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

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Abstract

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.

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Abbreviations

ADC:

Apparent diffusion coefficient

AUC:

Area under the curve

DTI:

Diffusion tensor imaging

FA:

Fractional anisotropy

T1C:

Postcontrast T1-weighted image

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Funding

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).

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Authors and Affiliations

  1. Department of Radiology, Ewha Womans University College of Medicine, Seoul, South Korea

    Yae Won Park

  2. Department of Radiology and Research Institute of Radiological Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, South Korea

    Yae Won Park, Kyunghwa Han, Sung Soo Ahn, Yoon Seong Choi & Seung-Koo Lee

  3. Department of Convergence Medicine, Ewha Womans University College of Medicine, Seoul, South Korea

    Jongmin Oh

  4. Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, South Korea

    Seng Chan You

  5. Department of Neurosurgery, Yonsei University College of Medicine, Seoul, South Korea

    Jong Hee Chang

  6. Department of Pathology, Yonsei University College of Medicine, Seoul, South Korea

    Se Hoon Kim

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  1. Yae Won Park
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Corresponding author

Correspondence to Sung Soo Ahn.

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Guarantor

The scientific guarantor of this publication is Professor Seung-Koo Lee, MD, PhD, from Yonsei University College of Medicine (slee@yuhs.ac).

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).

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The institutional review board waived the requirement to obtain informed patient consent for this retrospective study.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• Retrospective

• Diagnostic or prognostic study

• Performed at one institution

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Park, Y.W., Oh, J., You, S.C. et al. Radiomics and machine learning may accurately predict the grade and histological subtype in meningiomas using conventional and diffusion tensor imaging. Eur Radiol 29, 4068–4076 (2019). https://doi.org/10.1007/s00330-018-5830-3

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  • DOI: https://doi.org/10.1007/s00330-018-5830-3

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