Chia Nan University of Pharmacy & Science Institutional Repository:Item 310902800/34634
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    標題: The Application of DTCWT on MRI-Derived Radiomics for Differentiation of Glioblastoma and Solitary Brain Metastases
    作者: Wu, Wen-Feng
    Shen, Chia-Wei
    Lai, Kuan-Ming
    Chen, Yi-Jen
    Lin, Eugene C.
    Chen, Chien-Chin
    貢獻者: National Chung Cheng University
    Central Taiwan University Science & Technology
    Department of Cosmetic Science, Chia Nan University of Pharmacy & Science
    National Cheng Kung University
    關鍵字: european association
    texture analysis
    diffusion
    multiforme
    diagnosis
    perfusion
    tumors
    日期: 2022
    上傳時間: 2023-12-11 14:01:45 (UTC+8)
    出版者: MDPI
    摘要: Background: While magnetic resonance imaging (MRI) is the imaging modality of choice for the evaluation of patients with brain tumors, it may still be challenging to differentiate glioblastoma multiforme (GBM) from solitary brain metastasis (SBM) due to their similar imaging features. This study aimed to evaluate the features extracted of dual-tree complex wavelet transform (DTCWT) from routine MRI protocol for preoperative differentiation of glioblastoma (GBM) and solitary brain metastasis (SBM). Methods: A total of 51 patients were recruited, including 27 GBM and 24 SBM patients. Their contrast-enhanced T1-weighted images (CET1WIs), T2 fluid-attenuated inversion recovery (T2FLAIR) images, diffusion-weighted images (DWIs), and apparent diffusion coefficient (ADC) images were employed in this study. The statistical features of the pre-transformed images and the decomposed images of the wavelet transform and DTCWT were utilized to distinguish between GBM and SBM. Results: The support vector machine (SVM) showed that DTCWT images have a better accuracy (82.35%), sensitivity (77.78%), specificity (87.50%), and the area under the curve of the receiver operating characteristic curve (AUC) (89.20%) than the pre-transformed and conventional wavelet transform images. By incorporating DTCWT and pre-transformed images, the accuracy (86.27%), sensitivity (81.48%), specificity (91.67%), and AUC (93.06%) were further improved. Conclusions: Our studies suggest that the features extracted from the DTCWT images can potentially improve the differentiation between GBM and SBM.
    關聯: JOURNAL OF PERSONALIZED MEDICINE, v.12, n.8, 1276
    显示于类别:[化妝品應用與管理系(所)] 期刊論文

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