A Radiomics Approach to Traumatic Brain Injury Prediction in CT Scans
This work addresses the need for automated lesion classification in acute traumatic brain injury, offering a potential tool to assist radiologists, though it appears incremental as it applies existing radiomics methods to this specific domain.
The paper tackled the problem of predicting traumatic brain injury lesions from CT scans by using radiomics descriptors, achieving 89.7% accuracy on unseen test data.
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.