1
|
Gaetani L, Salvadori N, Brachelente G, Sperandei S, Di Sabatino E, Fiacca A, Mancini A, Villa A, De Stefano N, Parnetti L, Di Filippo M. Intrathecal B cell activation and memory impairment in multiple sclerosis. Mult Scler Relat Disord 2024; 85:105548. [PMID: 38513467 DOI: 10.1016/j.msard.2024.105548] [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] [Received: 03/04/2024] [Accepted: 03/07/2024] [Indexed: 03/23/2024]
Abstract
BACKGROUND Cognitive impairment (CI) is a common and disabling feature of people with multiple sclerosis (pwMS), but its underlying mechanisms are heterogenous and not fully understood. A role of infiltrating immune cells in the meninges and brain parenchyma has been hypothesized. This study aimed to explore the hypothesis that intrathecal B cells might influence cognitive performance in pwMS. METHODS A retrospective study was performed on 39 newly diagnosed pwMS who underwent cerebrospinal fluid (CSF) analysis. Kappa (κ)-index was measured as a biomarker of intrathecal B cell activation. Cognitive performance was assessed using the Brief Repeatable Battery of Neuropsychological Tests (BRBN). Brain T2 lesions number (T2LN) and volume (T2LV) together with brain, cortical grey matter, thalamic and hippocampal volumes were calculated to account for MRI-visible damage. RESULTS κ-index was higher in pwMS with verbal memory impairment (median 99.6, range 58.5-195.2 vs. median 37.2, range 2.3-396.9, p < 0.001), and it was negatively associated with BRBN tests exploring verbal memory and information processing speed. In multivariate models, higher κ-index was confirmed to be independently associated with worse scores of BRBN tests exploring verbal memory and with a higher probability of verbal memory impairment. CONCLUSION Intrathecal B cells might drive memory impairment in pwMS independently of brain damage visible on MRI scans.
Collapse
Affiliation(s)
- Lorenzo Gaetani
- Section of Neurology, Department of Medicine and Surgery, University of Perugia, Piazzale Severi 8, Perugia 06132, Italy..
| | - Nicola Salvadori
- Section of Neurology, Department of Medicine and Surgery, University of Perugia, Piazzale Severi 8, Perugia 06132, Italy
| | - Giovanni Brachelente
- Clinical Pathology Laboratory, University Hospital S. Maria della Misericordia, Perugia, Italy
| | - Silvia Sperandei
- Section of Neurology, Department of Medicine and Surgery, University of Perugia, Piazzale Severi 8, Perugia 06132, Italy
| | - Elena Di Sabatino
- Section of Neurology, Department of Medicine and Surgery, University of Perugia, Piazzale Severi 8, Perugia 06132, Italy
| | - Andrea Fiacca
- Section of Neuroradiology, University Hospital S. Maria della Misericordia, Perugia, Italy
| | - Andrea Mancini
- Section of Neurology, Department of Medicine and Surgery, University of Perugia, Piazzale Severi 8, Perugia 06132, Italy
| | - Alfredo Villa
- Clinical Pathology Laboratory, University Hospital S. Maria della Misericordia, Perugia, Italy
| | - Nicola De Stefano
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Lucilla Parnetti
- Section of Neurology, Department of Medicine and Surgery, University of Perugia, Piazzale Severi 8, Perugia 06132, Italy
| | - Massimiliano Di Filippo
- Section of Neurology, Department of Medicine and Surgery, University of Perugia, Piazzale Severi 8, Perugia 06132, Italy
| |
Collapse
|
2
|
Wiltgen T, McGinnis J, Schlaeger S, Kofler F, Voon C, Berthele A, Bischl D, Grundl L, Will N, Metz M, Schinz D, Sepp D, Prucker P, Schmitz-Koep B, Zimmer C, Menze B, Rueckert D, Hemmer B, Kirschke J, Mühlau M, Wiestler B. LST-AI: A deep learning ensemble for accurate MS lesion segmentation. Neuroimage Clin 2024; 42:103611. [PMID: 38703470 PMCID: PMC11088188 DOI: 10.1016/j.nicl.2024.103611] [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: 03/08/2024] [Revised: 04/19/2024] [Accepted: 04/23/2024] [Indexed: 05/06/2024]
Abstract
