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71. Jahrestagung der Deutschen Gesellschaft für Neurochirurgie (DGNC)
9. Joint Meeting mit der Japanischen Gesellschaft für Neurochirurgie

Deutsche Gesellschaft für Neurochirurgie (DGNC) e. V.

21.06. - 24.06.2020

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Accuracy of radiomics-based feature analysis on multiparametric magnetic resonance images for noninvasive meningioma grading

Multiparametrische Magnetresonanz-Bilder für nichtinvasives Meningiom-Grading

Meeting Abstract

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  • presenting/speaker Marco Timmer - Universitätsklinikum Köln, Klinik für Neurochirurgie, Köln, Deutschland
  • Kai Roman Laukamp - Universitätsklinikum Köln, Radiologie, Köln, Deutschland
  • Georgy Shakirin - Universitätsklinikum Köln, Radiologie, Köln, Deutschland
  • Bettina Baeßler - Universitätsklinikum Köln, Radiologie, Köln, Deutschland
  • Frank Thiele - Universitätsklinikum Köln, Radiologie, Köln, Deutschland
  • David Zopfs - Universitätsklinikum Köln, Radiologie, Köln, Deutschland
  • Nils Hokamp - Universitätsklinikum Köln, Radiologie, Köln, Deutschland
  • Christoph Kabbasch - Universitätsklinikum Köln, Radiologie, Köln, Deutschland
  • Michael Perkuhn - Universitätsklinikum Köln, Radiologie, Köln, Deutschland
  • Jan Borggrefe - Universitätsklinikum Köln, Radiologie, Köln, Deutschland

Deutsche Gesellschaft für Neurochirurgie. 71. Jahrestagung der Deutschen Gesellschaft für Neurochirurgie (DGNC), 9. Joint Meeting mit der Japanischen Gesellschaft für Neurochirurgie. sine loco [digital], 21.-24.06.2020. Düsseldorf: German Medical Science GMS Publishing House; 2020. DocV089

doi: 10.3205/20dgnc092, urn:nbn:de:0183-20dgnc0924

Veröffentlicht: 26. Juni 2020

© 2020 Timmer et al.
Dieser Artikel ist ein Open-Access-Artikel und steht unter den Lizenzbedingungen der Creative Commons Attribution 4.0 License (Namensnennung). Lizenz-Angaben siehe http://creativecommons.org/licenses/by/4.0/.

Gliederung

Text

Objective: Meningioma grading is relevant to therapy decisions in complete or partial resection, observation, and radiotherapy because higher grades are associated with tumor growth and recurrence. The differentiation of low and intermediate grades is particularly challenging. This study attempts to apply radiomics-based shape and texture analysis on routine multiparametric magnetic resonance imaging (MRI) from different scanners and institutions for grading.

Methods: We used MRI data (T1-weighted/T2-weighted, T1-weighted-contrast-enhanced [T1CE], fluid-attenuated inversion recovery [FLAIR], diffusion-weighted imaging [DWI], apparent diffusion coefficient [ADC]) of grade I (n = 46) and grade II (n = 25) nontreated meningiomas with histologic workup. Two experienced radiologists performed manual tumor segmentations on FLAIR, T1CE, and ADC images in consensus. The MRI data were preprocessed through T1CE and T1-subtraction, coregistration, resampling, and normalization. A PyRadiomics package was used to generate 990 shape/texture features. Stepwise dimension reduction and robust radiomics feature selection were performed. Biopsy results were used as standard of reference.

Results: Four statistically independent radiomics features were identified as showing the strongest predictive values for higher tumor grades: roundness-of-FLAIR-shape (area under curve [AUC], 0.80), cluster-shades-of-FLAIR/T1CE-gray-level (AUC, 0.80), DWI/ADC-gray-level-variability (AUC, 0.72), and FLAIR/T1CE-gray-level-energy (AUC, 0.76). In a multivariate logistic regression model, the combination of the features led to an AUC of 0.91 for the differentiation of grade I and grade II meningiomas.

Conclusion: Our results indicate that radiomics-based feature analysis applied on routine MRI is viable for meningioma grading, and a multivariate logistic regression model yielded strong classification performances. More advanced tumor stages are identifiable through certain shape parameters of the lesion, textural patterns in morphologic MRI sequences, and DWI/ADC variability.