1
|
Moen KG, Flusund AMH, Moe HK, Andelic N, Skandsen T, Håberg A, Kvistad KA, Olsen Ø, Saksvoll EH, Abel-Grüner S, Anke A, Follestad T, Vik A. The prognostic importance of traumatic axonal injury on early MRI: the Trondheim TAI-MRI grading and quantitative models. Eur Radiol 2024:10.1007/s00330-024-10841-1. [PMID: 38896232 DOI: 10.1007/s00330-024-10841-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 03/25/2024] [Accepted: 04/06/2024] [Indexed: 06/21/2024]
Abstract
OBJECTIVES We analysed magnetic resonance imaging (MRI) findings after traumatic brain injury (TBI) aiming to improve the grading of traumatic axonal injury (TAI) to better reflect the outcome. METHODS Four-hundred sixty-three patients (8-70 years) with mild (n = 158), moderate (n = 129), or severe (n = 176) TBI and early MRI were prospectively included. TAI presence, numbers, and volumes at predefined locations were registered on fluid-attenuated inversion recovery (FLAIR) and diffusion-weighted imaging, and presence and numbers on T2*GRE/SWI. Presence and volumes of contusions were registered on FLAIR. We assessed the outcome with the Glasgow Outcome Scale Extended. Multivariable logistic and elastic-net regression analyses were performed. RESULTS The presence of TAI differed between mild (6%), moderate (70%), and severe TBI (95%). In severe TBI, bilateral TAI in mesencephalon or thalami and bilateral TAI in pons predicted worse outcomes and were defined as the worst grades (4 and 5, respectively) in the Trondheim TAI-MRI grading. The Trondheim TAI-MRI grading performed better than the standard TAI grading in severe TBI (pseudo-R2 0.19 vs. 0.16). In moderate-severe TBI, quantitative models including both FLAIR volume of TAI and contusions performed best (pseudo-R2 0.19-0.21). In patients with mild TBI or Glasgow Coma Scale (GCS) score 13, models with the volume of contusions performed best (pseudo-R2 0.25-0.26). CONCLUSIONS We propose the Trondheim TAI-MRI grading (grades 1-5) with bilateral TAI in mesencephalon or thalami, and bilateral TAI in pons as the worst grades. The predictive value was highest for the quantitative models including FLAIR volume of TAI and contusions (GCS score <13) or FLAIR volume of contusions (GCS score ≥ 13), which emphasise artificial intelligence as a potentially important future tool. CLINICAL RELEVANCE STATEMENT The Trondheim TAI-MRI grading reflects patient outcomes better in severe TBI than today's standard TAI grading and can be implemented after external validation. The prognostic importance of volumetric models is promising for future use of artificial intelligence technologies. KEY POINTS Traumatic axonal injury (TAI) is an important injury type in all TBI severities. Studies demonstrating which MRI findings that can serve as future biomarkers are highly warranted. This study proposes the most optimal MRI models for predicting patient outcome at 6 months after TBI; one updated pragmatic model and a volumetric model. The Trondheim TAI-MRI grading, in severe TBI, reflects patient outcome better than today's standard grading of TAI and the prognostic importance of volumetric models in all severities of TBI is promising for future use of AI.
Collapse
Affiliation(s)
- Kent Gøran Moen
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), 7491, Trondheim, Norway.
- Department of Radiology, Vestre Viken Hospital Trust, Drammen Hospital, 3004, Drammen, Norway.
- Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, 7006, Trondheim, Norway.
- Department of Neurosurgery, St. Olavs Hospital, Trondheim University Hospital, 7006, Trondheim, Norway.
