Non-traumatic spontaneous intracerebral hemorrhage (ICH) is the most deadly stroke subtype with a 30-day mortality of 40%. ICH volume is an important predictor of early neurological deterioration and functional outcome and in one-third of patients the hemorrhage expands further, particularly in the first hours. Furthermore, secondary injury due to the development of perihematomal edema (PHE) contributes to disability and mortality. Prevention of early hematoma growth has become an important treatment target, although medical therapies have not been proven effective so far. ICH and PHE volumes are therefore important imaging biomarkers for patient stratification, treatment monitoring and outcome prediction. However, current quantification methods are mainly based on visual estimations or over-simplified assumptions, such as the ABC method, and are laborious, prone to observer variability and inaccurate. In addition, no method exists for PHE quantification in non-contrast CT (NCCT), and no model exists for longitudinal analysis.
The data in this project comes from the DUTCH ICH Surgery Trial (DIST) in which multiple 3D NCCTs are acquired of the patient over time. DIST investigates the effectiveness of minimally-invasive endoscopy-guided surgery for spontaneous ICH and the potential effect on patients’ functional outcome. In this study, neuro-imaging will be performed at multiple time points to assess, among others, ICH and PHE volumes. DIST is a part of the Collaboration for New Treatments of Acute Stroke (CONTRAST) consortium which is a collaboration of clinical and translational scientists from all academic and large clinical centers who want to act together to improve the treatment of acute stroke.
The RTC Deep Learning is developing a web-based application for the accurate segmentation and measurement of ICH and PHE on NCCT.