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Valošek J, Cohen-Adad J. Reproducible Spinal Cord Quantitative MRI Analysis with the Spinal Cord Toolbox. Magn Reson Med Sci 2024; 23:307-315. [PMID: 38479843 PMCID: PMC11234946 DOI: 10.2463/mrms.rev.2023-0159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/02/2024] Open
Abstract
The spinal cord plays a pivotal role in the central nervous system, providing communication between the brain and the body and containing critical motor and sensory networks. Recent advancements in spinal cord MRI data acquisition and image analysis have shown a potential to improve the diagnostics, prognosis, and management of a variety of pathological conditions. In this review, we first discuss the significance of standardized spinal cord MRI acquisition protocol in multi-center and multi-manufacturer studies. Then, we cover open-access spinal cord MRI datasets, which are important for reproducible science and validation of new methods. Finally, we elaborate on the recent advances in spinal cord MRI data analysis techniques implemented in the open-source software package Spinal Cord Toolbox (SCT).
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Affiliation(s)
- Jan Valošek
- 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
| | - 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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2
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Chaves H, Serra MM, Shalom DE, Ananía P, Rueda F, Osa Sanz E, Stefanoff NI, Rodríguez Murúa S, Costa ME, Kitamura FC, Yañez P, Cejas C, Correale J, Ferrante E, Fernández Slezak D, Farez MF. Assessing robustness and generalization of a deep neural network for brain MS lesion segmentation on real-world data. Eur Radiol 2024; 34:2024-2035. [PMID: 37650967 DOI: 10.1007/s00330-023-10093-5] [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: 02/03/2023] [Revised: 07/01/2023] [Accepted: 07/12/2023] [Indexed: 09/01/2023]
Abstract
OBJECTIVES Evaluate the performance of a deep learning (DL)-based model for multiple sclerosis (MS) lesion segmentation and compare it to other DL and non-DL algorithms. METHODS This ambispective, multicenter study assessed the performance of a DL-based model for MS lesion segmentation and compared it to alternative DL- and non-DL-based methods. Models were tested on internal (n = 20) and external (n = 18) datasets from Latin America, and on an external dataset from Europe (n = 49). We also examined robustness by rescanning six patients (n = 6) from our MS clinical cohort. Moreover, we studied inter-human annotator agreement and discussed our findings in light of these results. Performance and robustness were assessed using intraclass correlation coefficient (ICC), Dice coefficient (DC), and coefficient of variation (CV). RESULTS Inter-human ICC ranged from 0.89 to 0.95, while spatial agreement among annotators showed a median DC of 0.63. Using expert manual segmentations as ground truth, our DL model achieved a median DC of 0.73 on the internal, 0.66 on the external, and 0.70 on the challenge datasets. The performance of our DL model exceeded that of the alternative algorithms on all datasets. In the robustness experiment, our DL model also achieved higher DC (ranging from 0.82 to 0.90) and lower CV (ranging from 0.7 to 7.9%) when compared to the alternative methods. CONCLUSION Our DL-based model outperformed alternative methods for brain MS lesion segmentation. The model also proved to generalize well on unseen data and has a robust performance and low processing times both on real-world and challenge-based data. CLINICAL RELEVANCE STATEMENT Our DL-based model demonstrated superior performance in accurately segmenting brain MS lesions compared to alternative methods, indicating its potential for clinical application with improved accuracy, robustness, and efficiency. KEY POINTS • Automated lesion load quantification in MS patients is valuable; however, more accurate methods are still necessary. • A novel deep learning model outperformed alternative MS lesion segmentation methods on multisite datasets. • Deep learning models are particularly suitable for MS lesion segmentation in clinical scenarios.
