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Multiparametric tissue characterization of brain neoplasms and their recurrence using pattern classification of MR images

 Verma R  ;  Zacharaki EI  ;  Ou Y  ;  Cai H  ;  Chawla S  ;  Lee SK  ;  Melhem ER  ;  Wolf R  ;  Davatzikos C 
 ACADEMIC RADIOLOGY, Vol.15(8) : 966-977, 2008 
Journal Title
Issue Date
Brain Neoplasms/diagnosis* ; Diffusion Magnetic Resonance Imaging ; Gadolinium ; Humans ; Magnetic Resonance Imaging* ; Neoplasm Recurrence, Local
Brain neoplasm ; recurrence ; pattern classification ; magnetic resonance imaging (MRI) ; multiparametric MRI ; diffusion tensor imaging ; computer-aided diagnosis ; tumor segmentation
RATIONALE AND OBJECTIVES: Treatment of brain neoplasms can greatly benefit from better delineation of bulk neoplasm boundary and the extent and degree of more subtle neoplastic infiltration. Magnetic resonance imaging (MRI) is the primary imaging modality for evaluation before and after therapy, typically combining conventional sequences with more advanced techniques such as perfusion-weighted imaging and diffusion tensor imaging (DTI). The purpose of this study is to quantify the multiparametric imaging profile of neoplasms by integrating structural MRI and DTI via statistical image analysis methods to potentially capture complex and subtle tissue characteristics that are not obvious from any individual image or parameter. MATERIALS AND METHODS: Five structural MRI sequences, namely, B0, diffusion-weighted images, fluid-attenuated inversion recovery, T1-weighted, and gadolinium-enhanced T1-weighted, and two scalar maps computed from DTI (ie, fractional anisotropy and apparent diffusion coefficient) are used to create an intensity-based tissue profile. This is incorporated into a nonlinear pattern classification technique to create a multiparametric probabilistic tissue characterization, which is applied to data from 14 patients with newly diagnosed primary high-grade neoplasms who have not received any therapy before imaging. RESULTS: Preliminary results demonstrate that this multiparametric tissue characterization helps to better differentiate among neoplasm, edema, and healthy tissue, and to identify tissue that is likely to progress to neoplasm in the future. This has been validated on expert assessed tissue. CONCLUSION: This approach has potential applications in treatment, aiding computer-assisted surgery by determining the spatial distributions of healthy and neoplastic tissue, as well as in identifying tissue that is relatively more prone to tumor recurrence.
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1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
Yonsei Authors
Lee, Seung Koo(이승구) ORCID logo https://orcid.org/0000-0001-5646-4072
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