Developing Radiomic Neuroimaging Biomarkers Using Machine Learning for Predicting Post-traumatic Epilepsy (P8-18.003)

Mark Chao; Jerome Jeevarajan

doi:10.1212/wnl.0000000000215719

June 9, 2026Neurology

Developing Radiomic Neuroimaging Biomarkers Using Machine Learning for Predicting Post-traumatic Epilepsy (P8-18.003)

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Key Points

The aim is to identify radiomic biomarkers from CT scans that predict the risk of post-traumatic epilepsy in patients with traumatic brain injury.
Identified radiomic biomarkers via computed tomography imaging.
Developed machine learning models to predict post-traumatic epilepsy risk.
Found significant radiomic biomarkers associated with post-traumatic epilepsy.
Developed machine learning models that effectively predict PTE risk in TBI patients.

Implication

Identifies radiomic biomarkers for predicting post-traumatic epilepsy in TBI patients, suggesting advancements in risk assessment.

Abstract

To identify computed tomography (CT) based radiomic biomarkers of post-traumatic epilepsy (PTE) in traumatic brain injury (TBI) patients and develop machine learning (ML) models for PTE risk prediction.

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Cite This Study

Chao et al. (2026) studied this question.

synapsesocial.com/papers/6a2cf3b4faef96ed7f056296 https://doi.org/10.1212/wnl.0000000000215719

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Developing Radiomic Neuroimaging Biomarkers Using Machine Learning for Predicting Post-traumatic Epilepsy (P8-18.003)

Key Points

Implication

Abstract

Discussion

Cite This Study

Developing Radiomic Neuroimaging Biomarkers Using Machine Learning for Predicting Post-traumatic Epilepsy (P8-18.003)

Key Points

Implication

Abstract

Discussion

Cite This Study

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