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Affiliation(s)
- Hernán Chaves
- Diagnostic Imaging Department, Fleni, Montañeses, 2325 (C1428AQK), Ciudad de Buenos Aires, Argentina.
| | - María M Serra
- Diagnostic Imaging Department, Fleni, Montañeses, 2325 (C1428AQK), Ciudad de Buenos Aires, Argentina
| | - Diego E Shalom
- Department of Physics, University of Buenos Aires (UBA), Buenos Aires, Argentina
- Physics Institute of Buenos Aires (IFIBA) CONICET, Buenos Aires, Argentina
- Laboratorio de Neurociencia, Universidad Torcuato Di Tella, Buenos Aires, Argentina
| | | | - Fernanda Rueda
- Radiology Department, Diagnósticos da América SA (Dasa), Rio de Janeiro, Brazil
| | - Emilia Osa Sanz
- Diagnostic Imaging Department, Fleni, Montañeses, 2325 (C1428AQK), Ciudad de Buenos Aires, Argentina
| | - Nadia I Stefanoff
- Diagnostic Imaging Department, Fleni, Montañeses, 2325 (C1428AQK), Ciudad de Buenos Aires, Argentina
| | - Sofía Rodríguez Murúa
- Center for Research On Neuroimmunological Diseases (CIEN), Fleni, Buenos Aires, Argentina
| | | | - Felipe C Kitamura
- DasaInova, Diagnósticos da América SA (Dasa), São Paulo, São Paulo, Brazil
| | - Paulina Yañez
- Diagnostic Imaging Department, Fleni, Montañeses, 2325 (C1428AQK), Ciudad de Buenos Aires, Argentina
| | - Claudia Cejas
- Diagnostic Imaging Department, Fleni, Montañeses, 2325 (C1428AQK), Ciudad de Buenos Aires, Argentina
| | | | - Enzo Ferrante
- Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional, sinc(i) CONICET-UNL, Santa Fe, Argentina
| | - Diego Fernández Slezak
- Center for Research On Neuroimmunological Diseases (CIEN), Fleni, Buenos Aires, Argentina
- Departamento de Computación, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires (UBA), Buenos Aires, Argentina
- Instituto de Investigación en Ciencias de la Computación (ICC), CONICET-UBA, Buenos Aires, Argentina
| | - Mauricio F Farez
- Radiology Department, Diagnósticos da América SA (Dasa), Rio de Janeiro, Brazil
- Center for Research On Neuroimmunological Diseases (CIEN), Fleni, Buenos Aires, Argentina
- Center for Biostatistics, Epidemiology and Public Health (CEBES), Fleni, Buenos Aires, Argentina
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Storelli L, Pagani E, Pantano P, Piervincenzi C, Tedeschi G, Gallo A, De Stefano N, Battaglini M, Rocca MA, Filippi M. Methods for Brain Atrophy MR Quantification in Multiple Sclerosis: Application to the Multicenter INNI Dataset. J Magn Reson Imaging 2023; 58:1221-1231. [PMID: 36661195 DOI: 10.1002/jmri.28616] [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: 11/21/2022] [Revised: 01/11/2023] [Accepted: 01/12/2023] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND Current therapeutic strategies in multiple sclerosis (MS) target neurodegeneration. However, the integration of atrophy measures into the clinical scenario is still an unmet need. PURPOSE To compare methods for whole-brain and gray matter (GM) atrophy measurements using the Italian Neuroimaging Network Initiative (INNI) dataset. STUDY TYPE Retrospective (data available from INNI). POPULATION A total of 466 patients with relapsing-remitting MS (mean age = 37.3 ± 10 years, 323 women) and 279 healthy controls (HC; mean age = 38.2 ± 13 years, 164 women). FIELD STRENGTH/SEQUENCE A 3.0-T, T1-weighted (spin echo and gradient echo without gadolinium injection) and T2-weighted spin echo scans at baseline and after 1 year (170 MS, 48 HC). ASSESSMENT Structural Image Evaluation using Normalization of Atrophy (SIENA-X/XL; version 5.0.9), Statistical Parametric Mapping (SPM-v12); and Jim-v8 (Xinapse Systems, Colchester, UK) software were applied to all subjects. STATISTICAL TESTS In MS and HC, we evaluated the intraclass correlation coefficient (ICC) among FSL-SIENA(XL), SPM-v12, and Jim-v8 for cross-sectional whole-brain and GM tissue volumes and their longitudinal changes, the effect size according to the Cohen's d at baseline and the sample size requirement for whole-brain and GM atrophy progression at different power levels (lowest = 0.7, 0.05 alpha level). False discovery rate (Benjamini-Hochberg procedure) correction was applied. A P value <0.05 was considered statistically significant. RESULTS SPM-v12 and Jim-v8 showed significant agreement for cross-sectional whole-brain (ICC = 0.93 for HC and ICC = 0.84 for MS) and GM volumes (ICC = 0.66 for HC and ICC = 0.90) and longitudinal assessment of GM atrophy (ICC = 0.35 for HC and ICC = 0.59 for MS), while no significant agreement was found in the comparisons between whole-brain and GM volumes for SIENA-X/XL and both SPM-v12 (P = 0.19 and P = 0.29, respectively) and Jim-v8 (P = 0.21 and P = 0.32, respectively). SPM-v12 and Jim-v8 showed the highest effect size for cross-sectional GM atrophy (Cohen's d = -0.63 and -0.61). Jim-v8 and SIENA(XL) showed the smallest sample size requirements for whole-brain (58) and GM atrophy (152), at 0.7 power level. DATA CONCLUSION The findings obtained in this study should be considered when selecting the appropriate brain atrophy pipeline for MS studies. EVIDENCE LEVEL 4. TECHNICAL EFFICACY Stage 1.
