Predicting Hematoma Expansion after Spontaneous Intracranial Hemorrhage Through a Magnetic Resonance-Based Radiomics Model.

Abstract

Intracranial hemorrhage (ICH) is bleeding within the cranium and occurs within the brain tissue, ventricles, and intracranial space. Hematoma expansion following an ICH has been related to increased mortality and morbidity inpatients. To detect ICH patients at risk, machine learning models can be used to predict whether or not hematoma expansion will occur. This study aims to assess the feasibility of machine learning prediction models using a radiomics approach. The highest sensitivity results indicated as 95% confidence intervals are 0.68 ± 0.004 and 0.72 ± 0.004, were achieved by support vector machine and logistic regression classifier models, respectively.