Automated segmentation of brain white matter lesions is crucial for both clinical assessment and scientific research in multiple sclerosis (MS). Over a decade ago, we introduced an engineered lesion segmentation tool, LST. While recent lesion segmentation approaches have leveraged artificial intelligence (AI), they often remain proprietary and difficult to adopt. As an open-source tool, we present LST-AI, an advanced deep learning-based extension of LST that consists of an ensemble of three 3D U-Nets. LST-AI explicitly addresses the imbalance between white matter (WM) lesions and non-lesioned WM. It employs a composite loss function incorporating binary cross-entropy and Tversky loss to improve segmentation of the highly heterogeneous MS lesions. We train the network ensemble on 491 MS pairs of T1-weighted and FLAIR images, collected in-house from a 3T MRI scanner, and expert neuroradiologists manually segmented the utilized lesion maps for training. LST-AI also includes a lesion location annotation tool, labeling lesions as periventricular, infratentorial, and juxtacortical according to the 2017 McDonald criteria, and, additionally, as subcortical. We conduct evaluations on 103 test cases consisting of publicly available data using the Anima segmentation validation tools and compare LST-AI with several publicly available lesion segmentation models. Our empirical analysis shows that LST-AI achieves superior performance compared to existing methods. Its Dice and F1 scores exceeded 0.62, outperforming LST, SAMSEG (Sequence Adaptive Multimodal SEGmentation), and the popular nnUNet framework, which all scored below 0.56. Notably, LST-AI demonstrated exceptional performance on the MSSEG-1 challenge dataset, an international WM lesion segmentation challenge, with a Dice score of 0.65 and an F1 score of 0.63-surpassing all other competing models at the time of the challenge. With increasing lesion volume, the lesion detection rate rapidly increased with a detection rate of >75% for lesions with a volume between 10 mm3 and 100 mm3. Given its higher segmentation performance, we recommend that research groups currently using LST transition to LST-AI. To facilitate broad adoption, we are releasing LST-AI as an open-source model, available as a command-line tool, dockerized container, or Python script, enabling diverse applications across multiple platforms.
Collapse
Affiliation(s)
- Tun Wiltgen
- Department of Neurology, School of Medicine, Technical University of Munich, Munich, Germany; TUM-Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany
| | - Julian McGinnis
- Department of Neurology, School of Medicine, Technical University of Munich, Munich, Germany; TUM-Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany; Department of Computer Science, Institute for AI in Medicine, Technical University of Munich, Munich, Germany
| | - Sarah Schlaeger
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany
| | - Florian Kofler
- Department of Computer Science, Institute for AI in Medicine, Technical University of Munich, Munich, Germany; Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany; TranslaTUM, Central Institute for Translational Cancer Research of the Technical University of Munich, Munich, Germany; Helmholtz AI, Helmholtz Munich, Neuherberg, Germany
| | - CuiCi Voon
- Department of Neurology, School of Medicine, Technical University of Munich, Munich, Germany; TUM-Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany
| | - Achim Berthele
- Department of Neurology, School of Medicine, Technical University of Munich, Munich, Germany
| | - Daria Bischl
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany
| | - Lioba Grundl
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany
| | - Nikolaus Will
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany
| | - Marie Metz
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany
| | - David Schinz
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany; Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Dominik Sepp
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany
| | - Philipp Prucker
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany
| | - Benita Schmitz-Koep
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany
| | - Claus Zimmer
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany
| | - Bjoern Menze
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Daniel Rueckert
- Department of Computer Science, Institute for AI in Medicine, Technical University of Munich, Munich, Germany; Department of Computing, Imperial College London, London, United Kingdom
| | - Bernhard Hemmer
- Department of Neurology, School of Medicine, Technical University of Munich, Munich, Germany; Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Jan Kirschke
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany
| | - Mark Mühlau
- Department of Neurology, School of Medicine, Technical University of Munich, Munich, Germany; TUM-Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany.
