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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] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [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.
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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
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Voon CC, Wiltgen T, Wiestler B, Schlaeger S, Mühlau M. Quantitative susceptibility mapping in multiple sclerosis: A systematic review and meta-analysis. Neuroimage Clin 2024; 42:103598. [PMID: 38582068 PMCID: PMC11002889 DOI: 10.1016/j.nicl.2024.103598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 03/07/2024] [Accepted: 03/24/2024] [Indexed: 04/08/2024]
Abstract
BACKGROUND Quantitative susceptibility mapping (QSM) is a quantitative measure based on magnetic resonance imaging sensitive to iron and myelin content. This makes QSM a promising non-invasive tool for multiple sclerosis (MS) in research and clinical practice. OBJECTIVE We performed a systematic review and meta-analysis on the use of QSM in MS. METHODS Our review was prospectively registered on PROSPERO (CRD42022309563). We searched five databases for studies published between inception and 30th April 2023. We identified 83 English peer-reviewed studies that applied QSM images on MS cohorts. Fifty-five included studies had at least one of the following outcome measures: deep grey matter QSM values in MS, either compared to healthy controls (HC) (k = 13) or correlated with the score on the Expanded Disability Status Scale (EDSS) (k = 7), QSM lesion characteristics (k = 22) and their clinical correlates (k = 17), longitudinal correlates (k = 11), histological correlates (k = 7), or correlates with other imaging techniques (k = 12). Two meta-analyses on deep grey matter (DGM) susceptibility data were performed, while the remaining findings could only be analyzed descriptively. RESULTS After outlier removal, meta-analyses demonstrated a significant increase in the basal ganglia susceptibility (QSM values) in MS compared to HC, caudate (k = 9, standardized mean difference (SDM) = 0.54, 95 % CI = 0.39-0.70, I2 = 46 %), putamen (k = 9, SDM = 0.38, 95 % CI = 0.19-0.57, I2 = 59 %), and globus pallidus (k = 9, SDM = 0.48, 95 % CI = 0.28-0.67, I2 = 60 %), whereas thalamic QSM values exhibited a significant reduction (k = 12, SDM = -0.39, 95 % CI = -0.66--0.12, I2 = 84 %); these susceptibility differences in MS were independent of age. Further, putamen QSM values positively correlated with EDSS (k = 4, r = 0.36, 95 % CI = 0.16-0.53, I2 = 0 %). Regarding rim lesions, four out of seven studies, representing 73 % of all patients, reported rim lesions to be associated with more severe disability. Moreover, lesion development from initial detection to the inactive stage is paralleled by increasing, plateauing (after about two years), and gradually decreasing QSM values, respectively. Only one longitudinal study provided clinical outcome measures and found no association. Histological data suggest iron content to be the primary source of QSM values in DGM and at the edges of rim lesions; further, when also considering data from myelin water imaging, the decrease of myelin is likely to drive the increase of QSM values within WM lesions. CONCLUSIONS We could provide meta-analytic evidence for DGM susceptibility changes in MS compared to HC; basal ganglia susceptibility is increased and, in the putamen, associated with disability, while thalamic susceptibility is decreased. Beyond these findings, further investigations are necessary to establish the role of QSM in MS for research or even clinical routine.
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Affiliation(s)
- Cui Ci Voon
- Dept. of Neurology, School of Medicine and Health, Technical University of Munich, Munich, Germany; TUM-Neuroimaging Center, School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Tun Wiltgen
- Dept. of Neurology, School of Medicine and Health, Technical University of Munich, Munich, Germany; TUM-Neuroimaging Center, School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Benedikt Wiestler
- Dept. of Neuroradiology, School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Sarah Schlaeger
- Dept. of Neuroradiology, School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Mark Mühlau
- Dept. of Neurology, School of Medicine and Health, Technical University of Munich, Munich, Germany; TUM-Neuroimaging Center, School of Medicine and Health, Technical University of Munich, Munich, Germany.