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Affiliation(s)
- Loredana Storelli
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Elisabetta Pagani
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Patrizia Pantano
- Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
- IRCCS NEUROMED, Pozzilli, Italy
| | | | - Gioacchino Tedeschi
- Department of Advanced Medical and Surgical Sciences, and 3T MRI-Center, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Antonio Gallo
- Department of Advanced Medical and Surgical Sciences, and 3T MRI-Center, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Nicola De Stefano
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Marco Battaglini
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Maria A Rocca
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
- Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy
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Hu F, Chen AA, Horng H, Bashyam V, Davatzikos C, Alexander-Bloch A, Li M, Shou H, Satterthwaite TD, Yu M, Shinohara RT. Image harmonization: A review of statistical and deep learning methods for removing batch effects and evaluation metrics for effective harmonization. Neuroimage 2023; 274:120125. [PMID: 37084926 PMCID: PMC10257347 DOI: 10.1016/j.neuroimage.2023.120125] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 04/12/2023] [Accepted: 04/19/2023] [Indexed: 04/23/2023] Open
Abstract
Magnetic resonance imaging and computed tomography from multiple batches (e.g. sites, scanners, datasets, etc.) are increasingly used alongside complex downstream analyses to obtain new insights into the human brain. However, significant confounding due to batch-related technical variation, called batch effects, is present in this data; direct application of downstream analyses to the data may lead to biased results. Image harmonization methods seek to remove these batch effects and enable increased generalizability and reproducibility of downstream results. In this review, we describe and categorize current approaches in statistical and deep learning harmonization methods. We also describe current evaluation metrics used to assess harmonization methods and provide a standardized framework to evaluate newly-proposed methods for effective harmonization and preservation of biological information. Finally, we provide recommendations to end-users to advocate for more effective use of current methods and to methodologists to direct future efforts and accelerate development of the field.
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Affiliation(s)
- Fengling Hu
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States.
