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Karthik EN, Valosek J, Smith AC, Pfyffer D, Schading-Sassenhausen S, Farner L, Weber KA, Freund P, Cohen-Adad J. SCIseg: Automatic Segmentation of T2-weighted Intramedullary Lesions in Spinal Cord Injury. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.03.24300794. [PMID: 38699309 PMCID: PMC11065035 DOI: 10.1101/2024.01.03.24300794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
Purpose To develop a deep learning tool for the automatic segmentation of T2-weighted intramedullary lesions in spinal cord injury (SCI). Material and Methods This retrospective study included a cohort of SCI patients from three sites enrolled between July 2002 and February 2023. A deep learning model, SCIseg, was trained in a three-phase process involving active learning for the automatic segmentation of intramedullary SCI lesions and the spinal cord. The data consisted of T2-weighted MRI acquired using different scanner manufacturers with heterogeneous image resolutions (isotropic/anisotropic), orientations (axial/sagittal), lesion etiologies (traumatic/ischemic/hemorrhagic) and lesions spread across the cervical, thoracic and lumbar spine. The segmentations from the proposed model were visually and quantitatively compared with other open-source baselines. Wilcoxon signed-rank test was used to compare quantitative MRI biomarkers (lesion volume, lesion length, and maximal axial damage ratio) computed from manual lesion masks and those obtained automatically with SCIseg predictions. Results MRI data from 191 SCI patients (mean age, 48.1 years ± 17.9 [SD]; 142 males) were used for model training and evaluation. SCIseg achieved the best segmentation performance for both the cord and lesions. There was no statistically significant difference between lesion length and maximal axial damage ratio computed from manually annotated lesions and those obtained using SCIseg. Conclusion Automatic segmentation of intramedullary lesions commonly seen in SCI replaces the tedious manual annotation process and enables the extraction of relevant lesion morphometrics in large cohorts. The proposed model segments lesions across different etiologies, scanner manufacturers, and heterogeneous image resolutions. SCIseg is open-source and accessible through the Spinal Cord Toolbox.
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Affiliation(s)
- Enamundram Naga Karthik
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
- Mila - Quebec AI Institute, Montreal, QC, Canada
| | - Jan Valosek
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
- Mila - Quebec AI Institute, Montreal, QC, Canada
- Department of Neurosurgery, Faculty of Medicine and Dentistry, Palacký University Olomouc, Olomouc, Czechia
- Department of Neurology, Faculty of Medicine and Dentistry, Palacký University Olomouc, Olomouc, Czechia
| | - Andrew C Smith
- Department of Physical Medicine and Rehabilitation Physical Therapy Program, University of Colorado School of Medicine, Aurora, Colorado, USA
| | - Dario Pfyffer
- Spinal Cord Injury Center, Balgrist University Hospital, University of Zürich, Zürich, Switzerland
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California, USA
| | | | - Lynn Farner
- Spinal Cord Injury Center, Balgrist University Hospital, University of Zürich, Zürich, Switzerland
| | - Kenneth A Weber
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Patrick Freund
- Spinal Cord Injury Center, Balgrist University Hospital, University of Zürich, Zürich, Switzerland
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Julien Cohen-Adad
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
- Mila - Quebec AI Institute, Montreal, QC, Canada
- Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montreal, QC, Canada
- Centre de Recherche du CHU Sainte-Justine, Université de Montréal, Montreal, QC, Canada
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Dreizin D, Staziaki PV, Khatri GD, Beckmann NM, Feng Z, Liang Y, Delproposto ZS, Klug M, Spann JS, Sarkar N, Fu Y. Artificial intelligence CAD tools in trauma imaging: a scoping review from the American Society of Emergency Radiology (ASER) AI/ML Expert Panel. Emerg Radiol 2023; 30:251-265. [PMID: 36917287 PMCID: PMC10640925 DOI: 10.1007/s10140-023-02120-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 02/27/2023] [Indexed: 03/16/2023]
Abstract
BACKGROUND AI/ML CAD tools can potentially improve outcomes in the high-stakes, high-volume model of trauma radiology. No prior scoping review has been undertaken to comprehensively assess tools in this subspecialty. PURPOSE To map the evolution and current state of trauma radiology CAD tools along key dimensions of technology readiness. METHODS Following a search of databases, abstract screening, and full-text document review, CAD tool maturity was charted using elements of data curation, performance validation, outcomes research, explainability, user acceptance, and funding patterns. Descriptive statistics were used to illustrate key trends. RESULTS A total of 4052 records were screened, and 233 full-text articles were selected for content analysis. Twenty-one papers described FDA-approved commercial tools, and 212 reported algorithm prototypes. Works ranged from foundational research to multi-reader multi-case trials with heterogeneous external data. Scalable convolutional neural network-based implementations increased steeply after 2016 and were used in all commercial products; however, options for explainability were narrow. Of FDA-approved tools, 9/10 performed detection tasks. Dataset sizes ranged from < 100 to > 500,000 patients, and commercialization coincided with public dataset availability. Cross-sectional torso datasets were uniformly small. Data curation methods with ground truth labeling by independent readers were uncommon. No papers assessed user acceptance, and no method included human-computer interaction. The USA and China had the highest research output and frequency of research funding. CONCLUSIONS Trauma imaging CAD tools are likely to improve patient care but are currently in an early stage of maturity, with few FDA-approved products for a limited number of uses. The scarcity of high-quality annotated data remains a major barrier.
