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

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Noninvasive Prediction for Intracranial Pressure Related Parameters in Patients with Traumatic Brain Injury Using

Yihua Li¹, Ying Zhang¹, Yingchi Shan²

¹Department of Neurosurgery, Shanghai Xinhua Hospital, Shanghai Jiao Tong University of Medicine, Shanghai, China.

Journal of Neurotrauma

|July 18, 2025

Summary

This study introduces a noninvasive method using CT radiomic features to predict intracranial pressure (ICP)-related parameters in traumatic brain injury (TBI) patients. The developed models show good predictive ability, aiding in clinical guidance and prognosis.

Keywords:

computed tomography intracranial pressure radiomics traumatic brain injury

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Area of Science:

Radiology
Neurosurgery
Medical Imaging Analysis

Background:

Current intracranial pressure (ICP) monitoring is invasive, posing risks and limiting prognostic capabilities.
Predicting intracranial status and prognosis in traumatic brain injury (TBI) patients requires noninvasive methods.

Purpose of the Study:

To develop noninvasive prediction models for cerebrospinal compensatory reserve index (RAP) and pressure reactivity index (PRx) using CT radiomic features in TBI patients.
To assess the clinical utility and predictive accuracy of these models for guiding treatment and prognosis.

Main Methods:

Extracted 107 radiomic features from CT images of 60 TBI patients (42 training, 18 test) and validated on 20 external patients.
Utilized univariate regression and LASSO for feature selection, followed by multivariate logistic regression to build RAP and PRx prediction models.
Assessed model performance using AUC, calibration curves, and clinical usefulness evaluations.

Main Results:

The RAP model demonstrated strong discrimination with AUCs of 0.789 (training) and 0.818 (test), and 0.813 (external validation).
The PRx model showed significant discrimination with AUCs of 0.713 (training) and 0.667 (test), and 0.781 (external validation).
Both models exhibited excellent predictive accuracy and clinical usefulness, validated by nomograms and calibration curves.

Conclusions:

CT radiomic features offer a promising noninvasive approach to predict ICP-related parameters in TBI patients.
These radiomic models can serve as a valuable clinical aid for assessing intracranial conditions, guiding treatment, and indicating prognosis.