A RADIOMICS APPROACH TO TRAUMATIC BRAIN INJURY PREDICTION IN CT SCANS

Computer Tomography (CT) is the gold standard technique for brain damage evaluation after acute Traumatic Brain Injury (TM). It allows identification of most lesion types and determines the need of surgical or alternative therapeutic procedures. However, the traditional approach for lesion classification is restricted to visual image inspection. In this work, we characterize and predict lesions by using CT derived radiornics descriptors. Relevant shape, intensity and texture biomarkers characterizing the different lesions are isolated and a lesion predictive model is built by using Partial Least Squares. On a dataset containing 155 scans (105 train, 50 test) the methodology achieved 89.7% accuracy over the unseen data. When a model was built using only texture features, a 88.2% accuracy was obtained. Our results suggest that selected radiomics descriptors could play a key role in brain injury prediction. Besides, the proposed methodology is close to reproduce radiologists lesion labelling. These results open new possibilities for radiomics-inspired brain lesion detection, segmentation and prediction.