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Affiliation(s)
- David Dreizin
- Department of Diagnostic Radiology and Nuclear Medicine, R Adams Cowley Shock Trauma Center, University of Maryland School of Medicine, Baltimore, MD, USA.
| | - Pedro V Staziaki
- Cardiothoracic Imaging, Department of Radiology, Larner College of Medicine, University of Vermont, Burlington, VT, USA
| | - Garvit D Khatri
- Department of Radiology, University of Washington School of Medicine, Seattle, WA, USA
| | - Nicholas M Beckmann
- Memorial Hermann Orthopedic & Spine Hospital, McGovern Medical School at UTHealth, Houston, TX, USA
| | - Zhaoyong Feng
- Epidemiology & Public Health, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Yuanyuan Liang
- Epidemiology & Public Health, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Zachary S Delproposto
- Division of Emergency Radiology, Department of Radiology, University of Michigan, Ann Arbor, MI, USA
| | | | - J Stephen Spann
- Department of Radiology, University of Alabama at Birmingham Heersink School of Medicine, Birmingham, AL, USA
| | - Nathan Sarkar
- University of Maryland School of Medicine, Baltimore, MD, USA
| | - Yunting Fu
- Health Sciences and Human Services Library, University of Maryland, Baltimore, Baltimore, MD, USA
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Nozawa K, Maki S, Furuya T, Okimatsu S, Inoue T, Yunde A, Miura M, Shiratani Y, Shiga Y, Inage K, Eguchi Y, Ohtori S, Orita S. Magnetic resonance image segmentation of the compressed spinal cord in patients with degenerative cervical myelopathy using convolutional neural networks. Int J Comput Assist Radiol Surg 2023; 18:45-54. [PMID: 36342593 DOI: 10.1007/s11548-022-02783-0] [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: 04/06/2022] [Accepted: 10/20/2022] [Indexed: 11/09/2022]
Abstract
PURPOSE Spinal cord segmentation is the first step in atlas-based spinal cord image analysis, but segmentation of compressed spinal cords from patients with degenerative cervical myelopathy is challenging. We applied convolutional neural network models to segment the spinal cord from T2-weighted axial magnetic resonance images of DCM patients. Furthermore, we assessed the correlation between the cross-sectional area segmented by this network and the neurological symptoms of the patients. METHODS The CNN architecture was built using U-Net and DeepLabv3 + and PyTorch. The CNN was trained on 2762 axial slices from 174 patients, and an additional 517 axial slices from 33 patients were held out for validation and 777 axial slices from 46 patients for testing. The performance of the CNN was evaluated on a test dataset with Dice coefficients as the outcome measure. The ratio of CSA at the maximum compression level to CSA at the C2 level, as segmented by the CNN, was calculated. The correlation between the spinal cord CSA ratio and the Japanese Orthopaedic Association score in DCM patients from the test dataset was investigated using Spearman's rank correlation coefficient. RESULTS The best Dice coefficient was achieved when U-Net was used as the architecture and EfficientNet-b7 as the model for transfer learning. Spearman's rs between the spinal cord CSA ratio and the JOA score of DCM patients was 0.38 (p = 0.007), showing a weak correlation. CONCLUSION Using deep learning with magnetic resonance images of deformed spinal cords as training data, we were able to segment compressed spinal cords of DCM patients with a high concordance with expert manual segmentation. In addition, the spinal cord CSA ratio was weakly, but significantly, correlated with neurological symptoms. Our study demonstrated the first steps needed to implement automated atlas-based analysis of DCM patients.
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Affiliation(s)
- Kyohei Nozawa
- Department of Medical Engineering, Graduate School of Science and Engineering, Chiba University, Chiba, Japan
| | - Satoshi Maki
- Department of Orthopaedic Surgery, Chiba University Graduate School of Medicine, Chiba, Japan.
- Center for Frontier Medical Engineering, Chiba University, Chiba, Japan.
| | - Takeo Furuya
- Department of Orthopaedic Surgery, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Sho Okimatsu
- Department of Orthopaedic Surgery, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Takaki Inoue
- Department of Orthopaedic Surgery, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Atsushi Yunde
- Department of Orthopaedic Surgery, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Masataka Miura
- Department of Orthopaedic Surgery, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Yuki Shiratani
- Department of Orthopaedic Surgery, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Yasuhiro Shiga
- Department of Orthopaedic Surgery, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Kazuhide Inage
- Department of Orthopaedic Surgery, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Yawara Eguchi
- Department of Orthopaedic Surgery, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Seiji Ohtori
- Department of Orthopaedic Surgery, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Sumihisa Orita
- Department of Orthopaedic Surgery, Chiba University Graduate School of Medicine, Chiba, Japan
- Center for Frontier Medical Engineering, Chiba University, Chiba, Japan
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Fehlings MG, Pedro K, Hejrati N. Management of Acute Spinal Cord Injury: Where Have We Been? Where Are We Now? Where Are We Going? J Neurotrauma 2022; 39:1591-1602. [PMID: 35686453 DOI: 10.1089/neu.2022.0009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Affiliation(s)
- Michael G Fehlings
- Division of Genetics and Development, Krembil Brain Institute, University Health Network, Toronto, Ontario, Canada.,Institute of Medical Science, Department of Surgery, University of Toronto, Toronto, Ontario, Canada.,Division of Neurosurgery and Spine Program, Department of Surgery, University of Toronto, Toronto, Ontario, Canada
| | - Karlo Pedro
- Division of Neurosurgery and Spine Program, Department of Surgery, University of Toronto, Toronto, Ontario, Canada
| | - Nader Hejrati
- Division of Genetics and Development, Krembil Brain Institute, University Health Network, Toronto, Ontario, Canada.,Division of Neurosurgery and Spine Program, Department of Surgery, University of Toronto, Toronto, Ontario, Canada
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Bueno A, Bosch I, Rodríguez A, Jiménez A, Carreres J, Fernández M, Marti-Bonmati L, Alberich-Bayarri A. Automated Cervical Spinal Cord Segmentation in Real-World MRI of Multiple Sclerosis Patients by Optimized Hybrid Residual Attention-Aware Convolutional Neural Networks. J Digit Imaging 2022; 35:1131-1142. [PMID: 35789447 PMCID: PMC9582086 DOI: 10.1007/s10278-022-00637-4] [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: 04/12/2021] [Revised: 03/22/2022] [Accepted: 04/09/2022] [Indexed: 10/17/2022] Open
Abstract
Magnetic resonance (MR) imaging is the most sensitive clinical tool in the diagnosis and monitoring of multiple sclerosis (MS) alterations. Spinal cord evaluation has gained interest in this clinical scenario in recent years, but, unlike the brain, there is a more limited choice of algorithms to assist spinal cord segmentation. Our goal was to investigate and develop an automatic MR cervical cord segmentation method, enabling automated and seamless spinal cord atrophy assessment and setting the stage for the development of an aggregated algorithm for the extraction of lesion-related imaging biomarkers. The algorithm was developed using a real-world MR imaging dataset of 121 MS patients (96 cases used as a training dataset and 25 cases as a validation dataset). Transversal, 3D T1-weighted gradient echo MR images (TE/TR/FA = 1.7-2.7 ms/5.6-8.2 ms/12°) were acquired in a 3 T system (Signa HD, GEHC) as standard of care in our clinical practice. Experienced radiologists supervised the manual labelling, which was considered the ground-truth. The 2D convolutional neural network consisted of a hybrid residual attention-aware segmentation method trained to delineate the cervical spinal cord. The training was conducted using a focal loss function, based on the Tversky index to address label imbalance, and an automatic optimal learning rate finder. Our automated model provided an accurate segmentation, achieving a validation DICE coefficient of 0.904 ± 0.101 compared with the manual delineation. An automatic method for cervical spinal cord segmentation on T1-weighted MR images was successfully implemented. It will have direct implications serving as the first step for accelerating the process for MS staging and follow-up through imaging biomarkers.
