Abstract #1975

Prediction of Astrocytoma Pathological Grade Using Radiomics Extracted from Pre-operative Multiparametric MRI

Esra Sümer¹, Ayça Ersen Danyeli^2,3, M. Necmettin Pamir^3,4, Koray Özduman^3,4, Alp Dinçer^3,5, and Esin Ozturk-Isik^1,3

¹Institution of Biomedical Engineering, Boğaziçi University, İstanbul, Turkey, ²Department of Medical Pathology, Acıbadem University, İstanbul, Turkey, ³Brain Tumor Research Group, Acıbadem University, İstanbul, Turkey, ⁴Department of Neurosurgery, Acıbadem University, İstanbul, Turkey, ⁵Department of Radiology, Acıbadem University, İstanbul, Turkey

Synopsis

Keywords: Tumors, Radiomics, astrocytoma, pathological grade, machine learningThe astrocytomas are currently graded from 2 to 4 according to World Health Organization 2021 central nervous system tumor classification. Increasing grade defines increasing malignancy. This study aims to explore the potential of radiomics features extracted from multiparametric pre-operative MRI to predict the grade of astrocytomas. For differentiation of grade 2 from grades 3&4, the FLAIR radiomics were predominantly determined by the feature selection procedure. The highest accuracy for differentiating grade 2 from grades 3&4 was 88.04±0.03% (92.37±0.04% precision and 86.76±0.04% recall).

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