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Sanvito F, Pichiecchio A, Paoletti M, Rebella G, Resaz M, Benedetti L, Massa F, Morbelli S, Caverzasi E, Asteggiano C, Businaro P, Masciocchi S, Castellan L, Franciotta D, Gastaldi M, Roccatagliata L. Autoimmune encephalitis: what the radiologist needs to know. Neuroradiology 2024; 66:653-675. [PMID: 38507081 PMCID: PMC11031487 DOI: 10.1007/s00234-024-03318-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 02/20/2024] [Indexed: 03/22/2024]
Abstract
Autoimmune encephalitis is a relatively novel nosological entity characterized by an immune-mediated damage of the central nervous system. While originally described as a paraneoplastic inflammatory phenomenon affecting limbic structures, numerous instances of non-paraneoplastic pathogenesis, as well as extra-limbic involvement, have been characterized. Given the wide spectrum of insidious clinical presentations ranging from cognitive impairment to psychiatric symptoms or seizures, it is crucial to raise awareness about this disease category. In fact, an early diagnosis can be dramatically beneficial for the prognosis both to achieve an early therapeutic intervention and to detect a potential underlying malignancy. In this scenario, the radiologist can be the first to pose the hypothesis of autoimmune encephalitis and refer the patient to a comprehensive diagnostic work-up - including clinical, serological, and neurophysiological assessments.In this article, we illustrate the main radiological characteristics of autoimmune encephalitis and its subtypes, including the typical limbic presentation, the features of extra-limbic involvement, and also peculiar imaging findings. In addition, we review the most relevant alternative diagnoses that should be considered, ranging from other encephalitides to neoplasms, vascular conditions, and post-seizure alterations. Finally, we discuss the most appropriate imaging diagnostic work-up, also proposing a suggested MRI protocol.
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Affiliation(s)
- Francesco Sanvito
- Unit of Radiology, Department of Clinical, Surgical, Diagnostic, and Paediatric Sciences, University of Pavia, Viale Camillo Golgi, 19, 27100, Pavia, Italy.
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, University of California Los Angeles, Los Angeles, CA, USA.
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
| | - Anna Pichiecchio
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Advanced Imaging and Artificial Intelligence Center, Department of Neuroradiology, IRCCS Mondino Foundation, Via Mondino 2, 27100, Pavia, Italy
| | - Matteo Paoletti
- Advanced Imaging and Artificial Intelligence Center, Department of Neuroradiology, IRCCS Mondino Foundation, Via Mondino 2, 27100, Pavia, Italy
| | - Giacomo Rebella
- IRCCS Ospedale Policlinico San Martino, Largo Rosanna Benzi 10, 16132, Genoa, Italy
| | - Martina Resaz
- IRCCS Ospedale Policlinico San Martino, Largo Rosanna Benzi 10, 16132, Genoa, Italy
| | - Luana Benedetti
- IRCCS Ospedale Policlinico San Martino, Largo Rosanna Benzi 10, 16132, Genoa, Italy
| | - Federico Massa
- IRCCS Ospedale Policlinico San Martino, Largo Rosanna Benzi 10, 16132, Genoa, Italy
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Largo Daneo 3, 16132, Genoa, Italy
| | - Silvia Morbelli
- IRCCS Ospedale Policlinico San Martino, Largo Rosanna Benzi 10, 16132, Genoa, Italy
- Department of Health Sciences (DISSAL), University of Genoa, Via Antonio Pastore 1, 16132, Genoa, Italy
| | - Eduardo Caverzasi
