Radiomics and machine learning may accurately predict the grade and histological subtype in meningiomas using conventional and diffusion tensor imaging
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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.
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).
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.
Radiomics feature-based machine learning classifiers of T1C images, ADC, and FA maps are useful for differentiating meningioma grades.
• 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.
KeywordsDiffusion tensor imaging Magnetic resonance imaging Meningioma Radiomics
Apparent diffusion coefficient
Area under the curve
Diffusion tensor imaging
Postcontrast T1-weighted image
This research received funding from the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, Information and Communication Technologies, and Future Planning (2017R1D1A1B03030440).
Compliance with ethical standards
The scientific guarantor of this publication is Professor Seung-Koo Lee, MD, PhD, from Yonsei University College of Medicine (firstname.lastname@example.org).
Conflict of interest
The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.
Statistics and biometry
One of the authors has significant statistical expertise (J.M.O., a biostatistician with 5 years of experience in computational biology).
The institutional review board waived the requirement to obtain informed patient consent for this retrospective study.
Institutional Review Board approval was obtained.
• Diagnostic or prognostic study
• Performed at one institution
- 30.Provost F (2000) Machine learning from imbalanced data sets 101 proceedings of the AAAI’2000 workshop on imbalanced data sets, pp 1–3Google Scholar
- 31.Kuhn M (2008) Building predictive models in R using the caret package Caret package. J Stat Softw 28:1–26Google Scholar
- 32.Lunardon N, Menardi G, Torelli N (2014) ROSE: a package for binary imbalanced learning. R Journal 6(1)Google Scholar
- 33.Torgo L (2013) Package ‘DMwR’. Comprehensive R Archive NetworkGoogle Scholar
- 47.New PF, Hesselink JR, O'Carroll CP, Kleinman GM (1982) Malignant meningiomas: CT and histologic criteria, including a new CT sign. AJNR Am J Neuroradiol 3:267–276Google Scholar
- 54.Kleihues P, Cavenee WK (2000) Pathology and genetics of tumours of the nervous system, vol 1. International Agency for Research on Cancer, LyonGoogle Scholar