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Affiliation(s)
- América Bueno
- Instituto de Tecnologías y Aplicaciones Multimedia, Universitat Politècnica de Valencia, Valencia, Spain.
| | - Ignacio Bosch
- Instituto de Tecnologías y Aplicaciones Multimedia, Universitat Politècnica de Valencia, Valencia, Spain
| | - Alejandro Rodríguez
- Biomedical Imaging Research Group (GIBI230), Hospital Universitario y Politécnico e Instituto de Investigación Sanitaria La Fe, Valencia, Spain
| | - Ana Jiménez
- Quantitative Imaging Biomarkers in Medicine, QUIBIM S.L, Valencia, Spain
| | - Joan Carreres
- Radiology Department, Hospital Universitario y Politécnico La Fe, Valencia, Spain
| | - Matías Fernández
- Biomedical Imaging Research Group (GIBI230), Hospital Universitario y Politécnico e Instituto de Investigación Sanitaria La Fe, Valencia, Spain
| | - Luis Marti-Bonmati
- Biomedical Imaging Research Group (GIBI230), Hospital Universitario y Politécnico e Instituto de Investigación Sanitaria La Fe, Valencia, Spain
- Imaging La Fe node at Distributed Network for Biomedical Imaging (ReDIB) Unique Scientific and Technical Infrastructures (ICTS), Valencia, Spain
| | - Angel Alberich-Bayarri
- Biomedical Imaging Research Group (GIBI230), Hospital Universitario y Politécnico e Instituto de Investigación Sanitaria La Fe, Valencia, Spain
- Quantitative Imaging Biomarkers in Medicine, QUIBIM S.L, Valencia, Spain
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6
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Determining the short-term neurological prognosis for acute cervical spinal cord injury using machine learning. J Clin Neurosci 2022; 96:74-79. [PMID: 34998207 DOI: 10.1016/j.jocn.2021.11.037] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 10/25/2021] [Accepted: 11/30/2021] [Indexed: 11/22/2022]
Abstract
It is challenging to predict neurological outcomes of acute spinal cord injury (SCI) considering issues such as spinal shock and injury heterogeneity. Deep learning-based radiomics (DLR) were developed to quantify the radiographic characteristics automatically using a convolutional neural network (CNN), and to potentially allow the prognostic stratification of patients. We aimed to determine the functional prognosis of patients with cervical SCI using machine learning approach based on MRI and to assess the ability to predict the neurological outcomes. We retrospectively analyzed the medical records of SCI patients (n=215) who had undergone MRI and had an American Spinal cord Injury Association Impairment Scale (AIS) assessment at 1 month after injury, enrolled with a total of 294 MR images. Sagittal T2-weighted MR images were used for the CNN training and validation. The deep learning framework TensorFlow was used to construct the CNN architecture. After we calculated the probability of the AIS grade using the DLR, we built the identification model based upon the random forest using 3 features: the probability of each AIS grade obtained by the DLR method, age, and the initial AIS grade at admission. We performed a statistical evaluation between the actual and predicted AIS. The accuracy, precision, recall and f1 score of the ensemble model based on the DLR and RF were 0.714, 0.590, 0.565 and 0.567, respectively. The present study demonstrates that prediction of the short-term neurological outcomes for acute cervical spinal cord injury based on MRI using machine learning is feasible.
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Toufani H, Vard A, Adibi I. A pipeline to quantify spinal cord atrophy with deep learning: Application to differentiation of MS and NMOSD patients. Phys Med 2021; 89:51-62. [PMID: 34352676 DOI: 10.1016/j.ejmp.2021.07.030] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 06/28/2021] [Accepted: 07/21/2021] [Indexed: 01/04/2023] Open
Abstract
PURPOSE Quantitative measurement of various anatomical regions of the brain and spinal cord (SC) in MRI images are used as unique biomarkers to consider progress and effects of demyelinating diseases of the central nervous system. This paper presents a fully-automated image processing pipeline which quantifies the SC volume of MRI images. METHODS In the proposed pipeline, after conducting some pre-processing tasks, a deep convolutional network is utilized to segment the spinal cord cross-sectional area (SCCSA) of each slice. After full segmentation, certain extra slices interpolate between each two adjacent slices using the shape-based interpolation method. Then, a 3D model of the SC is reconstructed, and, by counting the voxels of it, the SC volume is calculated. The performance of the proposed method for the SCCSA segmentation is evaluated on 140 MRI images. Subsequently, to demonstrate the application of the proposed pipeline, we study the differentiations of SC atrophy between 38 Multiple Sclerosis (MS) and 25 Neuromyelitis Optica Spectrum Disorder (NMOSD) patients. RESULTS The experimental results of the SCCSA segmentation indicate that the proposed method, adapted by Mask R-CNN, presented the most satisfactory result with the average Dice coefficient of 0.96. For this method, statistical metrics including sensitivity, specificity, accuracy, and precision are 97.51%, 99.98%, 99.92%, and 98.04% respectively. Moreover, the t-test result (p-value = 0.00089) verified a significant difference between the SC atrophy of MS and NMOSD patients. CONCLUSION The pipeline efficiently quantifies the SC volume of MRI images and can be utilized as an affordable computer-aided tool for diagnostic purposes.