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Wiltgen T, Voon C, Van Leemput K, Wiestler B, Mühlau M. Intensity scaling of conventional brain magnetic resonance images avoiding cerebral reference regions: A systematic review. PLoS One 2024; 19:e0298642. [PMID: 38483873 PMCID: PMC10939249 DOI: 10.1371/journal.pone.0298642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 01/26/2024] [Indexed: 03/17/2024] Open
Abstract
BACKGROUND Conventional brain magnetic resonance imaging (MRI) produces image intensities that have an arbitrary scale, hampering quantification. Intensity scaling aims to overcome this shortfall. As neurodegenerative and inflammatory disorders may affect all brain compartments, reference regions within the brain may be misleading. Here we summarize approaches for intensity scaling of conventional T1-weighted (w) and T2w brain MRI avoiding reference regions within the brain. METHODS Literature was searched in the databases of Scopus, PubMed, and Web of Science. We included only studies that avoided reference regions within the brain for intensity scaling and provided validating evidence, which we divided into four categories: 1) comparative variance reduction, 2) comparative correlation with clinical parameters, 3) relation to quantitative imaging, or 4) relation to histology. RESULTS Of the 3825 studies screened, 24 fulfilled the inclusion criteria. Three studies used scaled T1w images, 2 scaled T2w images, and 21 T1w/T2w-ratio calculation (with double counts). A robust reduction in variance was reported. Twenty studies investigated the relation of scaled intensities to different types of quantitative imaging. Statistically significant correlations with clinical or demographic data were reported in 8 studies. Four studies reporting the relation to histology gave no clear picture of the main signal driver of conventional T1w and T2w MRI sequences. CONCLUSIONS T1w/T2w-ratio calculation was applied most often. Variance reduction and correlations with other measures suggest a biologically meaningful signal harmonization. However, there are open methodological questions and uncertainty on its biological underpinning. Validation evidence on other scaling methods is even sparser.
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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
| | - 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
| | - Koen Van Leemput
- Department of Neuroscience and Biomedical Engineering, Aalto University Helsinki, Espoo, Finland
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Benedikt Wiestler
- Department of 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
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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 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] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [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.
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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
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Tahedl M, Wiltgen T, Voon CC, Berthele A, Kirschke JS, Hemmer B, Mühlau M, Zimmer C, Wiestler B. Cortical Thin Patch Fraction Reflects Disease Burden in MS: The Mosaic Approach. AJNR Am J Neuroradiol 2023; 45:82-89. [PMID: 38164526 PMCID: PMC10756581 DOI: 10.3174/ajnr.a8064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 10/18/2023] [Indexed: 01/03/2024]
Abstract
BACKGROUND AND PURPOSE GM pathology plays an essential role in MS disability progression, emphasizing the importance of neuroradiologic biomarkers to capture the heterogeneity of cortical disease burden. This study aimed to assess the validity of a patch-wise, individual interpretation of cortical thickness data to identify GM pathology, the "mosaic approach," which was previously suggested as a biomarker for assessing and localizing atrophy. MATERIALS AND METHODS We investigated the mosaic approach in a cohort of 501 patients with MS with respect to 89 internal and 651 external controls. The resulting metric of the mosaic approach is the so-called thin patch fraction, which is an estimate of overall cortical disease burden per patient. We evaluated the mosaic approach with respect to the following: 1) discrimination between patients with MS and controls, 2) classification between different MS phenotypes, and 3) association with established biomarkers reflecting MS disease burden, using general linear modeling. RESULTS The thin patch fraction varied significantly between patients with MS and healthy controls and discriminated among MS phenotypes. Furthermore, the thin patch fraction was associated with disease burden, including the Expanded Disability Status Scale, cognitive and fatigue scores, and lesion volume. CONCLUSIONS This study demonstrates the validity of the mosaic approach as a neuroradiologic biomarker in MS. The output of the mosaic approach, namely the thin patch fraction, is a candidate biomarker for assessing and localizing cortical GM pathology. The mosaic approach can furthermore enhance the development of a personalized cortical MS biomarker, given that the thin patch fraction provides a feature on which artificial intelligence methods can be trained. Most important, we showed the validity of the mosaic approach when referencing data with respect to external control MR imaging repositories.