| | - Benedikt Wiestler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany; TranslaTUM, Central Institute for Translational Cancer Research of the Technical University of Munich, Munich, Germany; AI for Image-Guided Diagnosis and Therapy, School of Medicine, Technical University of Munich, Munich, Germany
| |
Collapse
|
3
|
Wiltgen T, McGinnis J, Schlaeger S, Kofler F, Voon C, Berthele A, Bischl D, Grundl L, Will N, Metz M, Schinz D, Sepp D, Prucker P, Schmitz-Koep B, Zimmer C, Menze B, Rueckert D, Hemmer B, Kirschke J, Mühlau M, Wiestler B. LST-AI: a Deep Learning Ensemble for Accurate MS Lesion Segmentation. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.11.23.23298966. [PMID: 38045345 PMCID: PMC10690346 DOI: 10.1101/2023.11.23.23298966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
Automated segmentation of brain white matter lesions is crucial for both clinical assessment and scientific research in multiple sclerosis (MS). Over a decade ago, we introduced an engineered lesion segmentation tool, LST. While recent lesion segmentation approaches have leveraged artificial intelligence (AI), they often remain proprietary and difficult to adopt. As an open-source tool, we present LST-AI, an advanced deep learning-based extension of LST that consists of an ensemble of three 3D-UNets. LST-AI explicitly addresses the imbalance between white matter (WM) lesions and non-lesioned WM. It employs a composite loss function incorporating binary cross-entropy and Tversky loss to improve segmentation of the highly heterogeneous MS lesions. We train the network ensemble on 491 MS pairs of T1w and FLAIR images, collected in-house from a 3T MRI scanner, and expert neuroradiologists manually segmented the utilized lesion maps for training. LST-AI additionally includes a lesion location annotation tool, labeling lesion location according to the 2017 McDonald criteria (periventricular, infratentorial, juxtacortical, subcortical). We conduct evaluations on 103 test cases consisting of publicly available data using the Anima segmentation validation tools and compare LST-AI with several publicly available lesion segmentation models. Our empirical analysis shows that LST-AI achieves superior performance compared to existing methods. Its Dice and F1 scores exceeded 0.62, outperforming LST, SAMSEG (Sequence Adaptive Multimodal SEGmentation), and the popular nnUNet framework, which all scored below 0.56. Notably, LST-AI demonstrated exceptional performance on the MSSEG-1 challenge dataset, an international WM lesion segmentation challenge, with a Dice score of 0.65 and an F1 score of 0.63-surpassing all other competing models at the time of the challenge. With increasing lesion volume, the lesion detection rate rapidly increased with a detection rate of >75% for lesions with a volume between 10mm3 and 100mm3. Given its higher segmentation performance, we recommend that research groups currently using LST transition to LST-AI. To facilitate broad adoption, we are releasing LST-AI as an open-source model, available as a command-line tool, dockerized container, or Python script, enabling diverse applications across multiple platforms.
Collapse
Affiliation(s)
- Tun Wiltgen
- Department of Neurology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TUM-Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany
| | - Julian McGinnis
- Department of Neurology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TUM-Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany
- Department of Computer Science, Institute for AI in Medicine, Technical University of Munich, Munich, Germany
| | - Sarah Schlaeger
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Florian Kofler
- Department of Computer Science, Institute for AI in Medicine, Technical University of Munich, Munich, Germany
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TranslaTUM, Center for Translational Cancer Research, Munich, Germany
- Helmholtz AI, Helmholtz Munich, Neuherberg, Germany
| | - CuiCi Voon
- Department of Neurology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TUM-Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany
| | - Achim Berthele
- Department of Neurology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Daria Bischl
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Lioba Grundl
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Nikolaus Will
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Marie Metz
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - David Schinz
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Dominik Sepp
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Philipp Prucker
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Benita Schmitz-Koep
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Claus Zimmer
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Bjoern Menze
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Daniel Rueckert
- Department of Computer Science, Institute for AI in Medicine, Technical University of Munich, Munich, Germany
- Department of Computing, Imperial College London, London, United Kingdom
| | - Bernhard Hemmer
- Department of Neurology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Jan Kirschke
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Mark Mühlau
- Department of Neurology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TUM-Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany
| | - Benedikt Wiestler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TranslaTUM, Center for Translational Cancer Research, Munich, Germany
| |
Collapse
|