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Affiliation(s)
- Hediyeh Toufani
- Department of Bioelectrics and Biomedical Engineering, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran; Student Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Alireza Vard
- Department of Bioelectrics and Biomedical Engineering, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran; Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran.
| | - Iman Adibi
- Department of Neurology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran; Isfahan Neurosciences Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
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Mina Y, Azodi S, Dubuche T, Andrada F, Osuorah I, Ohayon J, Cortese I, Wu T, Johnson KR, Reich DS, Nair G, Jacobson S. Cervical and thoracic cord atrophy in multiple sclerosis phenotypes: Quantification and correlation with clinical disability. NEUROIMAGE-CLINICAL 2021; 30:102680. [PMID: 34215150 PMCID: PMC8131917 DOI: 10.1016/j.nicl.2021.102680] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Revised: 04/15/2021] [Accepted: 04/16/2021] [Indexed: 12/01/2022]
Abstract
Spinal cord atrophy is prevalent across multiple sclerosis phenotypes. It correlates with disability, especially in relapsing-remitting patients. This correlation can be demonstrated both cross-sectionally and longitudinally. Cervical atrophy is highly associated with disability and disease progression. Thoracic atrophy contributes to improved correlation and radiological subgrouping.
Objective We sought to characterize spinal cord atrophy along the entire spinal cord in the major multiple sclerosis (MS) phenotypes, and evaluate its correlation with clinical disability. Methods Axial T1-weighted images were automatically reformatted at each point along the cord. Spinal cord cross‐sectional area (SCCSA) were calculated from C1-T10 vertebral body levels and profile plots were compared across phenotypes. Average values from C2-3, C4-5, and T4-9 regions were compared across phenotypes and correlated with clinical scores, and then categorized as atrophic/normal based on z-scores derived from controls, to compare clinical scores between subgroups. In a subset of relapsing-remitting cases with longitudinal scans these regions were compared to change in clinical scores. Results The cross-sectional study consisted of 149 adults diagnosed with relapsing-remitting MS (RRMS), 49 with secondary-progressive MS (SPMS), 58 with primary-progressive MS (PPMS) and 48 controls. The longitudinal study included 78 RRMS cases. Compared to controls, all MS groups had smaller average regions except RRMS in T4-9 region. In all MS groups, SCCSA from all regions, particularly the cervical cord, correlated with most clinical measures. In the RRMS cohort, 22% of cases had at least one atrophic region, whereas in progressive MS the rate was almost 70%. Longitudinal analysis showed correlation between clinical disability and cervical cord thinning. Conclusions Spinal cord atrophy was prevalent across MS phenotypes, with regional measures from the RRMS cohort and the progressive cohort, including SPMS and PPMS, being correlated with disability. Longitudinal changes in the spinal cord were documented in RRMS cases, making it a potential marker for disease progression. While cervical SCCSA correlated with most disability and progression measures, inclusion of thoracic measurements improved this correlation and allowed for better subgrouping of spinal cord phenotypes. Cord atrophy is an important and easily obtainable imaging marker of clinical and sub-clinical progression in all MS phenotypes, and such measures can play a key role in patient selection for clinical trials.
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Affiliation(s)
- Yair Mina
- Viral Immunology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States; Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
| | - Shila Azodi
- Viral Immunology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States; Neuroimmunology Clinic, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
| | - Tsemacha Dubuche
- Viral Immunology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
| | - Frances Andrada
- Neuroimmunology Clinic, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
| | - Ikesinachi Osuorah
- Neuroimmunology Clinic, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
| | - Joan Ohayon
- Neuroimmunology Clinic, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
| | - Irene Cortese
- Neuroimmunology Clinic, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
| | - Tianxia Wu
- Clinical Trials Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
| | - Kory R Johnson
- Bioinformatics Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
| | - Daniel S Reich
- Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
| | - Govind Nair
- Neuroimmunology Clinic, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States; Quantitative MRI Core Facility, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States
| | - Steven Jacobson
- Viral Immunology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States.
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Kyritsis N, Torres-Espín A, Schupp PG, Huie JR, Chou A, Duong-Fernandez X, Thomas LH, Tsolinas RE, Hemmerle DD, Pascual LU, Singh V, Pan JZ, Talbott JF, Whetstone WD, Burke JF, DiGiorgio AM, Weinstein PR, Manley GT, Dhall SS, Ferguson AR, Oldham MC, Bresnahan JC, Beattie MS. Diagnostic blood RNA profiles for human acute spinal cord injury. J Exp Med 2021; 218:e20201795. [PMID: 33512429 PMCID: PMC7852457 DOI: 10.1084/jem.20201795] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 11/18/2020] [Accepted: 12/22/2020] [Indexed: 12/14/2022] Open
Abstract
Diagnosis of spinal cord injury (SCI) severity at the ultra-acute stage is of great importance for emergency clinical care of patients as well as for potential enrollment into clinical trials. The lack of a diagnostic biomarker for SCI has played a major role in the poor results of clinical trials. We analyzed global gene expression in peripheral white blood cells during the acute injury phase and identified 197 genes whose expression changed after SCI compared with healthy and trauma controls and in direct relation to SCI severity. Unsupervised coexpression network analysis identified several gene modules that predicted injury severity (AIS grades) with an overall accuracy of 72.7% and included signatures of immune cell subtypes. Specifically, for complete SCIs (AIS A), ROC analysis showed impressive specificity and sensitivity (AUC: 0.865). Similar precision was also shown for AIS D SCIs (AUC: 0.938). Our findings indicate that global transcriptomic changes in peripheral blood cells have diagnostic and potentially prognostic value for SCI severity.
