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

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Title:	The Application of DTCWT on MRI-Derived Radiomics for Differentiation of Glioblastoma and Solitary Brain Metastases
Authors:	Wu, Wen-Feng Shen, Chia-Wei Lai, Kuan-Ming Chen, Yi-Jen Lin, Eugene C. Chen, Chien-Chin
Contributors:	National Chung Cheng University Central Taiwan University Science & Technology Department of Cosmetic Science, Chia Nan University of Pharmacy & Science National Cheng Kung University
Keywords:	european association texture analysis diffusion multiforme diagnosis perfusion tumors
Date:	2022
Issue Date:	2023-12-11 14:01:45 (UTC+8)
Publisher:	MDPI
Abstract:	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.
Relation:	JOURNAL OF PERSONALIZED MEDICINE, v.12, n.8, 1276
Appears in Collections:	[Dept. of Cosmetic Science and institute of cosmetic science] Periodical Articles

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