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Affiliation(s)
- Marlene Tahedl
- From the Department of Neuroradiology (M.T., J.S.K., C.Z., B.W.), School of Medicine, Technical University of Munich, Munich, Germany
| | - Tun Wiltgen
- Department of Neurology (T.W., C.C.V., A.B., B.H., M.M.), School of Medicine, Technical University of Munich, Munich, Germany
| | - Cui Ci Voon
- Department of Neurology (T.W., C.C.V., A.B., B.H., M.M.), School of Medicine, Technical University of Munich, Munich, Germany
| | - Achim Berthele
- Department of Neurology (T.W., C.C.V., A.B., B.H., M.M.), School of Medicine, Technical University of Munich, Munich, Germany
| | - Jan S Kirschke
- From the Department of Neuroradiology (M.T., J.S.K., C.Z., B.W.), School of Medicine, Technical University of Munich, Munich, Germany
| | - Bernhard Hemmer
- Department of Neurology (T.W., C.C.V., A.B., B.H., M.M.), School of Medicine, Technical University of Munich, Munich, Germany
- Munich Cluster for Systems Neurology (B.H.), Munich, Germany
| | - Mark Mühlau
- Department of Neurology (T.W., C.C.V., A.B., B.H., M.M.), School of Medicine, Technical University of Munich, Munich, Germany
| | - Claus Zimmer
- From the Department of Neuroradiology (M.T., J.S.K., C.Z., B.W.), School of Medicine, Technical University of Munich, Munich, Germany
| | - Benedikt Wiestler
- From the Department of Neuroradiology (M.T., J.S.K., C.Z., B.W.), School of Medicine, Technical University of Munich, Munich, Germany
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6
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Gasperi C, Wiltgen T, McGinnis J, Cerri S, Moridi T, Ouellette R, Pukaj A, Voon C, Bafligil C, Lauerer M, Andlauer TFM, Held F, Aly L, Shchetynsky K, Stridh P, Harroud A, Wiestler B, Kirschke JS, Zimmer C, Baras A, Piehl F, Berthele A, Granberg T, Kockum I, Hemmer B, Mühlau M. A Genetic Risk Variant for Multiple Sclerosis Severity is Associated with Brain Atrophy. Ann Neurol 2023; 94:1080-1085. [PMID: 37753809 DOI: 10.1002/ana.26807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 09/22/2023] [Accepted: 09/25/2023] [Indexed: 09/28/2023]
Abstract
The minor allele of the genetic variant rs10191329 in the DYSF-ZNF638 locus is associated with unfavorable long-term clinical outcomes in multiple sclerosis patients. We investigated if rs10191329 is associated with brain atrophy measured by magnetic resonance imaging in a discovery cohort of 748 and a replication cohort of 360 people with relapsing multiple sclerosis. We observed an association with 28% more brain atrophy per rs10191329*A allele. Our results encourage stratification for rs10191329 in clinical trials. Unraveling the underlying mechanisms may enhance our understanding of pathophysiology and identify treatment targets. ANN NEUROL 2023;94:1080-1085.
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Affiliation(s)
- Christiane Gasperi
- Department of Neurology, School of Medicine, Technical University of Munich, Munich, Germany
| | - 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
- Institute for AI in Medicine, Technical University of Munich, Munich, Germany
| | - Stefano Cerri
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Thomas Moridi
- Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden
- Center for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden
| | - Russell Ouellette
- Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden
- Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden
| | - Albert Pukaj
- Department of Neurology, School of Medicine, Technical University of Munich, Munich, 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
| | - Cemsel Bafligil
- Department of Neurology, School of Medicine, Technical University of Munich, Munich, Germany
| | - Markus Lauerer
- Department of Neurology, School of Medicine, Technical University of Munich, Munich, Germany
- TUM-Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany
| | - Till F M Andlauer
- Department of Neurology, School of Medicine, Technical University of Munich, Munich, Germany
| | - Friederike Held
- Department of Neurology, School of Medicine, Technical University of Munich, Munich, Germany
| | - Lilian Aly
- Department of Neurology, School of Medicine, Technical University of Munich, Munich, Germany
| | | | - Pernilla Stridh
- Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden
| | - Adil Harroud
- Department of Neurology and Neurosurgery and Department of Human Genetics, McGill University, Montréal, Quebec, Canada
- The Neuro (Montreal Neurological Institute and Hospital), McGill University, Montréal, Quebec, Canada
| | - Benedikt Wiestler
- Department of Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany
| | - Jan S Kirschke
- Department of Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany
| | - Claus Zimmer
- Department of Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany
| | - Aris Baras
- Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, New York, USA
| | - Fredrik Piehl
- Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden
| | - Achim Berthele
- Department of Neurology, School of Medicine, Technical University of Munich, Munich, Germany
| | - Tobias Granberg
- Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden
- Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden
| | - Ingrid Kockum
- Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden
- Center for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden
| | - Bernhard Hemmer
- Department of Neurology, School of Medicine, Technical University of Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), 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
