Challenges in Glioblastoma Radiomics and the Path to Clinical Implementation

SIMPLE SUMMARY: Glioblastoma (GBM) is a cancer with poor prognosis, its 5-year survival expectation is approximately 5%. Advances in oncologic treatment techniques have not led to significant improvements in survival outcomes for GBM patients. Part of the reason for the treatment failures in GBM patients is that treatments fail to account for heterogeneities both within and between different tumors. Radiomics is a rapidly emerging research field that examines the relationship between medical imaging features and patient clinical outcomes and biological characteristics of tumours. This review outlines the applications of radiomics for GBM patient management and the barriers facing the implementation of radiomics into clinical practice. In completing this review, we hope to inform clinicians and researchers on how radiomics may be used to improve patient clinical outcomes. ABSTRACT: Radiomics is a field of medical imaging analysis that focuses on the extraction of many quantitative imaging features related to shape, intensity and texture. These features are incorporated into models designed to predict important clinical or biological endpoints for patients. Attention for radiomics research has recently grown dramatically due to the increased use of imaging and the availability of large, publicly available imaging datasets. Glioblastoma multiforme (GBM) patients stand to benefit from this emerging research field as radiomics has the potential to assess the biological heterogeneity of the tumour, which contributes significantly to the inefficacy of current standard of care therapy. Radiomics models still require further development before they are implemented clinically in GBM patient management. Challenges relating to the standardisation of the radiomics process and the validation of radiomic models impede the progress of research towards clinical implementation. In this manuscript, we review the current state of radiomics in GBM, and we highlight the barriers to clinical implementation and discuss future validation studies needed to advance radiomics models towards clinical application.