| | - Andrew A Chen
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States
| | - Hannah Horng
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States
| | - Vishnu Bashyam
- Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, United States
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, United States
| | - Aaron Alexander-Bloch
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, United States; Penn-CHOP Lifespan Brain Institute, United States; Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, United States
| | - Mingyao Li
- Statistical Center for Single-Cell and Spatial Genomics, Perelman School of Medicine, University of Pennsylvania, United States
| | - Haochang Shou
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States; Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, United States
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, United States; Penn-CHOP Lifespan Brain Institute, United States; The Penn Lifespan Informatics and Neuroimaging Center, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, United States
| | - Meichen Yu
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, United States
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States; Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, United States
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Sun J, Zhao W, Xie Y, Zhou F, Wu L, Li Y, Li H, Li Y, Zeng C, Han X, Liu Y, Zhang N. Personalized estimates of morphometric similarity in multiple sclerosis and neuromyelitis optica spectrum disorders. Neuroimage Clin 2023; 39:103454. [PMID: 37343344 PMCID: PMC10509529 DOI: 10.1016/j.nicl.2023.103454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 05/21/2023] [Accepted: 06/16/2023] [Indexed: 06/23/2023]
Abstract
Brain morphometric alterations involve multiple brain regions on progression of the disease in multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD) and exhibit age-related degenerative changes during the pathological aging. Recent advance in brain morphometry as measured using MRI have leveraged Person-Based Similarity Index (PBSI) approach to assess the extent of within-diagnosis similarity or heterogeneity of brain neuroanatomical profiles between individuals of healthy populations and validate in neuropsychiatric disorders. Brain morphometric changes throughout the lifespan would be invaluable for understanding regional variability of age-related structural degeneration and the substrate of inflammatory demyelinating disease. Here, we aimed to quantify the neuroanatomical profiles with PBSI measures of cortical thickness (CT) and subcortical volumes (SV) in 263 MS, 207 NMOSD, and 338 healthy controls (HC) from six separate central datasets (aged 11-80). We explored the between-group comparisons of PBSI measures, as well as the advancing age and sex effects on PBSI measures. Compared to NMOSD, MS showed a lower extent of within-diagnosis similarity. Significant differences in regional contributions to PBSI score were observed in 29 brain regions between MS and NMOSD (P < 0.05/164, Bonferroni corrected), of which bilateral cerebellum in MS and bilateral parahippocampal gyrus in NMOSD represented the highest divergence between the two patient groups, with a high similarity effect within each group. The PBSI scores were generally lower with advancing age, but their associations showed different patterns depending on the age range. For MS, CT profiles were significantly negatively correlated with age until the early 30 s (ρ = -0.265, P = 0.030), while for NMOSD, SV profiles were significantly negatively correlated with age with 51 year-old and older (ρ = -0.365, P = 0.008). The current study suggests that PBSI approach could be used to quantify the variation in brain morphometric changes in CNS inflammatory demyelinating disease, and exhibited a greater neuroanatomical heterogeneity pattern in MS compared with NMOSD. Our results reveal that, as an MR marker, PBSI may be sensitive to distribute the disease-associated grey matter diversity and complexity. Disease-driven production of regionally selective and age stage-dependency changes in the neuroanatomical profile of MS and NMOSD should be considered to facilitate the prediction of clinical outcomes and assessment of treatment responses.
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Affiliation(s)
- Jie Sun
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Wenjin Zhao
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Yingying Xie
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Fuqing Zhou
- Department of Radiology, The First Afliated Hospital, Nanchang University, Nanchang 330006, Jiangxi Province, China
- Neuroimaging Lab, Jiangxi Province Medical Imaging Research Institute, Nanchang 330006, Jiangxi Province, China
| | - Lin Wu
- Department of Radiology, The First Afliated Hospital, Nanchang University, Nanchang 330006, Jiangxi Province, China
- Neuroimaging Lab, Jiangxi Province Medical Imaging Research Institute, Nanchang 330006, Jiangxi Province, China
| | - Yuxin Li
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Haiqing Li
