1
|
English C, Simpson DB, Billinger SA, Churilov L, Coupland KG, Drummond A, Kuppuswamy A, Kutlubaev MA, Lerdal A, Mahmood A, Moseley GL, Pittman QJ, Riley EA, Sutherland BA, Wong CHY, Corbett D, Mead G. A roadmap for research in post-stroke fatigue: Consensus-based core recommendations from the third Stroke Recovery and Rehabilitation Roundtable. Int J Stroke 2024; 19:133-144. [PMID: 37424273 PMCID: PMC10811972 DOI: 10.1177/17474930231189135] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 06/27/2023] [Indexed: 07/11/2023]
Abstract
RATIONALE Fatigue affects almost half of all people living with stroke. Stroke survivors rank understanding fatigue and how to reduce it as one of the highest research priorities. METHODS We convened an interdisciplinary, international group of clinical and pre-clinical researchers and lived experience experts. We identified four priority areas: (1) best measurement tools for research, (2) clinical identification of fatigue and potentially modifiable causes, (3) promising interventions and recommendations for future trials, and (4) possible biological mechanisms of fatigue. Cross-cutting themes were aphasia and the voice of people with lived experience. Working parties were formed and structured consensus building processes were followed. RESULTS We present 20 recommendations covering outcome measures for research, development, and testing of new interventions and priority areas for future research on the biology of post-stroke fatigue. We developed and recommend the use of the Stroke Fatigue Clinical Assessment Tool. CONCLUSIONS By synthesizing current knowledge in post-stroke fatigue across clinical and pre-clinical fields, our work provides a roadmap for future research into post-stroke fatigue.
Collapse
Affiliation(s)
- Coralie English
- School of Health Sciences, College of Health, Medicine and Wellbeing, University of Newcastle, Callaghan, NSW, Australia
- Heart and Stroke Program, Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - Dawn B Simpson
- School of Health Sciences, College of Health, Medicine and Wellbeing, University of Newcastle, Callaghan, NSW, Australia
- Heart and Stroke Program, Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - Sandra A Billinger
- Department of Neurology, University of Kansas Medical Centre, University of Kansas Alzheimer’s Disease Research Centre, Kansas City, KS, USA
| | - Leonid Churilov
- Department of Medicine (RMH), University of Melbourne, Heidelberg, VIC, Australia
| | - Kirsten G Coupland
- Heart and Stroke Program, Hunter Medical Research Institute, Newcastle, NSW, Australia
- School of Biomedical Sciences and Pharmacy, College of Health, Medicine and Wellbeing, University of Newcastle, Callaghan, NSW, Australia
| | - Avril Drummond
- School of Health Sciences, University of Nottingham, Nottingham, UK
| | | | | | - Anners Lerdal
- Department of Interdisciplinary Health Sciences, Institute of Health and Society, Faculty of Medicine, University of Oslo, Oslo, Norway
- Research Department, Lovisenberg Diaconal Hospital, Oslo, Norway
| | - Amreen Mahmood
- Faculty of Health, Health and Education, Manchester Metropolitan University, Manchester, UK
| | - G Lorimer Moseley
- IIMPACT in Health, University of South Australia, Adelaide, SA, Australia
| | - Quentin J Pittman
- Department of Physiology and Pharmacology, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Ellyn A Riley
- Department of Communication Sciences and Disorders, Syracuse University, Syracuse, NY, USA
| | - Brad A Sutherland
- Tasmanian School of Medicine, College of Health and Medicine, University of Tasmania, Hobart, TAS, Australia
| | - Connie HY Wong
- Centre for Inflammatory Diseases, Department of Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
| | - Dale Corbett
- Department of Cellular and Molecular Medicine, University of Ottawa Roger Guindon Hall, Ottawa, ON, Canada
| | - Gillian Mead
- Ageing and Health, Usher Institute, University of Edinburgh, Edinburgh, UK
| |
Collapse
|
2
|
Bourached A, Bonkhoff AK, Schirmer MD, Regenhardt RW, Bretzner M, Hong S, Dalca AV, Giese AK, Winzeck S, Jern C, Lindgren AG, Maguire J, Wu O, Rhee J, Kimchi EY, Rost NS. Scaling behaviours of deep learning and linear algorithms for the prediction of stroke severity. Brain Commun 2024; 6:fcae007. [PMID: 38274570 PMCID: PMC10808016 DOI: 10.1093/braincomms/fcae007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 09/01/2023] [Accepted: 01/09/2024] [Indexed: 01/27/2024] Open
Abstract
