Prediction of Etiology and Prognosis Based on Hematoma Location of Spontaneous Intracerebral Hemorrhage

The investigators retrospectively collected participants with spontaneous cerebral hemorrhage(sICH) from January 2015 to December 2019 for training and internal validation. Clinical and imaging data were collected. Modified Rankin Scale (mRS) scores were determined good outcome as "mRS = 0-2", poor outcome as "mRS = 3-6". The location features of sICH were extracted by symptom mapping. Noncontrast computed tomography images of patients and hematoma masks were registered with standard human brain templates to identify specific affected brain regions. Then a probability map of hemorrhage for different causes and prognosis is generated. PyRadiomics was used to extract the radiomic features, integrate radiomic and clinical features into multiple logistic regression models, and develop and validate optimal etiological and prognostic models. Further tests were performed in an independent cohort. The area under the working characteristic curve (AUC), sensibility, specificity were used to evaluate the reliability of the model.