Abstract
To introduce a standardized method for identification of regions of interest when analyzing MR images using radiomics; to test the hypothesis that this approach is effective for distinguishing different histological types of gliomas. We analyzed preoperative MR data in 83 adults with various gliomas (WHO classification, 2016), i.e. oligodendroglioma, anaplastic oligodendroglioma, anaplastic astrocytoma, and glioblastoma. Radiomic features were computed for T1, T1-enhanced, T2 and T2-FLAIR modalities in four standardized volumetric regions of interest by 356 voxels (46.93 mm3): 1) contrast enhancement; 2) edema-infiltration; 3) area adjacent to edema-infiltration; 4) reference area in contralateral hemisphere. Subsequently, mathematical models were trained to classify MR-images of glioma depending on histological type and quantitative features. Mean accuracy of differential diagnosis of 4 histological types of gliomas in experiments with machine learning was 81.6%, mean accuracy of identification of tumor types - from 94.1% to 99.5%. The best results were obtained using support vector machines and random forest model. In a pilot study, the proposed standardization of regions of interest demonstrated high effectiveness for MR-based differential diagnosis of oligodendroglioma, anaplastic oligodendroglioma, anaplastic astrocytoma and glioblastoma. There are grounds for applying and improving this methodology in further studies.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call