Deep learning has allowed for remarkable progress in many medical scenarios. Deep learning prediction models often require 105-107 examples. It is currently unknown whether deep learning can also enhance predictions of symptoms post-stroke in real-world samples of stroke patients that are often several magnitudes smaller. Such stroke outcome predictions however could be particularly instrumental in guiding acute clinical and rehabilitation care decisions. We here compared the capacities of classically used linear and novel deep learning algorithms in their prediction of stroke severity. Our analyses relied on a total of 1430 patients assembled from the MRI-Genetics Interface Exploration collaboration and a Massachusetts General Hospital-based study. The outcome of interest was National Institutes of Health Stroke Scale-based stroke severity in the acute phase after ischaemic stroke onset, which we predict by means of MRI-derived lesion location. We automatically derived lesion segmentations from diffusion-weighted clinical MRI scans, performed spatial normalization and included a principal component analysis step, retaining 95% of the variance of the original data. We then repeatedly separated a train, validation and test set to investigate the effects of sample size; we subsampled the train set to 100, 300 and 900 and trained the algorithms to predict the stroke severity score for each sample size with regularized linear regression and an eight-layered neural network. We selected hyperparameters on the validation set. We evaluated model performance based on the explained variance (R2) in the test set. While linear regression performed significantly better for a sample size of 100 patients, deep learning started to significantly outperform linear regression when trained on 900 patients. Average prediction performance improved by ∼20% when increasing the sample size 9× [maximum for 100 patients: 0.279 ± 0.005 (R2, 95% confidence interval), 900 patients: 0.337 ± 0.006]. In summary, for sample sizes of 900 patients, deep learning showed a higher prediction performance than typically employed linear methods. These findings suggest the existence of non-linear relationships between lesion location and stroke severity that can be utilized for an improved prediction performance for larger sample sizes.
Collapse
Affiliation(s)
- Anthony Bourached
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
- UCL Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK
| | - Anna K Bonkhoff
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Markus D Schirmer
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Robert W Regenhardt
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Martin Bretzner
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
- University of Lille, Inserm, CHU Lille, U1171—LilNCog (JPARC)—Lille Neurosciences & Cognition, Lille F-59000, France
| | - Sungmin Hong
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Adrian V Dalca
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA 02129, USA
| | - Anne-Katrin Giese
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg 20251, Germany
| | - Stefan Winzeck
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Department of Computing, Imperial College London, London SW7 2RH, UK
| | - Christina Jern
- Institute of Biomedicine, Department of Laboratory Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg 41390, Sweden
- Department of Clinical Genetics and Genomics Gothenburg, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg 41345, Sweden
| | - Arne G Lindgren
- Department of Neurology, Skåne University Hospital, Lund 22185, Sweden
| | - Jane Maguire
- Department of Clinical Sciences Lund, Neurology, Lund University, Lund 22185, Sweden
- University of Technology Sydney, Ultimo, NSW 2007, Australia
| | - Ona Wu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA 02129, USA
| | - John Rhee
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02139, USA
| | - Eyal Y Kimchi
- Department of Neurology, Feinberg School of Medicine, Northwestern University, Evaston, IL 60201, USA
| | - Natalia S Rost
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| |
Collapse
|
3
|
Kuppuswamy A, Billinger S, Coupland KG, English C, Kutlubaev MA, Moseley L, Pittman QJ, Simpson DB, Sutherland BA, Wong C, Corbett D. Mechanisms of Post-Stroke Fatigue: A Follow-Up From the Third Stroke Recovery and Rehabilitation Roundtable. Neurorehabil Neural Repair 2024; 38:52-61. [PMID: 38156702 PMCID: PMC10798014 DOI: 10.1177/15459683231219266] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2024]
Abstract