| | - Anne-Mari Holte Flusund
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), 7491, Trondheim, Norway
- Department of Radiology, Møre and Romsdal Hospital Trust, Molde Hospital, 6412, Molde, Norway
| | - Hans Kristian Moe
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), 7491, Trondheim, Norway
- Department of Neurosurgery, Oslo University Hospital, Rikshospitalet, P.O. Box 4950 Nydalen, 0424, Oslo, Norway
| | - Nada Andelic
- Institute of Health and Society, Research Centre for Habilitation and Rehabilitation Models and Services (CHARM), Faculty of Medicine, University of Oslo, P.O. Box 1130 Blindern, 0318, Oslo, Norway
- Department of Physical Medicine and Rehabilitation, Oslo University Hospital, Ullevål Hospital, P.O. Box 4956 Nydalen, 0424, Oslo, Norway
| | - Toril Skandsen
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), 7491, Trondheim, Norway
- Department of Physical Medicine and Rehabilitation, St. Olavs Hospital, Trondheim University Hospital, 7006, Trondheim, Norway
| | - Asta Håberg
- Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, 7006, Trondheim, Norway
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), 7491, Trondheim, Norway
- MI Lab and Department of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU), 7491, Trondheim, Norway
| | - Kjell Arne Kvistad
- Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, 7006, Trondheim, Norway
| | - Øystein Olsen
- Department of Radiology, Nord-Trøndelag Hospital Trust, Levanger Hospital, 7600, Levanger, Norway
| | - Elin Hildrum Saksvoll
- Department of Radiology, Nord-Trøndelag Hospital Trust, Levanger Hospital, 7600, Levanger, Norway
| | - Sebastian Abel-Grüner
- Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, 7006, Trondheim, Norway
| | - Audny Anke
- Department of Rehabilitation, University Hospital of North Norway, 9038, Tromsø, Norway
- Faculty of Health Sciences, Department of Clinical Medicine, UiT- The Arctic University of Norway, 9038, Tromsø, Norway
| | - Turid Follestad
- Department of Clinical and Molecular Medicine, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, 7491, Trondheim, Norway
- Clinical Research Unit Central Norway, St. Olavs Hospital, Trondheim University Hospital, 7006, Trondheim, Norway
| | - Anne Vik
- Department of Neurosurgery, St. Olavs Hospital, Trondheim University Hospital, 7006, Trondheim, Norway
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), 7491, Trondheim, Norway
| |
Collapse
|
2
|
Vande Vyvere T, Pisică D, Wilms G, Claes L, Van Dyck P, Snoeckx A, van den Hauwe L, Pullens P, Verheyden J, Wintermark M, Dekeyzer S, Mac Donald CL, Maas AIR, Parizel PM. Imaging Findings in Acute Traumatic Brain Injury: a National Institute of Neurological Disorders and Stroke Common Data Element-Based Pictorial Review and Analysis of Over 4000 Admission Brain Computed Tomography Scans from the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) Study. J Neurotrauma 2024. [PMID: 38482818 DOI: 10.1089/neu.2023.0553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/20/2024] Open
Abstract
In 2010, the National Institute of Neurological Disorders and Stroke (NINDS) created a set of common data elements (CDEs) to help standardize the assessment and reporting of imaging findings in traumatic brain injury (TBI). However, as opposed to other standardized radiology reporting systems, a visual overview and data to support the proposed standardized lexicon are lacking. We used over 4000 admission computed tomography (CT) scans of patients with TBI from the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) study to develop an extensive pictorial overview of the NINDS TBI CDEs, with visual examples and background information on individual pathoanatomical lesion types, up to the level of supplemental and emerging information (e.g., location and estimated volumes). We documented the frequency of lesion occurrence, aiming to quantify the relative importance of different CDEs for characterizing TBI, and performed a critical appraisal of our experience with the intent to inform updating of the CDEs. In addition, we investigated the co-occurrence and clustering of lesion types and the distribution of six CT classification systems. The median age of the 4087 patients in our dataset was 50 years (interquartile range, 29-66; range, 0-96), including 238 patients under 18 years old (5.8%). Traumatic subarachnoid hemorrhage (45.3%), skull fractures (37.4%), contusions (31.3%), and acute subdural hematoma (28.9%) were the most frequently occurring CT findings in acute TBI. The ranking of these lesions was the same in patients with mild TBI (baseline Glasgow Coma Scale [GCS] score 13-15) compared with those with moderate-severe TBI (baseline GCS score 3-12), but the frequency of occurrence was up to three times higher in moderate-severe TBI. In most TBI patients with CT abnormalities, there was co-occurrence and clustering of different lesion types, with significant differences between mild and moderate-severe TBI patients. More specifically, lesion patterns were more complex in moderate-severe TBI patients, with more co-existing lesions and more frequent signs of mass effect. These patients also had higher and more heterogeneous CT score distributions, associated with worse predicted outcomes. The critical appraisal of the NINDS CDEs was highly positive, but revealed that full assessment can be time consuming, that some CDEs had very low frequencies, and identified a few redundancies and ambiguity in some definitions. Whilst primarily developed for research, implementation of CDE templates for use in clinical practice is advocated, but this will require development of an abbreviated version. In conclusion, with this study, we provide an educational resource for clinicians and researchers to help assess, characterize, and report the vast and complex spectrum of imaging findings in patients with TBI. Our data provides a comprehensive overview of the contemporary landscape of TBI imaging pathology in Europe, and the findings can serve as empirical evidence for updating the current NINDS radiologic CDEs to version 3.0.
