Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States. Department of Otorhinolaryngology, University Hospital of Ludwig Maximilians Universität München, Munich, Germany. Zeenat Qureshi Stroke Institute and Department of Neurology, University of Missouri, Columbia, MO, United States. Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY, United States. Department of Radiology, Tufts University School of Medicine, Boston, MA, United States. Department of Neurology, Yale School of Medicine, New Haven, CT, United States.
BACKGROUND Radiomics provides a framework for automated extraction of high-dimensional feature sets from medical images. We aimed to determine radiomics signature correlates of admission clinical severity and medium-term outcome from intracerebral hemorrhage (ICH) lesions on baseline head CTs. METHODS We used the ATACH-2 (Antihypertensive-Treatment-of-Acute-Cerebral-Hemorrhage-II) trial dataset. Patients included in this analysis (n=895) were randomly allocated to discovery (n=448) and independent
validation (n=447) cohorts. We extracted 1130 radiomics features from hematoma lesions on baseline non-contrast head CTs and generated radiomics signatures associated with admission Glasgow Coma Scale (GCS), admission National Institutes of Health Stroke Scale (NIHSS), and 3-month modified Rankin Scale (mRS) scores. Spearman's correlation between radiomics signatures and corresponding target variables was compared with hematoma volume. RESULTS In the discovery cohort, radiomics signatures - compared to ICH volume - had significantly stronger association with admission GCS (0.47 vs 0.44, p=0.008), admission NIHSS (0.69 vs 0.57, p<0.001), and 3-month mRS scores (0.44 vs 0.32, p<0.001). Similarly, in independent validation, radiomics signatures - compared to ICH volume - had significantly stronger association with admission GCS (0.43 vs 0.41, p=0.02), NIHSS (0.64 vs 0.56, p<0.001), and 3-month mRS scores (0.43 vs 0.33, p<0.001). In multiple regression analysis adjusted for known predictors of ICH outcome, the radiomics signature was an independent predictor of 3-month mRS in both cohorts. CONCLUSIONS Limited by the enrollment criteria of the ATACH-2 trial, we showed that radiomics features quantifying hematoma texture, density and shape on baseline CT can provide imaging correlates for clinical presentation and medium-term outcome.