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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

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Published on: April 13, 2013

Radiomics-enhanced machine learning for differentiating epidural and subdural hematomas on non-contrast CT.

Shakiba Houshi¹, Mehdi Karami², Awat Feizi³

¹Isfahan Neurosciences Research Center, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.

Neurological Research

|May 6, 2026

Summary

Machine learning using radiomic and CT features can help differentiate epidural hematoma (EDH) from subdural hematoma (SDH). This approach improves diagnostic accuracy for challenging cases, aiding clinical decision-making.

Keywords:

CT imaging features Hematoma epidural hemorrhage (EDH)machine learning techniques subdural hemorrhage (SDH)

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Area of Science:

Radiology
Artificial Intelligence
Medical Imaging Analysis

Background:

Differentiating epidural hematoma (EDH) from subdural hematoma (SDH) on non-contrast CT (NCCT) is crucial but challenging due to overlapping imaging features.
Accurate differentiation is vital for appropriate clinical management and patient outcomes.

Purpose of the Study:

To evaluate the efficacy of radiomic features combined with machine learning in improving the discrimination between EDH and SDH.
To assess the performance of machine learning models in classifying EDH versus SDH using CT imaging data.

Main Methods:

A retrospective analysis of 175 adult patients with surgically confirmed EDH or SDH.
Extraction and assessment of conventional morphometric CT features and 107 radiomic features from segmented hematomas.
Development and evaluation of machine learning models (including XGBoost) using LASSO feature selection, AUC, AUCPR, and accuracy metrics.

Main Results:

Radiomic features demonstrated superior discriminative performance compared to morphometric features alone.
The XGBoost model achieved the highest performance.
Combining radiomic and morphometric features yielded the best results with an AUC of 0.75, AUCPR of 0.70, and accuracy of 0.74.

Conclusions:

Integrating radiomic and conventional CT features enhances automated differentiation of EDH and SDH.
Radiomics-assisted machine learning shows potential as a decision-support tool for difficult diagnoses.
Further external validation is recommended prior to clinical implementation.