Radiomics in the differential diagnosis of glioblastoma under the primary neurooncoimaging conditions

Introduction. According to the 2021 WHO Classification of Tumors of the Central Nervous System (CNS) and the 2023 Clinical Practice Guidelines on the Drug Management of Primary CNS Cancers, the first step of molecular genetic testing to identify the morphological type and malignancy of adult-type diffuse gliomas is the detection of isocitrate dehydrogenase (IDH) mutation status. However, tumor tissue biopsy as the conventional diagnostic standard has a number of limitations that can potentially be mitigated by applying the principles of radiomics to the interpretation of magnetic resonance (MR) images. The aim of our study is to develop a radiomics model for IDH mutation status prediction, which can be applied to primary diagnostic imaging in patients with suspected adult-type diffuse gliomas. Materials and methods. We conducted a retrospective comparative statistical analysis of radiomic features extracted from 46 conventional brain MR images of the patients with adult-type diffuse gliomas and identified IDH mutation status using the Random Forest algorithm of machine learning in combination with various preprocessing methods of the source imaging data and a semi-automated LevelTracing tool used for segmentation of the regions of interest (ROI). Results. The most effective combination of tools for preprocessing, segmentation, and classification was found to be ScaleIntensity, LevelTracing, and Random Forest, respectively. Using this combination, we verified the reliability of six radiomic predictors identified at the previous study stage. These features were all associated with IDH mutation status, and most of them capture texture heterogeneity in the ROIs at the voxel level. We were also able to improve the prognostic performance of our classification model up to AUC = 0.845 ± 0.089 (p 0.05). Conclusion. Based on a small, technically heterogeneous sample of routine MR imaging data, we developed a multiparametric model of IDH mutation status prediction in the patients with adult-type diffuse gliomas. Our conclusion is that relatively uniform preprocessing techniques based on uniform voxel intensity changes, which allow to preserve the structural detail, are feasible in clinical practice. The identified radiomic, likely voxel-based, features reflect the severity of perifocal vasogenic edema and the measure of intratumor morphological heterogeneity. We plan to assess the reproducibility of the study results using similar medical imaging data from open sources and to develop a color mapping technique for the ROIs to facilitate visual interpretation of quantitative radiomic data.