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Advanced Imaging and Artificial Intelligence Center, Department of Neuroradiology, IRCCS Mondino Foundation, Via Mondino 2, 27100, Pavia, Italy
| | - Carlo Asteggiano
- Advanced Imaging and Artificial Intelligence Center, Department of Neuroradiology, IRCCS Mondino Foundation, Via Mondino 2, 27100, Pavia, Italy
| | - Pietro Businaro
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Neuroimmunology Laboratory and Neuroimmunology Research Section, IRCCS Mondino Foundation, Via Mondino 2, 27100, Pavia, Italy
| | - Stefano Masciocchi
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Neuroimmunology Laboratory and Neuroimmunology Research Section, IRCCS Mondino Foundation, Via Mondino 2, 27100, Pavia, Italy
| | - Lucio Castellan
- IRCCS Ospedale Policlinico San Martino, Largo Rosanna Benzi 10, 16132, Genoa, Italy
| | - Diego Franciotta
- Neuroimmunology Laboratory and Neuroimmunology Research Section, IRCCS Mondino Foundation, Via Mondino 2, 27100, Pavia, Italy
| | - Matteo Gastaldi
- Neuroimmunology Laboratory and Neuroimmunology Research Section, IRCCS Mondino Foundation, Via Mondino 2, 27100, Pavia, Italy
| | - Luca Roccatagliata
- IRCCS Ospedale Policlinico San Martino, Largo Rosanna Benzi 10, 16132, Genoa, Italy
- Department of Health Sciences (DISSAL), University of Genoa, Via Antonio Pastore 1, 16132, Genoa, Italy
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Seiler A, Nöth U, Hok P, Reiländer A, Maiworm M, Baudrexel S, Meuth S, Rosenow F, Steinmetz H, Wagner M, Hattingen E, Deichmann R, Gracien RM. Multiparametric Quantitative MRI in Neurological Diseases. Front Neurol 2021; 12:640239. [PMID: 33763021 PMCID: PMC7982527 DOI: 10.3389/fneur.2021.640239] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Accepted: 02/12/2021] [Indexed: 11/27/2022] Open
Abstract
Magnetic resonance imaging (MRI) is the gold standard imaging technique for diagnosis and monitoring of many neurological diseases. However, the application of conventional MRI in clinical routine is mainly limited to the visual detection of macroscopic tissue pathology since mixed tissue contrasts depending on hardware and protocol parameters hamper its application for the assessment of subtle or diffuse impairment of the structural tissue integrity. Multiparametric quantitative (q)MRI determines tissue parameters quantitatively, enabling the detection of microstructural processes related to tissue remodeling in aging and neurological diseases. In contrast to measuring tissue atrophy via structural imaging, multiparametric qMRI allows for investigating biologically distinct microstructural processes, which precede changes of the tissue volume. This facilitates a more comprehensive characterization of tissue alterations by revealing early impairment of the microstructural integrity and specific disease-related patterns. So far, qMRI techniques have been employed in a wide range of neurological diseases, including in particular conditions with inflammatory, cerebrovascular and neurodegenerative pathology. Numerous studies suggest that qMRI might add valuable information, including the detection of microstructural tissue damage in areas appearing normal on conventional MRI and unveiling the microstructural correlates of clinical manifestations. This review will give an overview of current qMRI techniques, the most relevant tissue parameters and potential applications in neurological diseases, such as early (differential) diagnosis, monitoring of disease progression, and evaluating effects of therapeutic interventions.
