The Application of DTCWT on MRI-Derived Radiomics for Differentiation of Glioblastoma and Solitary Brain Metastases

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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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