BACKGROUND Post-stroke fatigue (PSF) is a significant and highly prevalent symptom, whose mechanisms are poorly understood. The third Stroke Recovery and Rehabilitation Roundtable paper on PSF focussed primarily on defining and measuring PSF while mechanisms were briefly discussed. This companion paper to the main paper is aimed at elaborating possible mechanisms of PSF. METHODS This paper reviews the available evidence that potentially explains the pathophysiology of PSF and draws parallels from fatigue literature in other conditions. We start by proposing a case for phenotyping PSF based on structural, functional, and behavioral characteristics of PSF. This is followed by discussion of a potentially significant role of early inflammation in the development of fatigue, specifically the impact of low-grade inflammation and its long-term systemic effects resulting in PSF. Of the many neurotransmitter systems in the brain, the dopaminergic systems have the most evidence for a role in PSF, along with a role in sensorimotor processing. Sensorimotor neural network dynamics are compromised as highlighted by evidence from both neurostimulation and neuromodulation studies. The double-edged sword effect of exercise on PSF provides further insight into how PSF might emerge and the importance of carefully titrating interventional paradigms. CONCLUSION The paper concludes by synthesizing the presented evidence into a unifying model of fatigue which distinguishes between factors that pre-dispose, precipitate, and perpetuate PSF. This framework will help guide new research into the biological mechanisms of PSF which is a necessary prerequisite for developing treatments to mitigate the debilitating effects of post-stroke fatigue.
Collapse
Affiliation(s)
- Annapoorna Kuppuswamy
- Queen Square Institute of Neurology, University College London, London, UK
- Department of Biomedical Sciences, University of Leeds, Leeds, UK
| | - Sandra Billinger
- Department of Neurology, University of Kansas Medical Center, University of Kansas Alzheimer’s Disease Research Center, Fairway, KS, MO, USA
| | - Kirsten G. Coupland
- School of Biomedical Sciences and Pharmacy, College of Health, Medicine and Wellbeing, University of Newcastle, Australia Heart and Stroke Program, Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - Coralie English
- School of Health Sciences, College of Health, Medicine and Wellbeing, University of Newcastle, Australia Heart and Stroke Program, Hunter Medical Research Institute, Newcastle, NSW, Australia
| | | | - Lorimer Moseley
- IIMPACT in Health, University of South Australia, Adelaide, SA, Australia
| | - Quentin J. Pittman
- Department of Physiology and Pharmacology, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Dawn B. Simpson
- School of Health Sciences, College of Health, Medicine and Wellbeing, University of Newcastle, Australia Heart and Stroke Program, Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - Brad A. Sutherland
- Tasmanian School of Medicine, College of Health and Medicine, University of Tasmania, Hobart, TS, Australia
| | - Connie Wong
- Centre for Inflammatory Diseases, Department of Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
| | - Dale Corbett
- Department of Cellular and Molecular Medicine, University of Ottawa Brain and Mind Institute, University of Ottawa, Ottawa, ON, Canada
| |
Collapse
|
4
|
English C, Simpson DB, Billinger SA, Churilov L, Coupland KG, Drummond A, Kuppuswamy A, Kutlubaev MA, Lerdal A, Mahmood A, Moseley GL, Pittman QJ, Riley EA, Sutherland BA, Wong CHY, Corbett D, Mead G. A roadmap for research in post-stroke fatigue: Consensus-based core recommendations from the third Stroke Recovery and Rehabilitation Roundtable. Neurorehabil Neural Repair 2024; 38:7-18. [PMID: 37837346 PMCID: PMC10798034 DOI: 10.1177/15459683231209170] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2023]
Abstract
RATIONALE Fatigue affects almost half of all people living with stroke. Stroke survivors rank understanding fatigue and how to reduce it as one of the highest research priorities. METHODS We convened an interdisciplinary, international group of clinical and pre-clinical researchers and lived experience experts. We identified four priority areas: (1) best measurement tools for research, (2) clinical identification of fatigue and potentially modifiable causes, (3) promising interventions and recommendations for future trials, and (4) possible biological mechanisms of fatigue. Cross-cutting themes were aphasia and the voice of people with lived experience. Working parties were formed and structured consensus building processes were followed. RESULTS We present 20 recommendations covering outcome measures for research, development, and testing of new interventions and priority areas for future research on the biology of post-stroke fatigue. We developed and recommend the use of the Stroke Fatigue Clinical Assessment Tool. CONCLUSIONS By synthesizing current knowledge in post-stroke fatigue across clinical and pre-clinical fields, our work provides a roadmap for future research into post-stroke fatigue.
