Tumor grade in soft-tissue sarcoma: Prediction with magnetic resonance imaging texture analysis

软组织肉瘤的肿瘤分级:基于磁共振成像纹理分析的预测

阅读:1

Abstract

To determine the value of 3T magnetic resonance imaging (MRI) texture analysis in differentiating high- from low-grade soft-tissue sarcoma.Forty-two patients with soft-tissue sarcomas who underwent 3T MRI were analyzed. Qualitative and texture analysis were performed on T1-, T2- and fat-suppressed contrast-enhanced (CE) T1-weighted images. Various features of qualitative and texture analysis were compared between high- and low-grade sarcoma. Areas under the receiver operating characteristic curves (AUC) were calculated for texture features. Multivariate logistic regression analysis was used to analyze the value of qualitative and texture analysis.There were 11 low- and 31 high-grade sarcomas. Among qualitative features, signal intensity on T1-weighted images, tumor margin on T2-weighted images, tumor margin on fat-suppressed CE T1-weighted images and peritumoral enhancement were significantly different between high- and low-grade sarcomas. Among texture features, T2 mean, T1 SD, CE T1 skewness, CE T1 mean, CE T1 difference variance and CE T1 contrast were significantly different between high- and low-grade sarcomas. The AUCs of the above texture features were > 0.7: T2 mean, .710 (95% confidence interval [CI] .543-.876); CE T1 mean, .768 (.590-.947); T1 SD, .730 (.554-.906); CE T1 skewness, .751 (.586-.916); CE T1 difference variance, .721 (.536-.907); and CE T1 contrast, .727 (.530-.924). The multivariate logistic regression model of both qualitative and texture features had numerically higher AUC than those of only qualitative or texture features.Texture analysis at 3T MRI may provide additional diagnostic value to the qualitative MRI imaging features for the differentiation of high- and low-grade sarcomas.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。