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Affiliation(s)
- Nikos Kyritsis
- Weill Institute for Neurosciences, Brain and Spinal Injury Center, University of California, San Francisco, San Francisco, CA
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA
- Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, CA
| | - Abel Torres-Espín
- Weill Institute for Neurosciences, Brain and Spinal Injury Center, University of California, San Francisco, San Francisco, CA
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA
- Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, CA
| | - Patrick G. Schupp
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA
- Brain Tumor Center, University of California, San Francisco, San Francisco, CA
| | - J. Russell Huie
- Weill Institute for Neurosciences, Brain and Spinal Injury Center, University of California, San Francisco, San Francisco, CA
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA
- Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, CA
| | - Austin Chou
- Weill Institute for Neurosciences, Brain and Spinal Injury Center, University of California, San Francisco, San Francisco, CA
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA
- Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, CA
| | - Xuan Duong-Fernandez
- Weill Institute for Neurosciences, Brain and Spinal Injury Center, University of California, San Francisco, San Francisco, CA
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA
- Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, CA
| | - Leigh H. Thomas
- Weill Institute for Neurosciences, Brain and Spinal Injury Center, University of California, San Francisco, San Francisco, CA
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA
- Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, CA
| | - Rachel E. Tsolinas
- Weill Institute for Neurosciences, Brain and Spinal Injury Center, University of California, San Francisco, San Francisco, CA
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA
- Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, CA
| | - Debra D. Hemmerle
- Weill Institute for Neurosciences, Brain and Spinal Injury Center, University of California, San Francisco, San Francisco, CA
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA
- Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, CA
| | - Lisa U. Pascual
- Orthopaedic Trauma Institute, Department of Orthopaedic Surgery, University of California, San Francisco, San Francisco, CA
| | - Vineeta Singh
- Weill Institute for Neurosciences, Brain and Spinal Injury Center, University of California, San Francisco, San Francisco, CA
- Department of Neurology, University of California, San Francisco, San Francisco, CA
| | - Jonathan Z. Pan
- Weill Institute for Neurosciences, Brain and Spinal Injury Center, University of California, San Francisco, San Francisco, CA
- Department of Anesthesia and Perioperative Care, University of California, San Francisco, San Francisco, CA
| | - Jason F. Talbott
- Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, CA
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA
| | - William D. Whetstone
- Department of Emergency Medicine, University of California, San Francisco, San Francisco, CA
| | - John F. Burke
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA
| | - Anthony M. DiGiorgio
- Weill Institute for Neurosciences, Brain and Spinal Injury Center, University of California, San Francisco, San Francisco, CA
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA
- Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, CA
| | - Philip R. Weinstein
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA
- Department of Neurology, University of California, San Francisco, San Francisco, CA
- Weill Institute for Neurosciences, Institute for Neurodegenerative Diseases, Spine Center, University of California, San Francisco, San Francisco, CA
| | - Geoffrey T. Manley
- Weill Institute for Neurosciences, Brain and Spinal Injury Center, University of California, San Francisco, San Francisco, CA
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA
- Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, CA
| | - Sanjay S. Dhall
- Weill Institute for Neurosciences, Brain and Spinal Injury Center, University of California, San Francisco, San Francisco, CA
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA
- Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, CA
| | - Adam R. Ferguson
- Weill Institute for Neurosciences, Brain and Spinal Injury Center, University of California, San Francisco, San Francisco, CA
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA
- Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, CA
- San Francisco Veterans Affairs Healthcare System, San Francisco, CA
| | - Michael C. Oldham
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA
- Brain Tumor Center, University of California, San Francisco, San Francisco, CA
| | - Jacqueline C. Bresnahan
- Weill Institute for Neurosciences, Brain and Spinal Injury Center, University of California, San Francisco, San Francisco, CA
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA
- Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, CA
| | - Michael S. Beattie
- Weill Institute for Neurosciences, Brain and Spinal Injury Center, University of California, San Francisco, San Francisco, CA
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA
- Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, CA
- San Francisco Veterans Affairs Healthcare System, San Francisco, CA
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10
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Mummaneni N, Burke JF, DiGiorgio AM, Thomas LH, Duong-Fernandez X, Harris M, Pascual LU, Ferguson AR, Russell Huie J, Pan JZ, Hemmerle DD, Singh V, Torres-Espin A, Omondi C, Kyritsis N, Weinstein PR, Whetstone WD, Manley GT, Bresnahan JC, Beattie MS, Cohen-Adad J, Dhall SS, Talbott JF. Injury volume extracted from MRI predicts neurologic outcome in acute spinal cord injury: A prospective TRACK-SCI pilot study. J Clin Neurosci 2020; 82:231-236. [PMID: 33248950 DOI: 10.1016/j.jocn.2020.11.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 11/01/2020] [Indexed: 12/18/2022]
Abstract
Conventional MRI measures of traumatic spinal cord injury severity largely rely on 2-dimensional injury characteristics such as intramedullary lesion length and cord compression. Recent advances in spinal cord (SC) analysis have led to the development of a robust anatomic atlas incorporated into an open-source platform called the Spinal Cord Toolbox (SCT) that allows for quantitative volumetric injury analysis. In the current study, we evaluate the prognostic value of volumetric measures of spinal cord injury on MRI following registration of T2-weighted (T2w) images and segmented lesions from acute SCI patients with a standardized atlas. This IRB-approved prospective cohort study involved the image analysis of 60 blunt cervical SCI patients enrolled in the TRACK-SCI clinical research protocol. Axial T2w MRI data obtained within 24 h of injury were processed using the SCT. Briefly, SC MRIs were automatically segmented using the sct_deepseg_sc tool in the SCT and segmentations were manually corrected by a neuro-radiologist. Lesion volume data were used as predictor variables for correlation with lower extremity motor scores at discharge. Volumetric MRI measures of T2w signal abnormality comprising the SCI lesion accurately predict lower extremity motor scores at time of patient discharge. Similarly, MRI measures of injury volume significantly correlated with motor scores to a greater degree than conventional 2-D metrics of lesion size. The volume of total injury and of injured spinal cord motor regions on T2w MRI is significantly and independently associated with neurologic outcome at discharge after injury.
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Affiliation(s)
- Nikhil Mummaneni
- Brain and Spinal Injury Center, Zuckerberg San Francisco General Hospital, San Francisco, CA, USA
| | - John F Burke
- Brain and Spinal Injury Center, Zuckerberg San Francisco General Hospital, San Francisco, CA, USA; Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA.
