Quantitative parameters of CT texture analysis as potential markersfor early prediction of spontaneous intracranial hemorrhage enlargement.

<bold>Objective: </bold>To objectively quantify intracranial hematoma (ICH) enlargement by analysing the image texture of head CT scans and to provide objective and quantitative imaging parameters for predicting early hematoma enlargement.<bold>Methods: </bold>We retrospectively studied 108 ICH patients with baseline non-contrast computed tomography (NCCT) and 24-h follow-up CT available. Image data were assessed by a chief radiologist and a resident radiologist. Consistency analysis between observers was tested. The patients were divided into training set (75%) and validation set (25%) by stratified sampling. Patients in the training set were dichotomized according to 24-h hematoma expansion ≥ 33%. Using the Laplacian of Gaussian bandpass filter, we chose different anatomical spatial domains ranging from fine texture to coarse texture to obtain a series of derived parameters (mean grayscale intensity, variance, uniformity) in order to quantify and evaluate all data. The parameters were externally validated on validation set.<bold>Results: </bold>Significant differences were found between the two groups of patients within variance at V1.0 and in uniformity at U1.0, U1.8 and U2.5. The intraclass correlation coefficients for the texture parameters were between 0.67 and 0.99. The area under the ROC curve between the two groups of ICH cases was between 0.77 and 0.92. The accuracy of validation set by CTTA was 0.59-0.85.<bold>Conclusion: </bold>NCCT texture analysis can objectively quantify the heterogeneity of ICH and independently predict early hematoma enlargement.<bold>Key Points: </bold>• Heterogeneity is helpful in predicting ICH enlargement. • CTTA could play an important role in predicting early ICH enlargement. • After filtering, fine texture had the best diagnostic performance. • The histogram-based uniformity parameters can independently predict ICH enlargement. • CTTA is more objective, more comprehensive, more independently operable, than previous methods. [ABSTRACT FROM AUTHOR]