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Updated: Jan 7, 2026

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

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

Published on: April 13, 2013

Clinically Applicable Machine Learning Approach to Predict Intracerebral Hematoma Expansion.

Shogo Watanabe¹, Nice Ren¹, Yukihiro Imaoka¹

¹Department of Stroke and Cardiovascular Disease Next Generation Medical Research National Cerebral and Cardiovascular Center Osaka Japan.

Journal of the American Heart Association

|December 30, 2025

Summary

This summary is machine-generated.

Hematoma expansion (HE) in intracerebral hemorrhage (ICH) can be predicted using a combined model of clinical and radiomics features. This model, outperforming others, aids in emergency treatment decisions for ICH patients.

Keywords:

hematoma expansion imaging biomarkers intracerebral hemorrhage machine learning prediction model radiomics

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Area of Science:

Neurology
Radiology
Medical Informatics

Background:

Hematoma expansion (HE) is a key predictor of poor outcomes in patients with intracerebral hemorrhage (ICH).
Accurate prediction of HE is vital for effective treatment planning in ICH cases.

Purpose of the Study:

To develop and evaluate a predictive model for HE in ICH patients.
To compare the performance of clinical variables, radiomics features, and a combined model for HE prediction.

Main Methods:

A cohort of 452 ICH patients was analyzed.
Clinical variables and 1142 radiomics features from CT images were used for prediction.
Gradient boosting and LASSO were employed for feature selection, with models built and validated using 5-fold cross-validation.

Main Results:

The combined model achieved the highest predictive performance (mean AUC of 0.77±0.05).
The combined model outperformed models using only clinical variables (0.70±0.06) or radiomics features (0.73±0.04).
Anticoagulant treatment emerged as the most significant predictor in the combined model.

Conclusions:

A novel HE prediction model integrating clinical and radiomics data was successfully developed.
This model can assist non-stroke specialists in making timely treatment decisions for ICH patients in emergency settings.