Quantitative imaging for predicting hematoma expansion in intracerebral hemorrhage: A multimodel comparison.

BACKGROUND: Several studies report that radiomics provides additional information for predicting hematoma expansion in intracerebral hemorrhage (ICH). However, the comparison of diagnostic performance of radiomics for predicting revised hematoma expansion (RHE) remains unclear. METHODS: The cohort comprised 312 consecutive patients with ICH. A total of 1106 radiomics features from seven categories were extracted using Python software. Support vector machines achieved the best performance in both the training and validation datasets. Clinical factors models were constructed to predict RHE. Receiver operating characteristic curve analysis was used to assess the abilities of non-contrast computed tomography (NCCT) signs, radiomics features, and combined models to predict RHE. RESULTS: We finally selected the top 21 features for predicting RHE. After univariate analysis, 4 clinical factors and 5 NCCT signs were selected for inclusion in the prediction models. In the training and validation dataset, radiomics features had a higher predictive value for RHE (AUC = 0.83) than a single NCCT sign and expansion-prone hematoma. The combined prediction model including radiomics features, clinical factors, and NCCT signs achieved higher predictive performances for RHE (AUC = 0.88) than other combined models. CONCLUSIONS: NCCT radiomics features have a good degree of discrimination for predicting RHE in ICH patients. Combined prediction models that include quantitative imaging significantly improve the prediction of RHE, which may assist in the risk stratification of ICH patients for anti-expansion treatments.

SERPINA3: A novel inflammatory biomarker associated with cerebral small vessel disease burden in ischemic stroke.

BACKGROUND AND OBJECTIVE: Inflammation has emerged as a prominent risk factor for cerebral small vessel disease (CSVD). However, the specific association between various inflammatory biomarkers and the development of CSVD remains unclear. Serine proteinase inhibitor A3 (SERPINA3), Matrix metalloproteinase-9 (MMP-9), Tissue inhibitor metalloproteinase-1 (TIMP-1), Monocyte Chemoattractant Protein-1 (MCP-1) are several inflammatory biomarkers that are potentially involved in the development of CSVD. In this present study, we aimed to investigate the relationship between candidate molecules and CSVD features. METHOD: The concentration of each biomarker was measured in 79 acute ischemic stroke patients admitted within 72 h after symptom onset. The associations between blood levels of inflammatory markers and CSVD score were investigated, as well as each CSVD feature, including white matter hyperintensities (WMH), lacunes, and enlarged perivascular spaces (EPVS). RESULTS: The mean age was 69.0 ± 11.8 years, and 65.8% of participants were male. Higher SERPINA3 level (>78.90 ng/mL) was significantly associated with larger WMH volume and higher scores on Fazekas's scale in all three models. Multiple regression analyses revealed the linear association between absolute WMH burden and SERPINA3 level, especially in model 3 (ß = 0.14; 95% confidence interval [CI], 0.04-0.24 ; p = 0.008 ). Restricted cubic spline regression demonstrated a dose-response relationship between SERPINA3 level and larger WMH volume (p nonlineariy = 0.0366 and 0.0378 in model 2 and mode 3, respectively). Using a receiving operating characteristic (ROC) curve, plasma SERPINA3 level of 64.15 ng/mL distinguished WMH >7.8 mL with the highest sensitivity and specificity (75.92% and 60%, respectively, area under curve [AUC] = 0.668, p = 0.0102). No statistically significant relationship has been found between other candidate biomarkers and CSVD features. CONCLUSION: In summary, among four inflammatory biomarkers that we investigated, SERPINA3 level at baseline was associated with WMH severity, which revealed a novel biomarker for CSVD and validated its relationship with inflammation and endothelial dysfunction.