  Computer Tomography (CT) is the gold standard technique for brain damage
evaluation after acute Traumatic Brain Injury (TBI). 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 TBI lesions by using CT-derived radiomics 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
build 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 decision making. These results open new possibilities for
radiomics-inspired brain lesion detection, segmentation and prediction.