| | - Anthony M DiGiorgio
- Brain and Spinal Injury Center, Zuckerberg San Francisco General Hospital, San Francisco, CA, USA; Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Leigh H Thomas
- Brain and Spinal Injury Center, Zuckerberg San Francisco General Hospital, San Francisco, CA, USA; Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA; Weill Institutes for Neuroscience, San Francisco, CA, USA
| | - Xuan Duong-Fernandez
- Brain and Spinal Injury Center, Zuckerberg San Francisco General Hospital, San Francisco, CA, USA; Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA; Weill Institutes for Neuroscience, San Francisco, CA, USA
| | - Mark Harris
- Brain and Spinal Injury Center, Zuckerberg San Francisco General Hospital, San Francisco, CA, USA; Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA; Weill Institutes for Neuroscience, San Francisco, CA, USA
| | - Lisa U Pascual
- Orthopedic Trauma Institute, Zuckerberg San Francisco General Hospital, San Francisco, CA, USA; Department of Orthopedic Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Adam R Ferguson
- Brain and Spinal Injury Center, Zuckerberg San Francisco General Hospital, San Francisco, CA, USA; Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA; Weill Institutes for Neuroscience, San Francisco, CA, USA; San Francisco Veterans Affairs Healthcare System, San Francisco, CA, USA
| | - J Russell Huie
- Brain and Spinal Injury Center, Zuckerberg San Francisco General Hospital, San Francisco, CA, USA; Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA; Weill Institutes for Neuroscience, San Francisco, CA, USA
| | - Jonathan Z Pan
- Brain and Spinal Injury Center, Zuckerberg San Francisco General Hospital, San Francisco, CA, USA; Department of Anesthesia and Perioperative Care, University of California San Francisco, San Francisco, CA, USA
| | - Debra D Hemmerle
- Brain and Spinal Injury Center, Zuckerberg San Francisco General Hospital, San Francisco, CA, USA; Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA; Weill Institutes for Neuroscience, San Francisco, CA, USA
| | - Vineeta Singh
- Brain and Spinal Injury Center, Zuckerberg San Francisco General Hospital, San Francisco, CA, USA; Weill Institutes for Neuroscience, San Francisco, CA, USA; Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Abel Torres-Espin
- Brain and Spinal Injury Center, Zuckerberg San Francisco General Hospital, San Francisco, CA, USA; Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Cleopa Omondi
- Brain and Spinal Injury Center, Zuckerberg San Francisco General Hospital, San Francisco, CA, USA; Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA; Weill Institutes for Neuroscience, San Francisco, CA, USA
| | - Nikos Kyritsis
- Brain and Spinal Injury Center, Zuckerberg San Francisco General Hospital, San Francisco, CA, USA; Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA; Weill Institutes for Neuroscience, San Francisco, CA, USA
| | - Phillip R Weinstein
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA; Weill Institutes for Neuroscience, San Francisco, CA, USA; Institute for Neurodegenerative Diseases, Spine Center, University of California San Francisco, San Francisco, CA, USA
| | - William D Whetstone
- Department of Emergency Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Geoffrey T Manley
- Brain and Spinal Injury Center, Zuckerberg San Francisco General Hospital, San Francisco, CA, USA; Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Jacqueline C Bresnahan
- Brain and Spinal Injury Center, Zuckerberg San Francisco General Hospital, San Francisco, CA, USA; Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA; Weill Institutes for Neuroscience, San Francisco, CA, USA
| | - Michael S Beattie
- Brain and Spinal Injury Center, Zuckerberg San Francisco General Hospital, San Francisco, CA, USA; Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA; Weill Institutes for Neuroscience, San Francisco, CA, USA
| | - Julien Cohen-Adad
- Polytechnique Montréal, Université de Montréal, Montréal, Quebec, Canada
| | - Sanjay S Dhall
- Brain and Spinal Injury Center, Zuckerberg San Francisco General Hospital, San Francisco, CA, USA; Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Jason F Talbott
- Brain and Spinal Injury Center, Zuckerberg San Francisco General Hospital, San Francisco, CA, USA; Department of Radiology and Biomedical Imaging, Zuckerberg San Francisco General Hospital, San Francisco, CA, USA
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11
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Duong MT, Rauschecker AM, Mohan S. Diverse Applications of Artificial Intelligence in Neuroradiology. Neuroimaging Clin N Am 2020; 30:505-516. [PMID: 33039000 DOI: 10.1016/j.nic.2020.07.003] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Recent advances in artificial intelligence (AI) and deep learning (DL) hold promise to augment neuroimaging diagnosis for patients with brain tumors and stroke. Here, the authors review the diverse landscape of emerging neuroimaging applications of AI, including workflow optimization, lesion segmentation, and precision education. Given the many modalities used in diagnosing neurologic diseases, AI may be deployed to integrate across modalities (MR imaging, computed tomography, PET, electroencephalography, clinical and laboratory findings), facilitate crosstalk among specialists, and potentially improve diagnosis in patients with trauma, multiple sclerosis, epilepsy, and neurodegeneration. Together, there are myriad applications of AI for neuroradiology."
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Affiliation(s)
- Michael Tran Duong
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, 3400 Spruce Street, 219 Dulles Building, Philadelphia, PA 19104, USA. https://twitter.com/MichaelDuongMD
| | - Andreas M Rauschecker
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, 513 Parnassus Avenue, Room S-261, San Francisco, CA 94143, USA. https://twitter.com/DrDreMDPhD
| | - Suyash Mohan
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, 3400 Spruce Street, 219 Dulles Building, Philadelphia, PA 19104, USA.
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12
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Azzarito M, Kyathanahally SP, Balbastre Y, Seif M, Blaiotta C, Callaghan MF, Ashburner J, Freund P. Simultaneous voxel-wise analysis of brain and spinal cord morphometry and microstructure within the SPM framework. Hum Brain Mapp 2020; 42:220-232. [PMID: 32991031 PMCID: PMC7721239 DOI: 10.1002/hbm.25218] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Revised: 08/05/2020] [Accepted: 09/09/2020] [Indexed: 12/12/2022] Open
Abstract
To validate a simultaneous analysis tool for the brain and cervical cord embedded in the statistical parametric mapping (SPM) framework, we compared trauma-induced macro- and microstructural changes in spinal cord injury (SCI) patients to controls. The findings were compared with results obtained from existing processing tools that assess the brain and spinal cord separately. A probabilistic brain-spinal cord template (BSC) was generated using a generative semi-supervised modelling approach. The template was incorporated into the pre-processing pipeline of voxel-based morphometry and voxel-based quantification analyses in SPM. This approach was validated on T1-weighted scans and multiparameter maps, by assessing trauma-induced changes in SCI patients relative to controls and comparing the findings with the outcome from existing analytical tools. Consistency of the MRI measures was assessed using intraclass correlation coefficients (ICC). The SPM approach using the BSC template revealed trauma-induced changes across the sensorimotor system in the cord and brain in SCI patients. These changes were confirmed with established approaches covering brain or cord, separately. The ICC in the brain was high within regions of interest, such as the sensorimotor cortices, corticospinal tracts and thalamus. The simultaneous voxel-wise analysis of brain and cervical spinal cord was performed in a unique SPM-based framework incorporating pre-processing and statistical analysis in the same environment. Validation based on a SCI cohort demonstrated that the new processing approach based on the brain and cord is comparable to available processing tools, while offering the advantage of performing the analysis simultaneously across the neuraxis.
