Definition and Intelligent Extraction of Texture Features of Vestibular Schwannoma Based on MRI Imaging

Authors

DOI:

https://doi.org/10.18372/1990-5548.86.20626

Keywords:

vestibular schwannoma, magnetic resonance imaging, radiomics, gray level co-occurrence matrix, gray level size zone matrix, wavelet, Random Forest, PyRadiomics

Abstract

The scientific work is devoted to the development of a method for intelligent extraction of textural features of vestibular schwannomas based on magnetic resonance imaging images for predicting tumor growth. The VS-MC-RC2 dataset was analyzed (421 timepoints, 189 patients, 1990–1999). The ML dataset consists of 211 samples (74 growing, 137 stable, imbalance 1.85:1). Gray Level Co-occurrence Matrix and Gray Level Size Zone Matrix matrices, shape features, wavelet transform, and the PyRadiomics v3.0.1 library were used to extract features from T1C images (priority) and T1 images (fallback) with the following parameters: bins = 32, δ = 1 voxel, 13 3D directions. Model v2 (107 original features) achieved an AUC of 0.618. Model v3 (851 features + 8 wavelet decompositions) achieved an AUC of 0.712 (+15.2%). Validation was performed using 10-fold cross-validation with an 80/20 train/test split. Among the top 15 features, 73% were wavelet features (LHH, LLH, HLH). The best feature, original_glszm_ZoneEntropy (F = 12.67, threshold = 4.51), correlates with the Antoni A/B tissue ratio and the proliferative activity of the tumor.

Author Biographies

Victor Sineglazov , State University "Kyiv Aviation Institute"

Doctor of Engineering Science

Professor

Head of the Department Aviation Computer-Integrated Complexes

Faculty of Air Navigation Electronics and Telecommunications

Maksym Shevchenko , State University "Kyiv Aviation Institute"

Postgraduate Student

Aviation Computer-Integrated Complexes Department

Faculty of Air Navigation Electronics and Telecommunications

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Published

2025-12-19

Issue

Vol. 4 No. 86 (2025)

Section

AUTOMATION AND COMPUTER-INTEGRATED TECHNOLOGIES

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