM), and gray-level size zone matrix (GLSZM) groups. There was a statistically significant difference between the arachnoid cyst and the epidermoid cyst in the variables of compacity, compactness 1, compactness 2, sphericity, asphericity, sum average, coarseness, and low gray-level zone (p<0.05). According to the multiple logistic regression model, it was determined that the sum average in the GLCM group (B=-0.11; p=0.015), coarseness (B= 869.5; p=0.044) in the NGTDM group, and morphological sphericity (B=24.18; p=0.047) were the radiomics variables that increased the probability of epidermoid diagnosis. According to the classification table of the model, the sensitivity rate was found to be 83%, and the specificity rate was found to be 96%. Therefore, the probability of accurate model classification was 92%.
CT TA is a method that can be applied with high diagnostic accuracy in the differential diagnosis of intracranial epidermoid and arachnoid cysts, especially in patients who cannot undergo an MRI examination.
本研究评估了蛛网膜囊肿和表皮样囊肿在计算机断层扫描（CT）纹理分析（TA）中的差异。
本研究纳入了12例颅内表皮样囊肿患者和26例颅内蛛网膜囊肿患者，这些患者均经扩散加权磁共振成像（DW-MRI）诊断，且在治疗前接受了非增强CT检查。使用LIFEx应用软件获取纹理特征。为每个病灶自动计算38个病灶的82个纹理特征。采用Shapiro-Wilk检验来检验分数的正态性，采用Mann-Whitney U检验来检验组间差异。通过受试者操作特征（ROC）曲线和多变量逻辑回归建模来检验参数的诊断性能。
患者的中位年龄为53岁（范围：19 - 88岁）。在一阶中评估了82个纹理参数：灰度共生矩阵（GLCM）、灰度游程长度矩阵（GLRLM）、邻域灰度差矩阵（NGTDM）和灰度大小区域矩阵（GLSZM）组。蛛网膜囊肿和表皮样囊肿在致密性、致密性1、致密性2、球形度、非球形度、总和平均值、粗糙度和低灰度级区域等变量上存在统计学显著差异（p<0.05）。根据多变量逻辑回归模型，确定GLCM组中的总和平均值（B=-0.11；p=0.015）、NGTDM组中的粗糙度（B=869.5；p=0.044）以及形态球形度（B=24.18；p=0.047）是增加表皮样诊断概率的放射组学变量。根据模型的分类表，发现灵敏度率为83%，特异度率为96%。因此，模型准确分类的概率为92%。
CT TA是一种在颅内表皮样囊肿和蛛网膜囊肿的鉴别诊断中可应用且诊断准确性高的方法，尤其适用于无法进行MRI检查的患者。