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Yongmei Li
- Department of Radiology, The First Afliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Chun Zeng
- Department of Radiology, The First Afliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Xuemei Han
- Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun 130031, Jilin Province, China
| | - Yaou Liu
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, No.119, The West Southern 4th Ring Road, Fengtai District, Beijing 100070, China
| | - Ningnannan Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
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Reijonen M, Holopainen E, Arponen O, Könönen M, Vanninen R, Anttila M, Sallinen H, Rinta-Kiikka I, Lindgren A. Neoadjuvant chemotherapy induces an elevation of tumour apparent diffusion coefficient values in patients with ovarian cancer. BMC Cancer 2023; 23:299. [PMID: 37005578 PMCID: PMC10068179 DOI: 10.1186/s12885-023-10760-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 03/21/2023] [Indexed: 04/04/2023] Open
Abstract
OBJECTIVES Multiparametric magnetic resonance imaging (mMRI) is the modality of choice in the imaging of ovarian cancer (OC). We aimed to investigate the feasibility of different types of regions of interest (ROIs) in the measurement of apparent diffusion coefficient (ADC) values of diffusion-weighted imaging in OC patients treated with neoadjuvant chemotherapy (NACT). METHODS We retrospectively enrolled 23 consecutive patients with advanced OC who had undergone NACT and mMRI. Seventeen of them had been imaged before and after NACT. Two observers independently measured the ADC values in both ovaries and in the metastatic mass by drawing on a single slice of (1) freehand large ROIs (L-ROIs) covering the solid parts of the whole tumour and (2) three small round ROIs (S-ROIs). The side of the primary ovarian tumour was defined. We evaluated the interobserver reproducibility and statistical significance of the change in tumoural pre- and post-NACT ADC values. Each patient's disease was defined as platinum-sensitive, semi-sensitive, or resistant. The patients were deemed either responders or non-responders. RESULTS The interobserver reproducibility of the L-ROI and S-ROI measurements ranged from good to excellent (ICC range: 0.71-0.99). The mean ADC values were significantly higher after NACT in the primary tumour (L-ROI p < 0.001, S-ROIs p < 0.01), and the increase after NACT was associated with sensitivity to platinum-based chemotherapy. The changes in the ADC values of the omental mass were associated with a response to NACT. CONCLUSION The mean ADC values of the primary tumour increased significantly after NACT in the OC patients, and the amount of increase in omental mass was associated with the response to platinum-based NACT. Our study indicates that quantitative analysis of ADC values with a single slice and a whole tumour ROI placement is a reproducible method that has a potential role in the evaluation of NACT response in patients with OC. TRIAL REGISTRATION Retrospectively registered (institutional permission code: 5302501; date of the permission: 31.7.2020).
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Affiliation(s)
- Milja Reijonen
- Department of Radiology, Tampere University Hospital, Tampere, Finland.
- Faculty of Medicine and Health Technology, University of Tampere, Tampere, Finland.
| | - Erikka Holopainen
- Department of Radiology, Kuopio University Hospital, Kuopio, Finland
- Institute of Clinical Medicine, School of Medicine, Clinical Radiology, University of Eastern Finland, Kuopio, Finland
| | - Otso Arponen
- Department of Radiology, Tampere University Hospital, Tampere, Finland
- Faculty of Medicine and Health Technology, University of Tampere, Tampere, Finland
| | - Mervi Könönen
- Department of Radiology, Kuopio University Hospital, Kuopio, Finland
- Department of Clinical Neurophysiology, Kuopio University Hospital, Kuopio, Finland
| | - Ritva Vanninen
- Department of Radiology, Kuopio University Hospital, Kuopio, Finland
- Institute of Clinical Medicine, School of Medicine, Clinical Radiology, University of Eastern Finland, Kuopio, Finland
| | - Maarit Anttila
- Department of Gynaecology and Obstetrics, Kuopio University Hospital, Kuopio, Finland
- Institute of Clinical Medicine, School of Medicine, Obstetrics and Gynaecology, University of Eastern Finland, Kuopio, Finland
| | - Hanna Sallinen
- Department of Gynaecology and Obstetrics, Kuopio University Hospital, Kuopio, Finland
| | - Irina Rinta-Kiikka
- Department of Radiology, Tampere University Hospital, Tampere, Finland
- Faculty of Medicine and Health Technology, University of Tampere, Tampere, Finland
| | - Auni Lindgren
- Department of Gynaecology and Obstetrics, Kuopio University Hospital, Kuopio, Finland
- Institute of Clinical Medicine, School of Medicine, Obstetrics and Gynaecology, University of Eastern Finland, Kuopio, Finland
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7