Collapse
Affiliation(s)
- Coralie English
- School of Health Sciences, College of Health, Medicine and Wellbeing, University of Newcastle, Callaghan, NSW, Australia
- Heart and Stroke Program, Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - Dawn B Simpson
- School of Health Sciences, College of Health, Medicine and Wellbeing, University of Newcastle, Callaghan, NSW, Australia
- Heart and Stroke Program, Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - Sandra A Billinger
- Department of Neurology, University of Kansas Medical Centre, University of Kansas Alzheimer’s Disease Research Centre, Kansas City, KS, USA
| | - Leonid Churilov
- Department of Medicine (RMH), University of Melbourne, Heidelberg, VIC, Australia
| | - Kirsten G Coupland
- Heart and Stroke Program, Hunter Medical Research Institute, Newcastle, NSW, Australia
- School of Biomedical Sciences and Pharmacy, College of Health, Medicine and Wellbeing, University of Newcastle, Callaghan, NSW, Australia
| | - Avril Drummond
- School of Health Sciences, University of Nottingham, Nottingham, UK
| | | | | | - Anners Lerdal
- Department of Interdisciplinary Health Sciences, Institute of Health and Society, Faculty of Medicine, University of Oslo, Oslo, Norway
- Research Department, Lovisenberg Diaconal Hospital, Oslo, Norway
| | - Amreen Mahmood
- Faculty of Health, Health and Education, Manchester Metropolitan University, Manchester, UK
| | - G Lorimer Moseley
- IIMPACT in Health, University of South Australia, Adelaide, SA, Australia
| | - Quentin J Pittman
- Department of Physiology and Pharmacology, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Ellyn A Riley
- Department of Communication Sciences and Disorders, Syracuse University, Syracuse, NY, USA
| | - Brad A Sutherland
- Tasmanian School of Medicine, College of Health and Medicine, University of Tasmania, Hobart, TAS, Australia
| | - Connie HY Wong
- Centre for Inflammatory Diseases, Department of Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
| | - Dale Corbett
- Department of Cellular and Molecular Medicine, University of Ottawa Roger Guindon Hall, Ottawa, ON, Canada
| | - Gillian Mead
- Ageing and Health, Usher Institute, University of Edinburgh, Edinburgh, UK
| |
Collapse
|
5
|
Au Yeung J, Wang YY, Kraljevic Z, Teo JTH. Artificial intelligence (AI) for neurologists: do digital neurones dream of electric sheep? Pract Neurol 2023; 23:476-488. [PMID: 37977806 DOI: 10.1136/pn-2023-003757] [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] [Accepted: 08/29/2023] [Indexed: 11/19/2023]
Abstract
Artificial intelligence (AI) is routinely mentioned in journals and newspapers, and non-technical outsiders may have difficulty in distinguishing hyperbole from reality. We present a practical guide to help non-technical neurologists to understand healthcare AI. AI is being used to support clinical decisions in treating neurological disorders. We introduce basic concepts of AI, such as machine learning and natural language processing, and explain how AI is being used in healthcare, giving examples its benefits and challenges. We also cover how AI performance is measured, and its regulatory aspects in healthcare. An important theme is that AI is a general-purpose technology like medical statistics, with broad utility applicable in various scenarios, such that niche approaches are outpaced by approaches that are broadly applicable in many disease areas and specialties. By understanding AI basics and its potential applications, neurologists can make informed decisions when evaluating AI used in their clinical practice. This article was written by four humans, with generative AI helping with formatting and image generation.