Collapse
Affiliation(s)
- Thijs Vande Vyvere
- Department of Radiology, Antwerp University Hospital, Antwerp, Belgium
- Department of Molecular Imaging and Radiology (MIRA), Faculty of Medicine and Health Science, University of Antwerp, Antwerp, Belgium
| | - Dana Pisică
- Department of Neurosurgery, Erasmus MC - University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Public Health, Erasmus MC - University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Guido Wilms
- Department of Radiology, University Hospitals Leuven, Leuven, Belgium
| | - Lene Claes
- icometrix, Research and Development, Leuven, Belgium
| | - Pieter Van Dyck
- Department of Radiology, Antwerp University Hospital, Antwerp, Belgium
- Department of Molecular Imaging and Radiology (MIRA), Faculty of Medicine and Health Science, University of Antwerp, Antwerp, Belgium
| | - Annemiek Snoeckx
- Department of Radiology, Antwerp University Hospital, Antwerp, Belgium
- Department of Molecular Imaging and Radiology (MIRA), Faculty of Medicine and Health Science, University of Antwerp, Antwerp, Belgium
| | - Luc van den Hauwe
- Department of Radiology, Antwerp University Hospital, Antwerp, Belgium
| | - Pim Pullens
- Department of Imaging, University Hospital Ghent; IBITech/MEDISIP, Engineering and Architecture, Ghent University; Ghent Institute for Functional and Metabolic Imaging, Ghent University, Belgium
| | - Jan Verheyden
- icometrix, Research and Development, Leuven, Belgium
| | - Max Wintermark
- Department of Neuroradiology, University of Texas MD Anderson Center, Houston, Texas, USA
| | - Sven Dekeyzer
- Department of Radiology, Antwerp University Hospital, Antwerp, Belgium
- Department of Radiology, University Hospital Ghent, Belgium
| | - Christine L Mac Donald
- Department of Neurological Surgery, School of Medicine, Harborview Medical Center, Seattle, Washington, USA
- Department of Neurological Surgery, School of Medicine, University of Washington, Seattle, Washington, USA
| | - Andrew I R Maas
- Department of Neurosurgery, Antwerp University Hospital, Antwerp, Belgium
- Department of Translational Neuroscience, Faculty of Medicine and Health Science, University of Antwerp, Antwerp, Belgium
| | - Paul M Parizel
- Department of Radiology, Royal Perth Hospital (RPH) and University of Western Australia (UWA), Perth, Australia; Western Australia National Imaging Facility (WA NIF) node, Australia
| |
Collapse
|
3
|
Ateeq T, Faheem ZB, Ghoneimy M, Ali J, Li Y, Baz A. Naïve Bayes classifier assisted automated detection of cerebral microbleeds in susceptibility-weighted imaging brain images. Biochem Cell Biol 2023; 101:562-573. [PMID: 37639730 DOI: 10.1139/bcb-2023-0156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/31/2023] Open
Abstract
Cerebral microbleeds (CMBs) in the brain are the essential indicators of critical brain disorders such as dementia and ischemic stroke. Generally, CMBs are detected manually by experts, which is an exhaustive task with limited productivity. Since CMBs have complex morphological nature, manual detection is prone to errors. This paper presents a machine learning-based automated CMB detection technique in the brain susceptibility-weighted imaging (SWI) scans based on statistical feature extraction and classification. The proposed method consists of three steps: (1) removal of the skull and extraction of the brain; (2) thresholding for the extraction of initial candidates; and (3) extracting features and applying classification models such as random forest and naïve Bayes classifiers for the detection of true positive CMBs. The proposed technique is validated on a dataset consisting of 20 subjects. The dataset is divided into training data that consist of 14 subjects with 104 microbleeds and testing data that consist of 6 subjects with 63 microbleeds. We were able to achieve 85.7% sensitivity using the random forest classifier with 4.2 false positives per CMB, and the naïve Bayes classifier achieved 90.5% sensitivity with 5.5 false positives per CMB. The proposed technique outperformed many state-of-the-art methods proposed in previous studies.
