Background and Purpose— Volumes of hemorrhage and perihematomal edema (PHE) are well-established biomarkers of primary and secondary injury, respectively, in spontaneous intracerebral hemorrhage. An automated imaging pipeline capable of accurately and rapidly quantifying these biomarkers would facilitate large cohort studies evaluating underlying mechanisms of injury. Methods— Regions of hemorrhage and PHE were manually delineated on computed tomography scans of patients enrolled in 2 intracerebral hemorrhage studies. Manual ground-truth masks from the first cohort were used to train a fully convolutional neural network to segment images into hemorrhage and PHE. The primary outcome was automated-versus-human concordance in hemorrhage and PHE volumes. The secondary outcome was voxel-by-voxel overlap of segmentations, quantified by the Dice similarity coefficient (DSC). Algorithm performance was validated on 84 scans from the second study. Results— Two hundred twenty-four scans from 124 patients with supratentorial intracerebral hemorrhage were used for algorithm derivation. Median volumes were 18 mL (interquartile range, 8–43) for hemorrhage and 12 mL (interquartile range, 5–30) for PHE. Concordance was excellent (0.96) for automated quantification of hemorrhage and good (0.81) for PHE, with DSC of 0.90 (interquartile range, 0.85–0.93) and 0.54 (0.39–0.65), respectively. External validation confirmed algorithm accuracy for hemorrhage (concordance 0.98, DSC 0.90) and PHE (concordance 0.90, DSC 0.55). This was comparable with the consistency observed between 2 human raters (DSC 0.90 for hemorrhage, 0.57 for PHE). Conclusions— We have developed a deep learning-based imaging algorithm capable of accurately measuring hemorrhage and PHE volumes. Rapid and consistent automated biomarker quantification may accelerate powerful and precise studies of disease biology in large cohorts of intracerebral hemorrhage patients.

Objective Observational studies point to an inverse correlation between low‐density lipoprotein (LDL) cholesterol levels and risk of intracerebral hemorrhage (ICH), but it remains unclear whether this association is causal. We tested the hypothesis that genetically elevated LDL is associated with reduced risk of ICH. Methods We constructed one polygenic risk score (PRS) per lipid trait (total cholesterol, LDL, high‐density lipoprotein [HDL], and triglycerides) using independent genomewide significant single nucleotide polymorphisms (SNPs) for each trait. We used data from 316,428 individuals enrolled in the UK Biobank to estimate the effect of each PRS on its corresponding trait, and data from 1,286 ICH cases and 1,261 matched controls to estimate the effect of each PRS on ICH risk. We used these estimates to conduct Mendelian Randomization (MR) analyses. Results We identified 410, 339, 393, and 317 lipid‐related SNPs for total cholesterol, LDL, HDL, and triglycerides, respectively. All four PRSs were strongly associated with their corresponding trait (all p < 1.00 × 10‐100). While one SD increase in the PRSs for total cholesterol (odds ratio [OR] = 0.92; 95% confidence interval [CI] = 0.85–0.99; p = 0.03) and LDL cholesterol (OR = 0.88; 95% CI = 0.81–0.95; p = 0.002) were inversely associated with ICH risk, no significant associations were found for HDL and triglycerides (both p > 0.05). MR analyses indicated that 1mmol/L (38.67mg/dL) increase of genetically instrumented total and LDL cholesterol were associated with 23% (OR = 0.77; 95% CI = 0.65–0.98; p = 0.03) and 41% lower risks of ICH (OR = 0.59; 95% CI = 0.42–0.82; p = 0.002), respectively. Interpretation Genetically elevated LDL levels were associated with lower risk of ICH, providing support for a potential causal role of LDL cholesterol in ICH. ANN NEUROL 2020 ANN NEUROL 2020;88:56–66

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