Collapse
Affiliation(s)
- Joshua Au Yeung
- CogStack team, Guy's and St Thomas' NHS Foundation Trust, London, UK
- CogStack team, King's College Hospital NHS Foundation Trust, London, London, UK
| | - Yang Yang Wang
- Medicine, Guy's and St Thomas' Hospitals NHS Trust, London, London, UK
| | - Zeljko Kraljevic
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - James T H Teo
- CogStack team, Guy's and St Thomas' NHS Foundation Trust, London, UK
- CogStack team, King's College Hospital NHS Foundation Trust, London, London, UK
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| |
Collapse
|
6
|
Lin Z, Meng Z, Wang T, Guo R, Zhao Y, Li Y, Bo B, Guan Y, Liu J, Zhou H, Yu X, Lin DJ, Liang ZP, Nachev P, Li Y. Predicting the Onset of Ischemic Stroke With Fast High-Resolution 3D MR Spectroscopic Imaging. J Magn Reson Imaging 2023; 58:838-847. [PMID: 36625533 DOI: 10.1002/jmri.28596] [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: 09/16/2022] [Revised: 12/27/2022] [Accepted: 12/28/2022] [Indexed: 01/11/2023] Open
Abstract
BACKGROUND Neurometabolite concentrations provide a direct index of infarction progression in stroke. However, their relationship with stroke onset time remains unclear. PURPOSE To assess the temporal dynamics of N-acetylaspartate (NAA), creatine, choline, and lactate and estimate their value in predicting early (<6 hours) vs. late (6-24 hours) hyperacute stroke groups. STUDY TYPE Cross-sectional cohort. POPULATION A total of 73 ischemic stroke patients scanned at 1.8-302.5 hours after symptom onset, including 25 patients with follow-up scans. FIELD STRENGTH/SEQUENCE A 3 T/magnetization-prepared rapid acquisition gradient echo sequence for anatomical imaging, diffusion-weighted imaging and fluid-attenuated inversion recovery imaging for lesion delineation, and 3D MR spectroscopic imaging (MRSI) for neurometabolic mapping. ASSESSMENT Patients were divided into hyperacute (0-24 hours), acute (24 hours to 1 week), and subacute (1-2 weeks) groups, and into early (<6 hours) and late (6-24 hours) hyperacute groups. Bayesian logistic regression was used to compare classification performance between early and late hyperacute groups by using different combinations of neurometabolites as inputs. STATISTICAL TESTS Linear mixed effects modeling was applied for group-wise comparisons between NAA, creatine, choline, and lactate. Pearson's correlation analysis was used for neurometabolites vs. time. P < 0.05 was considered statistically significant. RESULTS Lesional NAA and creatine were significantly lower in subacute than in acute stroke. The main effects of time were shown on NAA (F = 14.321) and creatine (F = 12.261). NAA was significantly lower in late than early hyperacute patients, and was inversely related to time from symptom onset across both groups (r = -0.440). The decrease of NAA and increase of lactate were correlated with lesion volume (NAA: r = -0.472; lactate: r = 0.366) in hyperacute stroke. Discrimination was improved by combining NAA, creatine, and choline signals (area under the curve [AUC] = 0.90). DATA CONCLUSION High-resolution 3D MRSI effectively assessed the neurometabolite changes and discriminated early and late hyperacute stroke lesions. EVIDENCE LEVEL 1. TECHNICAL EFFICACY Stage 2.