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De Rosa AP, Esposito F, Valsasina P, d'Ambrosio A, Bisecco A, Rocca MA, Tommasin S, Marzi C, De Stefano N, Battaglini M, Pantano P, Cirillo M, Tedeschi G, Filippi M, Gallo A. Resting-state functional MRI in multicenter studies on multiple sclerosis: a report on raw data quality and functional connectivity features from the Italian Neuroimaging Network Initiative. J Neurol 2023; 270:1047-1066. [PMID: 36350401 PMCID: PMC9886598 DOI: 10.1007/s00415-022-11479-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 11/03/2022] [Accepted: 11/04/2022] [Indexed: 11/11/2022]
Abstract
The Italian Neuroimaging Network Initiative (INNI) is an expanding repository of brain MRI data from multiple sclerosis (MS) patients recruited at four Italian MRI research sites. We describe the raw data quality of resting-state functional MRI (RS-fMRI) time-series in INNI and the inter-site variability in functional connectivity (FC) features after unified automated data preprocessing. MRI datasets from 489 MS patients and 246 healthy control (HC) subjects were retrieved from the INNI database. Raw data quality metrics included temporal signal-to-noise ratio (tSNR), spatial smoothness (FWHM), framewise displacement (FD), and differential variation in signals (DVARS). Automated preprocessing integrated white-matter lesion segmentation (SAMSEG) into a standard fMRI pipeline (fMRIPrep). FC features were calculated on pre-processed data and harmonized between sites (Combat) prior to assessing general MS-related alterations. Across centers (both groups), median tSNR and FWHM ranged from 47 to 84 and from 2.0 to 2.5, and median FD and DVARS ranged from 0.08 to 0.24 and from 1.06 to 1.22. After preprocessing, only global FC-related features were significantly correlated with FD or DVARS. Across large-scale networks, age/sex/FD-adjusted and harmonized FC features exhibited both inter-site and site-specific inter-group effects. Significant general reductions were obtained for somatomotor and limbic networks in MS patients (vs. HC). The implemented procedures provide technical information on raw data quality and outcome of fully automated preprocessing that might serve as reference in future RS-fMRI studies within INNI. The unified pipeline introduced little bias across sites and appears suitable for multisite FC analyses on harmonized network estimates.
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Affiliation(s)
- Alessandro Pasquale De Rosa
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Piazza Luigi Miraglia, 2, 80138, Naples, Italy
| | - Fabrizio Esposito
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Piazza Luigi Miraglia, 2, 80138, Naples, Italy.
| | - Paola Valsasina
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
| | - Alessandro d'Ambrosio
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Piazza Luigi Miraglia, 2, 80138, Naples, Italy
| | - Alvino Bisecco
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Piazza Luigi Miraglia, 2, 80138, Naples, Italy
| | - Maria A Rocca
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
- Vita-Salute San Raffaele University, Via Olgettina 58, 20132, Milan, Italy
| | - Silvia Tommasin
- Department of Human Neurosciences, Sapienza University of Rome, Viale Dell'Università, 30, 00185, Rome, Italy
| | - Chiara Marzi
- Institute of Applied Physics "Nello Cararra" (IFAC), National Research Council (CNR), Via Madonna del Piano, 10, Sesto Fiorentino, 50019, Florence, Italy
| | - Nicola De Stefano
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Marco Battaglini
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Patrizia Pantano
- Department of Human Neurosciences, Sapienza University of Rome, Viale Dell'Università, 30, 00185, Rome, Italy
| | - Mario Cirillo
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Piazza Luigi Miraglia, 2, 80138, Naples, Italy
| | - Gioacchino Tedeschi
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Piazza Luigi Miraglia, 2, 80138, Naples, Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
- Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
- Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
- Vita-Salute San Raffaele University, Via Olgettina 58, 20132, Milan, Italy
| | - Antonio Gallo
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Piazza Luigi Miraglia, 2, 80138, Naples, Italy
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Simonsen CS, Flemmen HØ, Broch L, Brekke K, Brunborg C, Berg-Hansen P, Celius EG. Rebaseline no evidence of disease activity (NEDA-3) as a predictor of long-term disease course in a Norwegian multiple sclerosis population. Front Neurol 2022; 13:1034056. [PMID: 36452173 PMCID: PMC9702815 DOI: 10.3389/fneur.2022.1034056] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 10/24/2022] [Indexed: 08/15/2023] Open
Abstract
