Quantitative parameters of CT texture analysis as potential markers for early prediction of spontaneous intracranial hemorrhage enlargement
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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.
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.
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.
NCCT texture analysis can objectively quantify the heterogeneity of ICH and independently predict early hematoma enlargement.
• 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.
KeywordsCerebral hemorrhage/diagnostic imaging Stroke Disease progression Tomography X-ray computed Algorithms
Activated partial prothrombin time
Area under the ROC curve
Computed tomography texture analysis
Field of view
Glasgow coma scale
Intraclass correlation coefficient
Laplacian of Gaussian
Mean gray-level intensity
Non-contrast computed tomography
National Institutes of Health stroke scale
Receiver operating characteristic curve
Region of interest
Time to scan
This study has received funding by the Department of Health of Zhejiang Province, China (No. 2017KY051).
This study has received funding by the Department of Health of Zhejiang Province, China (No. 2018KY582).
This study also has received funding by Hangzhou science and Technology Commission, China (No. 164519).
Compliance with ethical standards
The scientific guarantor of this publication is Zhan Feng.
Conflict of interest
The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.
Statistics and biometry
No complex statistical methods were necessary for this paper.
Written informed consent was not required for this study because of the retrospective nature of the study.
Institutional review board approval was obtained.
Study subjects or cohorts overlap
Study subjects or cohorts have not been previously reported.
• diagnostic or prognostic study
• performed at one institution
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