Collapse
Affiliation(s)
- Zengping Lin
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Ziyu Meng
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Tianyao Wang
- Radiology Department, Shanghai Fifth People's Hospital, Fudan University, Shanghai, China
| | - Rong Guo
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Siemens Medical Solutions USA, Inc., Urbana, Illinois, USA
| | - Yibo Zhao
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Yudu Li
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Bin Bo
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yue Guan
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Jun Liu
- Radiology Department, Shanghai Fifth People's Hospital, Fudan University, Shanghai, China
| | - Hong Zhou
- Department of Radiology, The First Affiliated Hospital of South China of University, South China of University, Hengyang, China
| | - Xin Yu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - David J Lin
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Zhi-Pei Liang
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | | | - Yao Li
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| |
Collapse
|
7
|
Seghier ML. The elusive metric of lesion load. Brain Struct Funct 2023; 228:703-716. [PMID: 36947181 DOI: 10.1007/s00429-023-02630-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 03/15/2023] [Indexed: 03/23/2023]
Abstract
One of the widely used metrics in lesion-symptom mapping is lesion load that codes the amount of damage to a given brain region of interest. Lesion load aims to reduce the complex 3D lesion information into a feature that can reflect both site of damage, defined by the location of the region of interest, and size of damage within that region of interest. Basically, the process of estimation of lesion load converts a voxel-based lesion map into a region-based lesion map, with regions defined as atlas-based or data-driven spatial patterns. Here, after examining current definitions of lesion load, four methodological issues are discussed: (1) lesion load is agnostic to the location of damage within the region of interest, and it disregards damage outside the region of interest, (2) lesion load estimates are prone to errors introduced by the uncertainty in lesion delineation, spatial warping of the lesion/region, and binarization of the lesion/region, (3) lesion load calculation depends on brain parcellation selection, and (4) lesion load does not necessarily reflect a white matter disconnection. Overall, lesion load, when calculated in a robust way, can serve as a clinically-useful feature for explaining and predicting post-stroke outcome and recovery.
Collapse
Affiliation(s)
- Mohamed L Seghier
- Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, UAE.
- Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, Abu Dhabi, UAE.
| |
Collapse
|
8
|
Ferris JK, Lo BP, Khlif MS, Brodtmann A, Boyd LA, Liew SL. Optimizing automated white matter hyperintensity segmentation in individuals with stroke. FRONTIERS IN NEUROIMAGING 2023; 2:1099301. [PMID: 37554631 PMCID: PMC10406248 DOI: 10.3389/fnimg.2023.1099301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 02/15/2023] [Indexed: 08/10/2023]
Abstract
White matter hyperintensities (WMHs) are a risk factor for stroke. Consequently, many individuals who suffer a stroke have comorbid WMHs. The impact of WMHs on stroke recovery is an active area of research. Automated WMH segmentation methods are often employed as they require minimal user input and reduce risk of rater bias; however, these automated methods have not been specifically validated for use in individuals with stroke. Here, we present methodological validation of automated WMH segmentation methods in individuals with stroke. We first optimized parameters for FSL's publicly available WMH segmentation software BIANCA in two independent (multi-site) datasets. Our optimized BIANCA protocol achieved good performance within each independent dataset, when the BIANCA model was trained and tested in the same dataset or trained on mixed-sample data. BIANCA segmentation failed when generalizing a trained model to a new testing dataset. We therefore contrasted BIANCA's performance with SAMSEG, an unsupervised WMH segmentation tool available through FreeSurfer. SAMSEG does not require prior WMH masks for model training and was more robust to handling multi-site data. However, SAMSEG performance was slightly lower than BIANCA when data from a single site were tested. This manuscript will serve as a guide for the development and utilization of WMH analysis pipelines for individuals with stroke.
