Radiomics and machine learning may accurately predict the grade and histological subtype in meningiomas using conventional and diffusion tensor imaging | Zendy

Yae Won Park | Zendy; Jongmin Oh | Zendy; Seng Chan You | Zendy; Kyunghwa Han | Zendy; Sung Soo Ahn | Zendy; Yoon Seong Choi | Zendy; Jong Hee Chang | Zendy; Se Hoon Kim | Zendy; SeungKoo Lee | Zendy

Open Access

Radiomics and machine learning may accurately predict the grade and histological subtype in meningiomas using conventional and diffusion tensor imaging

Author(s) -

Yae Won Park,

Jongmin Oh,

Seng Chan You,

Kyunghwa Han,

Sung Soo Ahn,

Yoon Seong Choi,

Jong Hee Chang,

Se Hoon Kim,

SeungKoo Lee

Publication year - 2018

Publication title -

european radiology

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.606

H-Index - 149

eISSN - 1432-1084

pISSN - 0938-7994

DOI - 10.1007/s00330-018-5830-3

Subject(s) - diffusion mri , radiomics , neuroradiology , medicine , interventional radiology , radiology , magnetic resonance imaging , artificial intelligence , medical physics , neurology , computer science , psychiatry

Preoperative, noninvasive prediction of the meningioma grade is important because it influences the treatment strategy. The purpose of this study was to evaluate the role of radiomics features of postcontrast T1-weighted images (T1C), apparent diffusion coefficient (ADC), and fractional anisotropy (FA) maps, based on the entire tumor volume, in the differentiation of grades and histological subtypes of meningiomas.

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