INTRODUCTION No evidence of disease activity with three components (NEDA-3) is achieved if the person with MS (pwMS) has no new MRI lesions, no new relapses and no change in Expanded disability status scale (EDSS) over 1 year. Whether NEDA-3 is a good tool in measuring disease activity is up for discussion, but it is superior to the individual parameters separately and user-friendly. There is disagreement on whether NEDA-3 is a good predictor of long-term disability. METHODS This is a retrospective cohort study using real-world data with limited selection bias from the complete MS population at two hospitals in the southeast of Norway. We included pwMS diagnosed between 2006 and 2017 who had enough information to determine time to failure of NEDA-3 after diagnosis. RESULTS Of 536 pwMS, only 38% achieved NEDA 1 year after diagnosis. PwMS achieving NEDA were more likely to be started on a high efficacy drug as the initial drug, but there were no demographic differences. Mean time to NEDA failure was 3.3 (95% CI 2.9-3.7) years. Starting a high efficiacy therapy was associated with an increased risk of sustaining NEDA as compared to those receiving moderate efficacy therapy. PwMS who achieved NEDA at year one had a mean time to EDSS 6 of 33.8 (95% CI 30.9-36.8) years vs. 30.8 (95% CI 25.0-36.6) years in pwMS who did not achieve NEDA, p < 0.001. When rebaselining NEDA 1 year after diagnosis, 52.2% achieved NEDA in the 1st year after rebaseline, mean time to NEDA failure was 3.4 (95% CI 3.0-3.7) years and mean time to EDSS 6 was 44.5 (95% CI 40.4-48.5) years in pwMS achieving NEDA vs. 29.6 (95% CI 24.2-35.0) years in pwMS not achieving NEDA, p < 0.001. After rebaseline, pwMS with a high efficacy therapy as the initial drug had a mean time from diagnosis to NEDA fail of 4.8 years (95% CI 3.9-5.8) vs. 3.1 years (95% CI 2.7-3.5) in pwMS started on a moderate efficacy therapy, p < 0.001. In pwMS with NEDA failure at year one, 70% failed one, 28% failed two and 2% failed three components. New MRI lesions were the most common cause of NEDA failure (63%), followed by new relapses (50%) and EDSS change (25%). CONCLUSION NEDA-3 from rebaseline after 1 year, once treatment is stabilized, can predict the long-term disease course in MS. Starting a high efficacy DMT is associated with longer time to NEDA failure than moderate therapies. Finally, most pwMS only fail one component and new MRI lesions are the most likely cause of NEDA failure.
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Affiliation(s)
| | - Heidi Øyen Flemmen
- Department of Neurology, Hospital Telemark HF, Skien, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Line Broch
- Department of Neurology, Vestre Viken Hospital Trust, Drammen, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Kamilla Brekke
- Department of Neurology, Vestre Viken Hospital Trust, Drammen, Norway
- Department of Neurology, Hospital Vestfold, Tønsberg, Norway
| | - Cathrine Brunborg
- Oslo Centre for Biostatistics and Epidemiology, Research Support Services, Oslo University Hospital, Oslo, Norway
| | - Pål Berg-Hansen
- Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Elisabeth Gulowsen Celius
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Neurology, Oslo University Hospital, Oslo, Norway
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Combes AJ, Clarke MA, O'Grady KP, Schilling KG, Smith SA. Advanced spinal cord MRI in multiple sclerosis: Current techniques and future directions. Neuroimage Clin 2022; 36:103244. [PMID: 36306717 PMCID: PMC9668663 DOI: 10.1016/j.nicl.2022.103244] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 09/02/2022] [Accepted: 10/19/2022] [Indexed: 11/11/2022]
Abstract
Spinal cord magnetic resonance imaging (MRI) has a central role in multiple sclerosis (MS) clinical practice for diagnosis and disease monitoring. Advanced MRI sequences capable of visualizing and quantifying tissue macro- and microstructure and reflecting different pathological disease processes have been used in MS research; however, the spinal cord remains under-explored, partly due to technical obstacles inherent to imaging this structure. We propose that the study of the spinal cord merits equal ambition in overcoming technical challenges, and that there is much information to be exploited to make valuable contributions to our understanding of MS. We present a narrative review on the latest progress in advanced spinal cord MRI in MS, covering in the first part structural, functional, metabolic and vascular imaging methods. We focus on recent studies of MS and those making significant technical steps, noting the challenges that remain to be addressed and what stands to be gained from such advances. Throughout we also refer to other works that presend more in-depth review on specific themes. In the second part, we present several topics that, in our view, hold particular potential. The need for better imaging of gray matter is discussed. We stress the importance of developing imaging beyond the cervical spinal cord, and explore the use of ultra-high field MRI. Finally, some recommendations are given for future research, from study design to newer developments in analysis, and the need for harmonization of sequences and methods within the field. This review is aimed at researchers and clinicians with an interest in gaining an overview of the current state of advanced MRI research in this field and what is primed to be the future of spinal cord imaging in MS research.
