The objective of this study was to compare the quantitative radiomics data between malignant mixed Müllerian tumors (MMMTs) and endometrial carcinoma (EC) and identify texture features associated with overall survival (OS). This study included 61 patients (36 with EC and 25 with MMMTs) and analyzed various radiomic features and gray-level co-occurrence matrix (GLCM) features. These variables and patient clinicopathologic characteristics were compared between EC and MMMTs using the Wilcoxon Rank sum and Fisher’s exact test. The area under the curve of the receiving operating characteristics (AUC ROC) was calculated for univariate analysis in predicting EC status. Logistic regression with elastic net regularization was performed for texture feature selection. This study showed that skewness (p= 0.045) and tumor volume (p= 0.007) significantly differed between EC and MMMTs. The range of cluster shade, the angular variance of cluster shade, and the range of the sum of squares variance were significant predictors of EC status (p≤ 0.05). The regularized Cox regression analysis identified the “256 Angular Variance of Energy” texture feature as significantly associated with OS independently of the EC/MMMT grouping (p= 0.004). The volume and texture features of the tumor region may help distinguish between EC and MMMTs and predict patient outcomes.
本研究旨在比较恶性混合性苗勒管肿瘤（MMMTs）与子宫内膜癌（EC）的定量影像组学数据，并识别与总生存期（OS）相关的纹理特征。研究纳入了61例患者（36例EC，25例MMMTs），分析了多种影像组学特征及灰度共生矩阵（GLCM）特征。通过Wilcoxon秩和检验与Fisher精确检验，比较了EC与MMMTs组间上述变量及患者临床病理特征的差异。采用受试者工作特征曲线下面积（AUC ROC）进行单变量分析以预测EC状态，并运用弹性网络正则化的逻辑回归进行纹理特征筛选。结果显示，偏度（p=0.045）与肿瘤体积（p=0.007）在EC与MMMTs间存在显著差异。聚类阴影范围、聚类阴影角度方差及平方和方差范围是预测EC状态的显著指标（p≤0.05）。经正则化Cox回归分析发现，“能量256角度方差”纹理特征与OS显著相关，且独立于EC/MMMT分组（p=0.004）。肿瘤区域的体积与纹理特征可能有助于区分EC与MMMTs，并对患者预后具有预测价值。
肿瘤(癌症)患者之家