Radiomics and machine learning may accurately predict the grade and histological subtype in meningiomas using conventional and diffusion tensor imaging.
Objectives: 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...
| Published in: | European Radiology Vol. 29; no. 8; pp. 4068 - 4077 |
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| Main Authors: | , , , , , , , , |
| Format: | diagnostic images pictorial research tables/charts Journal Article |
| Published: |
Springer Nature
Aug2019
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=137304092&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 137304092 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Aug2019 vid: 29 iid: 8 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 137304092 137304092 NLM30443758 137304092 10.1007/s00330-018-5830-3 NLM30443758 137304092 ppf: 4068 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Radiomics and machine learning may accurately predict the grade and histological subtype in meningiomas using conventional and diffusion tensor imaging. aug: au: Park, Yae Won Oh, Jongmin You, Seng Chan Han, Kyunghwa Ahn, Sung Soo Choi, Yoon Seong Chang, Jong Hee Kim, Se Hoon Lee, Seung-Koo affil: Department of Radiology, Ewha Womans University College of Medicine, Seoul, South Korea sug: subj: Meningioma Magnetic Resonance Imaging Methods Meningioma Pathology Meningeal Neoplasms Meningeal Neoplasms Pathology Aged Algorithms Middle Age Retrospective Design Reproducibility of Results Sensitivity and Specificity Neoplasm Grading Female Male Human Funding Source Aged: 65+ years Middle Aged: 45-64 years Female Male ab: Objectives: 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.Methods: One hundred thirty-six patients with pathologically diagnosed meningiomas (108 low-grade [benign], 28 high-grade [atypical and anaplastic]), who underwent T1C and diffusion tensor imaging, were included in the discovery set. The T1C image, ADC, and FA maps were analyzed to derive volume-based data of the entire tumor. Radiomics features were correlated with meningioma grades and histological subtypes. Various machine learning classifiers were trained to build classification models to predict meningioma grades. We tested the model in a validation set (58 patients; 46 low-grade; 12 high-grade).Results: The machine learning classifiers showed variable performances depending on the machine learning algorithms. The best classification system for the prediction of meningioma grades had an area under the curve of 0.86 (95% confidence interval [CI], 0.74-0.98) in the validation set. The accuracy, sensitivity, and specificity of the best classifier were 89.7, 75.0, and 93.5% in the validation set, respectively. Various texture parameters differed significantly between fibroblastic and non-fibroblastic subtypes.Conclusions: Radiomics feature-based machine learning classifiers of T1C images, ADC, and FA maps are useful for differentiating meningioma grades.Key Points: • Preoperative, noninvasive differentiation of the meningioma grade is important because it influences the treatment strategy. • Radiomics feature-based machine learning classifiers of T1C images, ADC, and FA maps are useful for differentiating meningioma grades. • In benign meningiomas, there were significant differences in the various texture parameters between fibroblastic and non-fibroblastic meningioma subtypes. pubtype: Academic Journal doctype: diagnostic images pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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