Radiomics in glioblastoma: current status, challenges and potential opportunities

Gliomas are tumors which develop in the brain, the most aggressive of which is glioblastoma multiforme (GBM). Despite extensive research to better understand the underlying biology of GBM and advancements in the treatment of this disease, it has an extremely poor prognosis. Poor outcomes in GBM are due to its molecular and clinical heterogeneity, as in other solid tumors. As imaging approaches have been taken to comprehensively characterize tumors, radiomics has emerged as the concept of extracting quantitative radiologic features and drawing associations with clinical outcomes. Radiomics has the potential to improve the predictive ability of radiological datasets. Tumor radiographs are segmented through manual, semi-automated or fully-automated procedures. Segmentation is followed by feature extraction from the tumor volume. Data analysis and predictive modeling are used to relate image-derived features with clinical outcomes. Substantial progress has already been made in solving many of the technical hurdles inherent in the radiomics process. Advances in sequencing, gene expression profiling and machine learning have increased the resolution of datasets and improved the sensitivity and specificity of computational methods used to analyze them. Numerous logistical, computational and clinical challenges remain to unlocking the full potential of the radiomics approach. To make the approach useful in clinical practice, improved statistical models are needed which relate GBM imaging features with patient outcomes with high specificity/sensitivity. More studies correlating radiomic features with disease outcomes and molecular attributes are also needed to illuminate the tumor biology which gives rise to imaging features and underlie response to therapy.