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Tahedl M, Wiltgen T, Voon CC, Berthele A, Kirschke JS, Hemmer B, Mühlau M, Zimmer C, Wiestler B. Benefits of a mosaic approach for assessing cortical atrophy in individual multiple sclerosis patients. Brain Behav 2023; 13:e3327. [PMID: 37961043 PMCID: PMC10726853 DOI: 10.1002/brb3.3327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 10/31/2023] [Accepted: 11/01/2023] [Indexed: 11/15/2023] Open
Abstract
OBJECTIVE Cortical gray matter (GM) atrophy plays a central role in multiple sclerosis (MS) pathology. However, it is not commonly assessed in clinical routine partly because a number of methodological problems hamper the development of a robust biomarker to quantify GM atrophy. In previous work, we have demonstrated the clinical utility of the "mosaic approach" (MAP) to assess individual GM atrophy in the motor neuron disease spectrum and frontotemporal dementia. In this study, we investigated the clinical utility of MAP in MS, comparing this novel biomarker to existing methods for computing GM atrophy in single patients. We contrasted the strategies based on correlations with established biomarkers reflecting MS disease burden. METHODS We analyzed T1-weighted MPRAGE magnetic resonance imaging data from 465 relapsing-remitting MS patients and 89 healthy controls. We inspected how variations of existing strategies to estimate individual GM atrophy ("standard approaches") as well as variations of MAP (i.e., different parcellation schemes) impact downstream analysis results, both on a group and an individual level. We interpreted individual cortical disease burden as single metric reflecting the fraction of significantly atrophic data points with respect to the control group. In addition, we evaluated the correlations to lesion volume (LV) and Expanded Disability Status Scale (EDSS). RESULTS We found that the MAP method yielded highest correlations with both LV and EDSS as compared to all other strategies. Although the parcellation resolution played a minor role in terms of absolute correlations with clinical variables, higher resolutions provided more clearly defined statistical brain maps which may facilitate clinical interpretability. CONCLUSION This study provides evidence that MAP yields high potential for a clinically relevant biomarker in MS, outperforming existing methods to compute cortical disease burden in single patients. Of note, MAP outputs brain maps illustrating individual cortical disease burden which can be directly interpreted in daily clinical routine.
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Affiliation(s)
- Marlene Tahedl
- Department of Neuroradiology, School of MedicineTechnical University of MunichMunichGermany
| | - Tun Wiltgen
- Department of Neurology, School of MedicineTechnical University of MunichMunichGermany
| | - Cui Ci Voon
- Department of Neurology, School of MedicineTechnical University of MunichMunichGermany
| | - Achim Berthele
- Department of Neurology, School of MedicineTechnical University of MunichMunichGermany
| | - Jan S. Kirschke
- Department of Neuroradiology, School of MedicineTechnical University of MunichMunichGermany
| | - Bernhard Hemmer
- Department of Neurology, School of MedicineTechnical University of MunichMunichGermany
| | - Mark Mühlau
- Department of Neurology, School of MedicineTechnical University of MunichMunichGermany
| | - Claus Zimmer
- Department of Neuroradiology, School of MedicineTechnical University of MunichMunichGermany
| | - Benedikt Wiestler
- Department of Neuroradiology, School of MedicineTechnical University of MunichMunichGermany
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8
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Romahn EF, Wiltgen T, Bussas M, Aly L, Wicklein R, Noll C, Berthele A, Dehmelt V, Mardin C, Zimmer C, Korn T, Hemmer B, Kirschke JS, Mühlau M, Knier B. Association of retinal vessel pathology and brain atrophy in relapsing-remitting multiple sclerosis. Front Immunol 2023; 14:1284986. [PMID: 38090586 PMCID: PMC10715309 DOI: 10.3389/fimmu.2023.1284986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 11/07/2023] [Indexed: 12/18/2023] Open
Abstract
Background Optical coherence tomography angiography (OCTA) allows non-invasive assessment of retinal vessel structures. Thinning and loss of retinal vessels is evident in eyes of patients with multiple sclerosis (MS) and might be associated with a proinflammatory disease phenotype and worse prognosis. We investigated whether changes of the retinal vasculature are linked to brain atrophy and disability in MS. Material and methods This study includes one longitudinal observational cohort (n=79) of patients with relapsing-remitting MS. Patients underwent annual assessment of the expanded disability status scale (EDSS), timed 25-foot walk, symbol digit modalities test (SDMT), retinal optical coherence tomography (OCT), OCTA, and brain MRI during a follow-up duration of at least 20 months. We investigated intra-individual associations between changes in the retinal architecture, vasculature, brain atrophy and disability. Eyes with a history of optic neuritis (ON) were excluded. Results We included 79 patients with a median disease duration of 12 (interquartile range 2 - 49) months and a median EDSS of 1.0 (0 - 2.0). Longitudinal retinal axonal and ganglion cell loss were linked to grey matter atrophy, cortical atrophy, and volume loss of the putamen. We observed an association between vessel loss of the superficial vascular complex (SVC) and both grey and white matter atrophy. Both observations were independent of retinal ganglion cell loss. Moreover, patients with worsening of the EDSS and SDMT revealed a pronounced longitudinal rarefication of the SVC and the deep vascular complex. Discussion ON-independent narrowing of the retinal vasculature might be linked to brain atrophy and disability in MS. Our findings suggest that retinal OCTA might be a new tool for monitoring neurodegeneration during MS.
