التفاصيل البيبلوغرافية
| العنوان: |
Differentiation Between Benign and Nonbenign Meningiomas by Using Texture Analysis From Multiparametric MRI. |
| المؤلفون: |
Ke, Chao, Chen, Haolin, Lv, Xiaofei, Li, Haojiang, Zhang, Yun, Chen, Maodong, Hu, Daokun, Ruan, Guangying, Zhang, Yu, Zhang, Youming, Liu, Lizhi, Feng, Yanqiu |
| المصدر: |
Journal of Magnetic Resonance Imaging; Jun2020, Vol. 51 Issue 6, p1810-1820, 11p |
| مصطلحات موضوعية: |
RECEIVER operating characteristic curves, TEXTURE analysis (Image processing) |
| الشركة/الكيان: |
WORLD Health Organization |
| مستخلص: |
Background: It is difficult to prospectively differentiate between benign (World Health Organization [WHO] I) and nonbenign (WHO II and III) meningiomas.Purpose: To evaluate the feasibility of preoperative differentiation between benign and nonbenign meningiomas by using texture analysis from multiparametric MR data.Study Type: Retrospective.Subjects: In all, 184 patients with meningioma (139 benign and 45 nonbenign) were included as the training cohort and 79 patients with meningioma (60 benign and 19 nonbenign) were included as the external validation cohort.Field Strength/sequence: T1 -weighted, T2 -weighted, and contrast-enhanced T1 -weighted imaging were performed on 1.5 or 3.0T MR systems from two centers.Assessment: Tumor segmentation and radiological characteristic (RC) evaluation were performed by experienced radiologists. The texture features were extracted from preprocessed images and combined with RCs, and then the combined features were reduced by using a two-step feature selection. Three single-sequence models and a multiparametric MRI (the combination of single sequences) model were constructed and then evaluated with the external validation cohort.Statistical Tests: Area under receiver operating characteristic curve (AUC), accuracy (Acc), f1-score (F1), sensitivity (Sen), and specificity (Spec), were calculated to quantify the performance of the models.Results: Among the four texture models, the multiparametric MRI model demonstrated the best performance for differentiating between benign and nonbenign meningiomas in both the training and external validation cohorts (AUC 0.91, Acc 89%, F1 0.88, Sen 0.93, and Spec 0.87 in the training cohort; AUC 0.83, Acc 80%, F1 0.77, Sen 0.84, and Spec 0.78 in the validation cohort).Data Conclusion: Nonbenign meningiomas might be preoperatively differentiated from benign meningiomas by using texture analysis from multiparametric MR data.Level Of Evidence: 3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;51:1810-1820. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Complementary Index |
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