基于MRI的影像组学特征在鉴别沃辛瘤与多形性腺瘤中的应用：使用T2加权和对比增强T1加权MR图像的性能评估
Usefulness of MRI-based radiomic features for distinguishing Warthin tumor from pleomorphic adenoma: performance assessment using T2-weighted and post-contrast T1-weighted MR images.
作者信息
Faggioni Lorenzo, Gabelloni Michela, De Vietro Fabrizio, Frey Jessica, Mendola Vincenzo, Cavallero Diletta, Borgheresi Rita, Tumminello Lorenzo, Shortrede Jorge, Morganti Riccardo, Seccia Veronica, Coppola Francesca, Cioni Dania, Neri Emanuele
机构信息
Academic Radiology, Department of Translational Research, University of Pisa, Via Roma 67, 56126, Pisa, Italy.
Department of Clinical and Experimental Medicine, Section of Statistics, University of Pisa, Via Roma 67, 56126, Pisa, Italy.
出版信息
Eur J Radiol Open. 2022 Jun 18;9:100429. doi: 10.1016/j.ejro.2022.100429. eCollection 2022.
PURPOSE
Differentiating Warthin tumor (WT) from pleomorphic adenoma (PA) is of primary importance due to differences in patient management, treatment and outcome. We sought to evaluate the performance of MRI-based radiomic features in discriminating PA from WT in the preoperative setting.
METHODS
We retrospectively evaluated 81 parotid gland lesions (48 PA and 33 WT) on T2-weighted (T2w) images and 52 of them on post-contrast fat-suppressed T1-weighted (pcfsT1w) images. All MRI examinations were carried out on a 1.5-Tesla MRI scanner, and images were segmented manually using the software ITK-SNAP (www.itk-snap.org).
RESULTS
The most discriminative feature on pcfsT1w images was GLCM_InverseVariance, yielding area under the curve (AUC), sensitivity and specificity of 0.9, 86 % and 87 %, respectively. Skewness was the feature extracted from T2w images with the highest specificity (88 %) in discriminating WT from PA.
CONCLUSION
Radiomic analysis could be an important tool to improve diagnostic accuracy in differentiating PA from WT.
目的
鉴于沃辛瘤（WT）和多形性腺瘤（PA）在患者管理、治疗及预后方面存在差异，鉴别两者至关重要。我们旨在评估基于MRI的影像组学特征在术前鉴别PA与WT中的性能。
方法
我们回顾性评估了81例腮腺病变（48例PA和33例WT）的T2加权（T2w）图像，其中52例还评估了对比剂增强脂肪抑制T1加权（pcfsT1w）图像。所有MRI检查均在1.5特斯拉MRI扫描仪上进行，图像使用ITK-SNAP软件（www.itk-snap.org）进行手动分割。
结果
pcfsT1w图像上最具鉴别力的特征是灰度共生矩阵逆方差（GLCM_InverseVariance），曲线下面积（AUC）、灵敏度和特异度分别为0.9、86%和87%。偏度是从T2w图像中提取的在鉴别WT与PA时特异度最高（88%）的特征。
结论
影像组学分析可能是提高鉴别PA与WT诊断准确性的重要工具。
Zotero 插件