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Affiliation(s)
- Michela Azzarito
- Spinal Cord Injury Center Balgrist, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Sreenath P Kyathanahally
- Spinal Cord Injury Center Balgrist, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Yaël Balbastre
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Maryam Seif
- Spinal Cord Injury Center Balgrist, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Claudia Blaiotta
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Martina F Callaghan
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - John Ashburner
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Patrick Freund
- Spinal Cord Injury Center Balgrist, University Hospital Zurich, University of Zurich, Zurich, Switzerland.,Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, UK.,Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, University College London, London, UK
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13
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Spinal cord atrophy in a primary progressive multiple sclerosis trial: Improved sample size using GBSI. NEUROIMAGE-CLINICAL 2020; 28:102418. [PMID: 32961403 PMCID: PMC7509079 DOI: 10.1016/j.nicl.2020.102418] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/22/2020] [Revised: 08/29/2020] [Accepted: 09/03/2020] [Indexed: 01/18/2023]
Abstract
The GBSI provided clinically meaningful measurements of spinal cord atrophy, with low sample size. Deriving spinal cord atrophy from brain MRI using the GBSI is easier than spinal cord MRI. Spinal cord atrophy on GBSI could be used as a secondary outcome measure.
Background We aimed to evaluate the implications for clinical trial design of the generalised boundary-shift integral (GBSI) for spinal cord atrophy measurement. Methods We included 220 primary-progressive multiple sclerosis patients from a phase 2 clinical trial, with baseline and week-48 3DT1-weighted MRI of the brain and spinal cord (1 × 1 × 1 mm3), acquired separately. We obtained segmentation-based cross-sectional spinal cord area (CSA) at C1-2 (from both brain and spinal cord MRI) and C2-5 levels (from spinal cord MRI) using DeepSeg, and, then, we computed corresponding GBSI. Results Depending on the spinal cord segment, we included 67.4–98.1% patients for CSA measurements, and 66.9–84.2% for GBSI. Spinal cord atrophy measurements obtained with GBSI had lower measurement variability, than corresponding CSA. Looking at the image noise floor, the lowest median standard deviation of the MRI signal within the cerebrospinal fluid surrounding the spinal cord was found on brain MRI at the C1-2 level. Spinal cord atrophy derived from brain MRI was related to the corresponding measures from dedicated spinal cord MRI, more strongly for GBSI than CSA. Spinal cord atrophy measurements using GBSI, but not CSA, were associated with upper and lower limb motor progression. Discussion Notwithstanding the reduced measurement variability, the clinical correlates, and the possibility of using brain acquisitions, spinal cord atrophy using GBSI should remain a secondary outcome measure in MS studies, until further advancements increase the quality of acquisition and reliability of processing.
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14
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Zhou J, Damasceno PF, Chachad R, Cheung JR, Ballatori A, Lotz JC, Lazar AA, Link TM, Fields AJ, Krug R. Automatic Vertebral Body Segmentation Based on Deep Learning of Dixon Images for Bone Marrow Fat Fraction Quantification. Front Endocrinol (Lausanne) 2020; 11:612. [PMID: 32982989 PMCID: PMC7492292 DOI: 10.3389/fendo.2020.00612] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Accepted: 07/27/2020] [Indexed: 12/16/2022] Open
Abstract
Background: Bone marrow fat (BMF) fraction quantification in vertebral bodies is used as a novel imaging biomarker to assess and characterize chronic lower back pain. However, manual segmentation of vertebral bodies is time consuming and laborious. Purpose: (1) Develop a deep learning pipeline for segmentation of vertebral bodies using quantitative water-fat MRI. (2) Compare BMF measurements between manual and automatic segmentation methods to assess performance. Materials and Methods: In this retrospective study, MR images using a 3D spoiled gradient-recalled echo (SPGR) sequence with Iterative Decomposition of water and fat with Echo Asymmetry and Least-squares estimation (IDEAL) reconstruction algorithm were obtained in 57 subjects (28 women, 29 men, mean age, 47.2 ± 12.6 years). An artificial network was trained for 100 epochs on a total of 165 lumbar vertebrae manually segmented from 31 subjects. Performance was assessed by analyzing the receiver operating characteristic curve, precision-recall, F1 scores, specificity, sensitivity, and similarity metrics. Bland-Altman analysis was used to assess performance of BMF fraction quantification using the predicted segmentations. Results: The deep learning segmentation method achieved an AUC of 0.92 (CI 95%: 0.9186, 0.9195) on a testing dataset (n = 24 subjects) on classification of pixels as vertebrae. A sensitivity of 0.99 and specificity of 0.80 were achieved for a testing dataset, and a mean Dice similarity coefficient of 0.849 ± 0.091. Comparing manual and automatic segmentations on fat fraction maps of lumbar vertebrae (n = 124 vertebral bodies) using Bland-Altman analysis resulted in a bias of only -0.605% (CI 95% = -0.847 to -0.363%) and agreement limits of -3.275% and +2.065%. Automatic segmentation was also feasible in 16 ± 1 s. Conclusion: Our results have demonstrated the feasibility of automated segmentation of vertebral bodies using deep learning models on water-fat MR (Dixon) images to define vertebral regions of interest with high specificity. These regions of interest can then be used to quantify BMF with comparable results as manual segmentation, providing a framework for completely automated investigation of vertebral changes in CLBP.