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Affiliation(s)
- Anna J.E. Combes
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, 1161 21st Avenue South, Medical Center North, AA-1105, Nashville, TN 37232-2310, United States,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Medical Center North, 1161 21st Ave. South, Nashville, TN 37232, United States,Corresponding author at: 1161 21st Ave S, MCN AA1105, Nashville, TN 37232, USA.
| | - Margareta A. Clarke
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, 1161 21st Avenue South, Medical Center North, AA-1105, Nashville, TN 37232-2310, United States
| | - Kristin P. O'Grady
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, 1161 21st Avenue South, Medical Center North, AA-1105, Nashville, TN 37232-2310, United States,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Medical Center North, 1161 21st Ave. South, Nashville, TN 37232, United States,Department of Biomedical Engineering, Vanderbilt University, 2301 Vanderbilt Place, PMB 351826, Nashville, TN 37235-1826, United States
| | - Kurt G. Schilling
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, 1161 21st Avenue South, Medical Center North, AA-1105, Nashville, TN 37232-2310, United States,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Medical Center North, 1161 21st Ave. South, Nashville, TN 37232, United States
| | - Seth A. Smith
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, 1161 21st Avenue South, Medical Center North, AA-1105, Nashville, TN 37232-2310, United States,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Medical Center North, 1161 21st Ave. South, Nashville, TN 37232, United States,Department of Biomedical Engineering, Vanderbilt University, 2301 Vanderbilt Place, PMB 351826, Nashville, TN 37235-1826, United States
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Casas-Roma J, Martinez-Heras E, Solé-Ribalta A, Solana E, Lopez-Soley E, Vivó F, Diaz-Hurtado M, Alba-Arbalat S, Sepulveda M, Blanco Y, Saiz A, Borge-Holthoefer J, Llufriu S, Prados F. Applying multilayer analysis to morphological, structural, and functional brain networks to identify relevant dysfunction patterns. Netw Neurosci 2022; 6:916-933. [PMID: 36605412 PMCID: PMC9810367 DOI: 10.1162/netn_a_00258] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 06/07/2022] [Indexed: 01/09/2023] Open
Abstract
In recent years, research on network analysis applied to MRI data has advanced significantly. However, the majority of the studies are limited to single networks obtained from resting-state fMRI, diffusion MRI, or gray matter probability maps derived from T1 images. Although a limited number of previous studies have combined two of these networks, none have introduced a framework to combine morphological, structural, and functional brain connectivity networks. The aim of this study was to combine the morphological, structural, and functional information, thus defining a new multilayer network perspective. This has proved advantageous when jointly analyzing multiple types of relational data from the same objects simultaneously using graph- mining techniques. The main contribution of this research is the design, development, and validation of a framework that merges these three layers of information into one multilayer network that links and relates the integrity of white matter connections with gray matter probability maps and resting-state fMRI. To validate our framework, several metrics from graph theory are expanded and adapted to our specific domain characteristics. This proof of concept was applied to a cohort of people with multiple sclerosis, and results show that several brain regions with a synchronized connectivity deterioration could be identified.
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Affiliation(s)
- Jordi Casas-Roma
- e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain,* Corresponding Author:
| | - Eloy Martinez-Heras
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clínic de Barcelona, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain
| | | | - Elisabeth Solana
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clínic de Barcelona, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain
| | - Elisabet Lopez-Soley
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clínic de Barcelona, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain
| | - Francesc Vivó
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clínic de Barcelona, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain
| | | | - Salut Alba-Arbalat
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clínic de Barcelona, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain
| | - Maria Sepulveda
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clínic de Barcelona, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain
| | - Yolanda Blanco
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clínic de Barcelona, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain
| | - Albert Saiz
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clínic de Barcelona, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain
| | | | - Sara Llufriu
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clínic de Barcelona, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain
| | - Ferran Prados
- e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain,Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom,Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
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