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Affiliation(s)
- Alexander Seiler
- Department of Neurology, Goethe University, Frankfurt, Germany.,Brain Imaging Center, Goethe University, Frankfurt, Germany
| | - Ulrike Nöth
- Brain Imaging Center, Goethe University, Frankfurt, Germany.,Center for Personalized Translational Epilepsy Research (CePTER) Consortium, Goethe University, Frankfurt, Germany
| | - Pavel Hok
- Department of Neurology, Palacký University Olomouc and University Hospital Olomouc, Olomouc, Czechia
| | - Annemarie Reiländer
- Department of Neurology, Goethe University, Frankfurt, Germany.,Brain Imaging Center, Goethe University, Frankfurt, Germany
| | - Michelle Maiworm
- Department of Neurology, Goethe University, Frankfurt, Germany.,Brain Imaging Center, Goethe University, Frankfurt, Germany.,Center for Personalized Translational Epilepsy Research (CePTER) Consortium, Goethe University, Frankfurt, Germany
| | - Simon Baudrexel
- Department of Neurology, Goethe University, Frankfurt, Germany.,Brain Imaging Center, Goethe University, Frankfurt, Germany
| | - Sven Meuth
- Department of Neurology, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Felix Rosenow
- Department of Neurology, Goethe University, Frankfurt, Germany.,Center for Personalized Translational Epilepsy Research (CePTER) Consortium, Goethe University, Frankfurt, Germany.,Epilepsy Center Frankfurt Rhine-Main, Center of Neurology and Neurosurgery, University Hospital, Frankfurt, Germany
| | - Helmuth Steinmetz
- Department of Neurology, Goethe University, Frankfurt, Germany.,Center for Personalized Translational Epilepsy Research (CePTER) Consortium, Goethe University, Frankfurt, Germany
| | - Marlies Wagner
- Brain Imaging Center, Goethe University, Frankfurt, Germany.,Center for Personalized Translational Epilepsy Research (CePTER) Consortium, Goethe University, Frankfurt, Germany
| | - Elke Hattingen
- Center for Personalized Translational Epilepsy Research (CePTER) Consortium, Goethe University, Frankfurt, Germany.,Department of Neuroradiology, Goethe University, Frankfurt, Germany
| | - Ralf Deichmann
- Brain Imaging Center, Goethe University, Frankfurt, Germany.,Center for Personalized Translational Epilepsy Research (CePTER) Consortium, Goethe University, Frankfurt, Germany
| | - René-Maxime Gracien
- Department of Neurology, Goethe University, Frankfurt, Germany.,Brain Imaging Center, Goethe University, Frankfurt, Germany.,Center for Personalized Translational Epilepsy Research (CePTER) Consortium, Goethe University, Frankfurt, Germany
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18F-FDG-PET/MRI in the diagnostic work-up of limbic encephalitis. PLoS One 2020; 15:e0227906. [PMID: 31951636 PMCID: PMC6968877 DOI: 10.1371/journal.pone.0227906] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Accepted: 01/02/2020] [Indexed: 01/18/2023] Open
Abstract
Introduction Limbic encephalitis (LE) is an immune-related, sometimes paraneoplastic process of the central nervous system. Initial diagnosis and treatment are based on the clinical presentation as well as antibody profiles and MRI. This study investigated the diagnostic value of integrated 18F-FDG-PET/MRI in the diagnostic work-up of patients with LE for a cerebral and whole-body imaging concept. Material and methods Twenty patients with suspected LE were enrolled in this prospective study. All patients underwent a dedicated PET/MRI protocol of the brain as well as the whole-body. Two neuroradiologists, one body radiologist and one nuclear medicine physician performed blinded consensus readings of each corresponding MRI and PET/MRI dataset of the brain and whole-body. Diagnostic confidence was evaluated on a Likert scale. Results Based on integrated PET/MRI 19 / 20 patients were found to show morphologic and / or metabolic changes indicative of LE, whereas sole MRI enabled correct identification in 16 / 20 patients. Three patients with negative MRI showed metabolic changes of the limbic system or extra-limbic regions, shifting the diagnosis from (negative) MRI to positive for LE in PET/MRI. Whole-body staging revealed suspected lesions in 2/20 patients, identified by MRI and PET, one confirmed as malignant and one false positive. Diagnostic confidence for cerebral and whole-body imaging reached higher scores for PET/MRI (cerebral: 2.7 and whole body: 4.8) compared to MRI alone (cerebral: 2.4 and whole body: 4.5). Conclusion LE diagnosis remains challenging for imaging as it shows only subtle imaging findings in most patients. Nevertheless, based on the simultaneous and combined analysis of morphologic and metabolic data, integrated PET/MRI may enable a dual platform for improved diagnostic confidence and overall detection of LE as well as whole-body imaging for exclusion of paraneoplastic LE.
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