Regional CT scan features predict hematoma growth after brain hemorrhage
Regional prediction of hematoma expansion from non-contrast computed tomography in spontaneous intracerebral hemorrhage patients
Computer Vision and Pattern Recognition
Summary
When someone has bleeding inside the brain, doctors want to know if the blood clot will get bigger, which can cause more problems. The paper shows a way to use pictures from a CT scan to predict this growth by looking at specific parts of the brain and the shape and density of the clot. The authors used a math method that turns these image features into numbers a computer can use to guess if the clot will expand. This method worked better than other tools when tested on different groups of patients. It helps doctors better predict and understand bleeding growth in the brain.
What this means in practice
- •For stroke care teams: Use regional CT image analysis to identify patients likely to have hematoma growth, improving decisions about monitoring and treatment after brain hemorrhages.
- •For medical imaging developers: Incorporate regional transport-based morphometry features into software tools to better predict hematoma expansion from standard CT scans.$Commercial implications: Enables development of diagnostic imaging products that provide quantitative risk scores for hematoma growth to hospitals and healthcare providers.
Authors
Natasha Ironside, Hongyu Kan, Kristofor Pas, Mohammad Shifat-E-Rabbi, Benjamin Newman, Shinjini Kundu, Kareem El Naamani, Tanvir Rizvi, Andrea Becerril-Gaitan, Harrison Snyder, Aarathi Manchikalapudi, Sarah Ratcliffe, Stacie L. Demel, Carl D. Langefeld, Daniel Woo, Stephan Mayer, E. Sander Connolly, Gustavo Rohde, for the ERICH Investigators, the VISTA-ICH collaboration
Abstract
Background The combination of hematoma location, expansion and final volume influence neurological outcome. The aim of this study was to derive and validate a quantitative and interpretable region-based NCCT image feature model for predicting hematoma expansion. Methods The Ethnic/Racial Variations of Intracerebral Hemorrhage(ERICH) study was used for derivation and the Virtual International Trials in Stroke Archive(VISTA) was used for validation. NCCT images at presentation and 24+/-6hours were pre-processed, segmented and categorized into lobar, basal ganglia or thalamic regions. Hematoma expansion was defined by region and according to its effect on clinical outcome. Transport-based morphometry (TBM) produced a unique transport-based representation from each NCCT hematoma image. Linear classifiers applied to transport representations were used to derive a predictive model. The regional TBM model's performance was compared to alternate methods using AUROC analyses. Results The derivation dataset included 574 patients, comprising 162 lobar, 257 basal ganglia and 155 thalamic hematomas. The validation dataset included 196 patients, comprising 31 lobar, 119 basal ganglia and 46 thalamic hematomas. A combined regional model predicted hematoma expansion in the validation dataset with an AUROC of 0.787. The principal image features which varied by location were size, heterogeneity, eccentricity and density distribution towards the hematoma periphery. The regional TBM model outperformed alternative clinician based and machine-learning methods. Conclusions TBM discriminated and visualized regional features of hematoma expansion from presentation NCCT scans. This provides preliminary evidence for regional radiographic features of hematoma expansion. It has the potential to improve hematoma expansion prediction and understanding of hematoma expansion pathophysiology.