ArticleClinical neuroradiology2025
Evaluation of CT and MRI Radiomics for an Early Assessment of Diffuse Axonal Injury in Patients with Traumatic Brain Injury Compared to Conventional Radiological Diagnosis.
Article in Clinical neuroradiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Critical Care Monitoring in Comatose Patients Receiving ECMO: A Cohort Study of Machine Learning on Quantitative EEG for Diagnosing Acute Brain Injury and Prognosticating Mortality.Journal of clinical medicine · 2026Article
- Current applications of radiomics in neurotrauma.Neurosurgical review · 2026Review
- Artificial intelligence in traumatic brain injury: Brain imaging analysis and outcome prediction: A mini review.World journal of critical care medicine · 2025Review
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11 authors.
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Abstract
backgroundDe- and acceleration traumata can cause diffuse axonal injury (DAI) in patients with traumatic brain injury (TBI). The diagnosis of DAI on CT is challenging due to the lack of structural abnormalities. Radiomics, a method from the field of artificial intelligence (AI) offers the opportunity to extract additional information from imaging data. The purpose of this work was the evaluation of the feasibility of radiomics for an improved diagnosis of DAI in comparison to conventional radiological image assessment.
methodsCT and MR imaging was performed in 42 patients suspicious of DAI due to the clinical state, and two control groups (n = 44;42). DAI was diagnosed by experienced neuroradiologists. Radiomics features were extracted using a standardized MRI-based atlas of the predilection areas for DAI. Different MRI and CT based models were trained and validated by five-fold cross validation. Diagnostic performance was compared to the reading of two experienced radiologists and further validated in an external test dataset.
resultsThe MRI and CT models showed significant differences in radiomics features between patients with DAI and controls. The developed MRI based random forest classifier yielded an accuracy of 80-90%. The best performing CT model yielded an accuracy of 88% in the training data and 70% in the external test data. The results were comparable to conventional image analysis which achieved an accuracy of 70-81% for CT-based diagnosis.
conclusionMRI- and CT-based radiomics analysis is feasible for the assessment of DAI. The radiomics classifier achieved equivalent performance rates as visual radiological image diagnosis. Especially a radiomics based CT classifier can be of clinical value as a screening and AI-based decision support tool for patients with TBI.
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