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Affiliation(s)
- Eva Feodora Romahn
- Department of Neurology, Klinikum rechts der Isar, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Tun Wiltgen
- Department of Neurology, Klinikum rechts der Isar, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Matthias Bussas
- Department of Neurology, Klinikum rechts der Isar, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Lilian Aly
- Department of Neurology, Klinikum rechts der Isar, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Rebecca Wicklein
- Department of Neurology, Klinikum rechts der Isar, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Christina Noll
- Department of Neurology, Klinikum rechts der Isar, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Achim Berthele
- Department of Neurology, Klinikum rechts der Isar, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Vera Dehmelt
- Department of Neurology, Klinikum rechts der Isar, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Christian Mardin
- Department of Ophthalmology, University Hospital of Erlangen-Nuremberg, Erlangen, Germany
| | - Claus Zimmer
- Department of Neuroradiology, Klinikum rechts der Isar, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Thomas Korn
- Department of Neurology, Klinikum rechts der Isar, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
- Institute for Experimental Neuroimmunology, Klinikum rechts der Isar, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Bernhard Hemmer
- Department of Neurology, Klinikum rechts der Isar, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Jan S. Kirschke
- Department of Neuroradiology, Klinikum rechts der Isar, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Mark Mühlau
- Department of Neurology, Klinikum rechts der Isar, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Benjamin Knier
- Department of Neurology, Klinikum rechts der Isar, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
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9
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Wiltgen T, Fleischmann DF, Kaiser L, Holzgreve A, Corradini S, Landry G, Ingrisch M, Popp I, Grosu AL, Unterrainer M, Bartenstein P, Parodi K, Belka C, Albert N, Niyazi M, Riboldi M. 18F-FET PET radiomics-based survival prediction in glioblastoma patients receiving radio(chemo)therapy. Radiat Oncol 2022; 17:198. [DOI: 10.1186/s13014-022-02164-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Accepted: 10/07/2022] [Indexed: 12/04/2022] Open
Abstract
Abstract
Background
Quantitative image analysis based on radiomic feature extraction is an emerging field for survival prediction in oncological patients. 18F-Fluorethyltyrosine positron emission tomography (18F-FET PET) provides important diagnostic and grading information for brain tumors, but data on its use in survival prediction is scarce. In this study, we aim at investigating survival prediction based on multiple radiomic features in glioblastoma patients undergoing radio(chemo)therapy.
Methods
A dataset of 37 patients with glioblastoma (WHO grade 4) receiving radio(chemo)therapy was analyzed. Radiomic features were extracted from pre-treatment 18F-FET PET images, following intensity rebinning with a fixed bin width. Principal component analysis (PCA) was applied for variable selection, aiming at the identification of the most relevant features in survival prediction. Random forest classification and prediction algorithms were optimized on an initial set of 25 patients. Testing of the implemented algorithms was carried out in different scenarios, which included additional 12 patients whose images were acquired with a different scanner to check the reproducibility in prediction results.
Results
First order intensity variations and shape features were predominant in the selection of most important radiomic signatures for survival prediction in the available dataset. The major axis length of the 18F-FET-PET volume at tumor to background ratio (TBR) 1.4 and 1.6 correlated significantly with reduced probability of survival. Additional radiomic features were identified as potential survival predictors in the PTV region, showing 76% accuracy in independent testing for both classification and regression.
Conclusions
18F-FET PET prior to radiation provides relevant information for survival prediction in glioblastoma patients. Based on our preliminary analysis, radiomic features in the PTV can be considered a robust dataset for survival prediction.
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