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Affiliation(s)
- Jiamin Zhou
- Department of Radiology and Biomedical Imaging, School of Medicine, University of California, San Francisco, San Francisco, CA, United States
| | - Pablo F. Damasceno
- Department of Radiology and Biomedical Imaging, School of Medicine, University of California, San Francisco, San Francisco, CA, United States
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, United States
- Center for Intelligent Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Ravi Chachad
- Department of Radiology and Biomedical Imaging, School of Medicine, University of California, San Francisco, San Francisco, CA, United States
| | - Justin R. Cheung
- Department of Radiology and Biomedical Imaging, School of Medicine, University of California, San Francisco, San Francisco, CA, United States
| | - Alexander Ballatori
- Department of Orthopaedic Surgery, School of Medicine, University of California, San Francisco, San Francisco, CA, United States
| | - Jeffrey C. Lotz
- Department of Orthopaedic Surgery, School of Medicine, University of California, San Francisco, San Francisco, CA, United States
| | - Ann A. Lazar
- Department of Preventive and Restorative Dental Sciences, School of Dentistry, University of California, San Francisco, San Francisco, CA, United States
- Department of Epidemiology and Biostatistics, School of Medicine, University of California, San Francisco, San Francisco, CA, United States
| | - Thomas M. Link
- Department of Radiology and Biomedical Imaging, School of Medicine, University of California, San Francisco, San Francisco, CA, United States
| | - Aaron J. Fields
- Department of Orthopaedic Surgery, School of Medicine, University of California, San Francisco, San Francisco, CA, United States
| | - Roland Krug
- Department of Radiology and Biomedical Imaging, School of Medicine, University of California, San Francisco, San Francisco, CA, United States
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15
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Inoue T, Ichikawa D, Ueno T, Cheong M, Inoue T, Whetstone WD, Endo T, Nizuma K, Tominaga T. XGBoost, a Machine Learning Method, Predicts Neurological Recovery in Patients with Cervical Spinal Cord Injury. Neurotrauma Rep 2020; 1:8-16. [PMID: 34223526 PMCID: PMC8240917 DOI: 10.1089/neur.2020.0009] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
The accurate prediction of neurological outcomes in patients with cervical spinal cord injury (SCI) is difficult because of heterogeneity in patient characteristics, treatment strategies, and radiographic findings. Although machine learning algorithms may increase the accuracy of outcome predictions in various fields, limited information is available on their efficacy in the management of SCI. We analyzed data from 165 patients with cervical SCI, and extracted important factors for predicting prognoses. Extreme gradient boosting (XGBoost) as a machine learning model was applied to assess the reliability of a machine learning algorithm to predict neurological outcomes compared with that of conventional methodology, such as a logistic regression or decision tree. We used regularly obtainable data as predictors, such as demographics, magnetic resonance variables, and treatment strategies. Predictive tools, including XGBoost, a logistic regression, and a decision tree, were applied to predict neurological improvements in the functional motor status (ASIA [American Spinal Injury Association] Impairment Scale [AIS] D and E) 6 months after injury. We evaluated predictive performance, including accuracy and the area under the receiver operating characteristic curve (AUC). Regarding predictions of neurological improvements in patients with cervical SCI, XGBoost had the highest accuracy (81.1%), followed by the logistic regression (80.6%) and the decision tree (78.8%). Regarding AUC, the logistic regression showed 0.877, followed by XGBoost (0.867) and the decision tree (0.753). XGBoost reliably predicted neurological alterations in patients with cervical SCI. The utilization of predictive machine learning algorithms may enhance personalized management choices through pre-treatment categorization of patients.
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Affiliation(s)
- Tomoo Inoue
- Department of Neurosurgery, National Health Organization Sendai Medical Center, Sendai, Miyagi, Japan
| | | | | | - Maxwell Cheong
- Department of Radiology, Stanford University School of Medicine, Palo Alto, California, USA
| | - Takashi Inoue
- Department of Neurosurgery, National Health Organization Sendai Medical Center, Sendai, Miyagi, Japan
| | - William D. Whetstone
- Department of Emergency Medicine, University of California, San Francisco, San Francisco, California, USA
| | - Toshiki Endo
- Department of Neurosurgery, National Health Organization Sendai Medical Center, Sendai, Miyagi, Japan
- Department of Neurosurgery, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan
| | - Kuniyasu Nizuma
- Department of Neurosurgery, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan
- Department of Neurosurgical Engineering and Translational Neuroscience, Graduate School of Biomedical Engineering, Tohoku University, Sendai, Miyagi, Japan
- Department of Neurosurgical Engineering and Translational Neuroscience, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan
| | - Teiji Tominaga
- Department of Neurosurgery, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan
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16
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17
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Khan O, Badhiwala JH, Wilson JRF, Jiang F, Martin AR, Fehlings MG. Predictive Modeling of Outcomes After Traumatic and Nontraumatic Spinal Cord Injury Using Machine Learning: Review of Current Progress and Future Directions. Neurospine 2019; 16:678-685. [PMID: 31905456 PMCID: PMC6945005 DOI: 10.14245/ns.1938390.195] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Accepted: 12/15/2019] [Indexed: 12/17/2022] Open
Abstract
Machine learning represents a promising frontier in epidemiological research on spine surgery. It consists of a series of algorithms that determines relationships between data. Machine learning maintains numerous advantages over conventional regression techniques, such as a reduced requirement for a priori knowledge on predictors and better ability to manage large datasets. Current studies have made extensive strides in employing machine learning to a greater capacity in spinal cord injury (SCI). Analyses using machine learning algorithms have been done on both traumatic SCI and nontraumatic SCI, the latter of which typically represents degenerative spine disease resulting in spinal cord compression, such as degenerative cervical myelopathy. This article is a literature review of current studies published in traumatic and nontraumatic SCI that employ machine learning for the prediction of a host of outcomes. The studies described utilize machine learning in a variety of capacities, including imaging analysis and prediction in large epidemiological data sets. We discuss the performance of these machine learning-based clinical prognostic models relative to conventional statistical prediction models. Finally, we detail the future steps needed for machine learning to become a more common modality for statistical analysis in SCI.
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Affiliation(s)
- Omar Khan
- Department of Surgery, University of Toronto, Toronto, ON, Canada
| | - Jetan H Badhiwala
- Department of Surgery, University of Toronto, Toronto, ON, Canada.,Spinal Program, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
| | - Jamie R F Wilson
- Department of Surgery, University of Toronto, Toronto, ON, Canada.,Spinal Program, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
| | - Fan Jiang
- Department of Surgery, University of Toronto, Toronto, ON, Canada.,Spinal Program, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
| | - Allan R Martin
- Spinal Program, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
| | - Michael G Fehlings
- Department of Surgery, University of Toronto, Toronto, ON, Canada.,Spinal Program, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
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