Classification algorithms using multiple MRI features in mild traumatic brain injury
| Autor: | Damon Kenul, Yuanyi Xue, Yulin Ge, Yao Wang, Yvonne W. Lui, Robert I. Grossman |
|---|---|
| Rok vydání: | 2014 |
| Předmět: |
Adult
Male medicine.medical_specialty Support Vector Machine Population Feature selection Pilot Projects Sensitivity and Specificity White matter Physical medicine and rehabilitation Neuroimaging Thalamus Medicine Cingulum (brain) Humans Diagnosis Computer-Assisted Prospective Studies education education.field_of_study medicine.diagnostic_test business.industry Brain Magnetic resonance imaging Bayes Theorem Magnetic Resonance Imaging Surgery Statistical classification medicine.anatomical_structure Diffusion Tensor Imaging Brain Injuries Female Neurology (clinical) business Algorithms Diffusion MRI |
| Zdroj: | Neurology. 83(14) |
| ISSN: | 1526-632X |
| Popis: | Objective: The purpose of this study was to develop an algorithm incorporating MRI metrics to classify patients with mild traumatic brain injury (mTBI) and controls. Methods: This was an institutional review board–approved, Health Insurance Portability and Accountability Act–compliant prospective study. We recruited patients with mTBI and healthy controls through the emergency department and general population. We acquired data on a 3.0T Siemens Trio magnet including conventional brain imaging, resting-state fMRI, diffusion-weighted imaging, and magnetic field correlation (MFC), and performed multifeature analysis using the following MRI metrics: mean kurtosis (MK) of thalamus, MFC of thalamus and frontal white matter, thalamocortical resting-state networks, and 5 regional gray matter and white matter volumes including the anterior cingulum and left frontal and temporal poles. Feature selection was performed using minimal-redundancy maximal-relevance. We used classifiers including support vector machine, naive Bayesian, Bayesian network, radial basis network, and multilayer perceptron to test maximal accuracy. Results: We studied 24 patients with mTBI and 26 controls. Best single-feature classification uses thalamic MK yielding 74% accuracy. Multifeature analysis yields 80% accuracy using the full feature set, and up to 86% accuracy using minimal-redundancy maximal-relevance feature selection (MK thalamus, right anterior cingulate volume, thalamic thickness, thalamocortical resting-state network, thalamic microscopic MFC, and sex). Conclusion: Multifeature analysis using diffusion-weighted imaging, MFC, fMRI, and volumetrics may aid in the classification of patients with mTBI compared with controls based on optimal feature selection and classification methods. Classification of evidence: This study provides Class III evidence that classification algorithms using multiple MRI features accurately identifies patients with mTBI as defined by American Congress of Rehabilitation Medicine criteria compared with healthy controls. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|