CT histogram analysis to distinguish between acute intracerebral hemorrhage and cavernous hemangioma.

Objective: Acute intracerebral hemorrhage (AICH) and cerebral cavernous hemangioma (CCM) are two common cerebral hemorrhage diseases with partially overlapping CT findings and clinical symptoms, making it hard to distinguish between them. The current study used histogram analysis based on CT images to differentiate between CCM and AICH and test its diagnosis performance.
Methods: This retrospective study included 158 patients with CCM and 137 patients with AICH. The histograms of brain CT plain scan images of both groups were extracted using Python code and included 18 histogram parameters of the lesions. The most effective parameters were selected by univariate logistic regression analysis and Spearman correlation analysis and included in the final multivariate logistic regression model. The sample was randomly divided into the training set and the validation set by 7:3. The ROC curve was constructed to evaluate the discriminant efficiency of the final logistic regression model in distinguishing between AICH and CCM.
Results: The univariate analysis identified seven significant histogram parameters with the following final logistic regression model: F = 3.731 + 2.6411 × 10 ^-9  × Energy-1.192 × Kurtosis-0.003 × Minimum-1.449 × Skewness + 2.5002 × 10 ^-10  × Total Energy-1.103 × Uniformity+0.009 × Variance. The model showed good diagnostic performance in distinguishing between AICH and CCM, with an AUC of 0.876, sensitivity of 70.8%, and specificity of 91.9% in the training set, and an AUC of 0.870, sensitivity of 82.9%, and specificity of 85.1% in the validation set.
Conclusions: The histogram analysis of brain CT images can be used as an auxiliary method to distinguish between AICH and CCM effectively.
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