Meißner, Anna-Katharina 
ORCID: 0000-0003-4150-7265, Gutsche, Robin ORCID: 0000-0001-8136-9536, Pennig, Lenhard ORCID: 0000-0002-6606-9313, Nelles, Christian ORCID: 0000-0002-7351-7516, Budzejko, Enrico, Hamisch, Christina, Kocher, Martin ORCID: 0000-0002-5674-9227, Schlamann, Marc ORCID: 0000-0002-7734-611X, Goldbrunner, Roland ORCID: 0000-0002-1927-0022, Grau, Stefan and Lohmann, Philipp ORCID: 0000-0002-5360-046X
(2025).
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.
Clinical Neuroradiology, 35 (3).
pp. 521-532.
Springer Nature.
ISSN 1869-1439
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Abstract
Background: De- 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. Methods: CT 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. Results: The 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. Conclusion: MRI- 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.
| Item Type: | Article |
| Creators: | Creators Email ORCID ORCID Put Code Budzejko, Enrico UNSPECIFIED UNSPECIFIED UNSPECIFIED Hamisch, Christina UNSPECIFIED UNSPECIFIED UNSPECIFIED Grau, Stefan UNSPECIFIED UNSPECIFIED UNSPECIFIED |
| URN: | urn:nbn:de:hbz:38-797857 |
| Identification Number: | 10.1007/s00062-025-01507-6 |
| Journal or Publication Title: | Clinical Neuroradiology |
| Volume: | 35 |
| Number: | 3 |
| Page Range: | pp. 521-532 |
| Number of Pages: | 12 |
| Date: | 7 September 2025 |
| Publisher: | Springer Nature |
| ISSN: | 1869-1439 |
| Language: | English |
| Faculty: | Central Institutions / Interdisciplinary Research Centers Faculty of Medicine |
| Divisions: | Außeruniversitäre Forschungseinrichtungen > Forschungszentrum Jülich Faculty of Medicine > Neurochirurgie > Klinik für Allgemeine Neurochirurgie Faculty of Medicine > Radiologische Diagnostik > Institut und Poliklinik für Radiologische Diagnostik |
| Subjects: | Medical sciences Medicine |
| ['eprint_fieldname_oa_funders' not defined]: | Publikationsfonds UzK |
| Refereed: | Yes |
| URI: | http://kups.ub.uni-koeln.de/id/eprint/79785 |
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