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

• Published in: European radiology 2019-08, Vol.29 (8), p.4068-4076
• Main Authors: 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
• Format: Article
• Language: English
• Subjects: Aged; Algorithms; Anisotropy; Artificial intelligence; Benign; Brain cancer; Classification; Classifiers; Confidence intervals; Diagnostic Radiology; Differentiation; Diffusion; Diffusion coefficient; Diffusion Tensor Imaging - methods; Female; Humans; Imaging; Internal Medicine; Interventional Radiology; Learning algorithms; Machine Learning; Magnetic resonance imaging; Male; Mathematical models; Medicine; Medicine & Public Health; Meningeal Neoplasms - diagnostic imaging; Meningeal Neoplasms - pathology; Meningioma; Meningioma - diagnostic imaging; Meningioma - pathology; Middle Aged; Model testing; Neoplasm Grading; Neuro; Neuroradiology; Parameters; Patients; Radiology; Radiomics; Reproducibility of Results; Retrospective Studies; Sensitivity and Specificity; Tensors; Texture; Tumors; Ultrasound
• ISSN: 0938-7994; 1432-1084
• DOI: 10.1007/s00330-018-5830-3

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.