Collapse
Affiliation(s)
- Tayyab Ateeq
- Department of Computer Engineering, The University of Lahore, Lahore 54000, Pakistan
| | - Zaid Bin Faheem
- Department of Computer Science & IT, The Islamia University of Bahawalpur, Bahawalpur, Punjab 63100, Pakistan
| | - Mohamed Ghoneimy
- Faculty of Computer Science, Modern Science & Arts (MSA) University, Giza, Egypt
| | - Jehad Ali
- Department of AI Convergence Network, Ajou University, Suwon, South Korea
| | - Yang Li
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China
| | - Abdullah Baz
- Department of Computer Engineering, College of Computer and Information Systems, Umm Al-Qura University, Makkah, Saudi Arabia
| |
Collapse
|
4
|
Banerjee G, Collinge J, Fox NC, Lashley T, Mead S, Schott JM, Werring DJ, Ryan NS. Clinical considerations in early-onset cerebral amyloid angiopathy. Brain 2023; 146:3991-4014. [PMID: 37280119 PMCID: PMC10545523 DOI: 10.1093/brain/awad193] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 04/16/2023] [Accepted: 05/01/2023] [Indexed: 06/08/2023] Open
Abstract
Cerebral amyloid angiopathy (CAA) is an important cerebral small vessel disease associated with brain haemorrhage and cognitive change. The commonest form, sporadic amyloid-β CAA, usually affects people in mid- to later life. However, early-onset forms, though uncommon, are increasingly recognized and may result from genetic or iatrogenic causes that warrant specific and focused investigation and management. In this review, we firstly describe the causes of early-onset CAA, including monogenic causes of amyloid-β CAA (APP missense mutations and copy number variants; mutations of PSEN1 and PSEN2) and non-amyloid-β CAA (associated with ITM2B, CST3, GSN, PRNP and TTR mutations), and other unusual sporadic and acquired causes including the newly-recognized iatrogenic subtype. We then provide a structured approach for investigating early-onset CAA, and highlight important management considerations. Improving awareness of these unusual forms of CAA amongst healthcare professionals is essential for facilitating their prompt diagnosis, and an understanding of their underlying pathophysiology may have implications for more common, late-onset, forms of the disease.
Collapse
Affiliation(s)
- Gargi Banerjee
- MRC Prion Unit at University College London (UCL), Institute of Prion Diseases, UCL, London, W1W 7FF, UK
| | - John Collinge
- MRC Prion Unit at University College London (UCL), Institute of Prion Diseases, UCL, London, W1W 7FF, UK
| | - Nick C Fox
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, WC1N 3BG, UK
- UK Dementia Research Institute at UCL, London, WC1E 6BT, UK
| | - Tammaryn Lashley
- The Queen Square Brain Bank for Neurological Disorders, Department of Clinical and Movement Disorders, UCL Queen Square Institute of Neurology, London, W1 1PJ, UK
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, WC1N 3BG, UK
| | - Simon Mead
- MRC Prion Unit at University College London (UCL), Institute of Prion Diseases, UCL, London, W1W 7FF, UK
| | - Jonathan M Schott
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, WC1N 3BG, UK
- UK Dementia Research Institute at UCL, London, WC1E 6BT, UK
| | - David J Werring
- Stroke Research Centre, Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, London, WC1N 3BG, UK
| | - Natalie S Ryan
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, WC1N 3BG, UK
- UK Dementia Research Institute at UCL, London, WC1E 6BT, UK
| |
Collapse
|
5
|
Xu X, Xu H, Zhang Z. Cerebral amyloid angiopathy-related cardiac injury: Focus on cardiac cell death. Front Cell Dev Biol 2023; 11:1156970. [PMID: 36910141 PMCID: PMC9998697 DOI: 10.3389/fcell.2023.1156970] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 02/16/2023] [Indexed: 03/14/2023] Open
Abstract
Cerebral amyloid angiopathy (CAA) is a kind of disease in which amyloid β (Aβ) and other amyloid protein deposits in the cerebral cortex and the small blood vessels of the brain, causing cerebrovascular and brain parenchymal damage. CAA patients are often accompanied by cardiac injury, involving Aβ, tau and transthyroxine amyloid (ATTR). Aβ is the main injury factor of CAA, which can accelerate the formation of coronary artery atherosclerosis, aortic valve osteogenesis calcification and cardiomyocytes basophilic degeneration. In the early stage of CAA (pre-stroke), the accompanying locus coeruleus (LC) amyloidosis, vasculitis and circulating Aβ will induce first hit to the heart. When the CAA progresses to an advanced stage and causes a cerebral hemorrhage, the hemorrhage leads to autonomic nervous function disturbance, catecholamine surges, and systemic inflammation reaction, which can deal the second hit to the heart. Based on the brain-heart axis, CAA and its associated cardiac injury can create a vicious cycle that accelerates the progression of each other.
Collapse
Affiliation(s)
- Xiaofang Xu
- Department of Critical Care Medicine, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Huikang Xu
- Department of Critical Care Medicine, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Zhaocai Zhang
- Department of Critical Care Medicine, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.,Key Laboratory of the Diagnosis and Treatment for Severe Trauma and Burn of Zhejiang Province, Hangzhou, China.,Zhejiang Province Clinical Research Center for Emergency and Critical care medicine, Hangzhou, China
| |
Collapse
|