Collapse
Affiliation(s)
- Jennifer K. Ferris
- Graduate Program in Rehabilitation Sciences, University of British Columbia, Vancouver, BC, Canada
- Gerontology Research Centre, Simon Fraser University, Vancouver, BC, Canada
| | - Bethany P. Lo
- Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, United States
| | - Mohamed Salah Khlif
- Cognitive Health Initiative, Central Clinical School, Monash University, Melbourne, VIC, Australia
| | - Amy Brodtmann
- Cognitive Health Initiative, Central Clinical School, Monash University, Melbourne, VIC, Australia
- Department of Medicine, Royal Melbourne Hospital, Melbourne, VIC, Australia
| | - Lara A. Boyd
- Graduate Program in Rehabilitation Sciences, University of British Columbia, Vancouver, BC, Canada
- Department of Physical Therapy, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Sook-Lei Liew
- Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, United States
- Department of Neurology, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| |
Collapse
|
9
|
Bonkhoff AK, Schirmer MD, Bretzner M, Hong S, Regenhardt RW, Donahue KL, Nardin MJ, Dalca AV, Giese A, Etherton MR, Hancock BL, Mocking SJT, McIntosh EC, Attia J, Cole JW, Donatti A, Griessenauer CJ, Heitsch L, Holmegaard L, Jood K, Jimenez‐Conde J, Kittner SJ, Lemmens R, Levi CR, McDonough CW, Meschia JF, Phuah C, Ropele S, Rosand J, Roquer J, Rundek T, Sacco RL, Schmidt R, Sharma P, Slowik A, Sousa A, Stanne TM, Strbian D, Tatlisumak T, Thijs V, Vagal A, Wasselius J, Woo D, Zand R, McArdle PF, Worrall BB, Jern C, Lindgren AG, Maguire J, Wu O, Rost NS. The relevance of rich club regions for functional outcome post-stroke is enhanced in women. Hum Brain Mapp 2023; 44:1579-1592. [PMID: 36440953 PMCID: PMC9921242 DOI: 10.1002/hbm.26159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2022] [Revised: 10/24/2022] [Accepted: 11/11/2022] [Indexed: 11/30/2022] Open
Abstract
This study aimed to investigate the influence of stroke lesions in predefined highly interconnected (rich-club) brain regions on functional outcome post-stroke, determine their spatial specificity and explore the effects of biological sex on their relevance. We analyzed MRI data recorded at index stroke and ~3-months modified Rankin Scale (mRS) data from patients with acute ischemic stroke enrolled in the multisite MRI-GENIE study. Spatially normalized structural stroke lesions were parcellated into 108 atlas-defined bilateral (sub)cortical brain regions. Unfavorable outcome (mRS > 2) was modeled in a Bayesian logistic regression framework. Effects of individual brain regions were captured as two compound effects for (i) six bilateral rich club and (ii) all further non-rich club regions. In spatial specificity analyses, we randomized the split into "rich club" and "non-rich club" regions and compared the effect of the actual rich club regions to the distribution of effects from 1000 combinations of six random regions. In sex-specific analyses, we introduced an additional hierarchical level in our model structure to compare male and female-specific rich club effects. A total of 822 patients (age: 64.7[15.0], 39% women) were analyzed. Rich club regions had substantial relevance in explaining unfavorable functional outcome (mean of posterior distribution: 0.08, area under the curve: 0.8). In particular, the rich club-combination had a higher relevance than 98.4% of random constellations. Rich club regions were substantially more important in explaining long-term outcome in women than in men. All in all, lesions in rich club regions were associated with increased odds of unfavorable outcome. These effects were spatially specific and more pronounced in women.
Collapse
Affiliation(s)
- Anna K. Bonkhoff
- J. Philip Kistler Stroke Research CenterMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Markus D. Schirmer
- J. Philip Kistler Stroke Research CenterMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Martin Bretzner
- J. Philip Kistler Stroke Research CenterMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Univ. Lille, Inserm, CHU Lille, U1171 – LilNCog (JPARC) – Lille Neurosciences & CognitionLilleFrance
| | - Sungmin Hong
- J. Philip Kistler Stroke Research CenterMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Robert W. Regenhardt
- J. Philip Kistler Stroke Research CenterMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Kathleen L. Donahue
- J. Philip Kistler Stroke Research CenterMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Marco J. Nardin
- J. Philip Kistler Stroke Research CenterMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Adrian V. Dalca
- Computer Science and Artificial Intelligence LabMassachusetts Institute of TechnologyBostonMassachusettsUSA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of RadiologyMassachusetts General HospitalCharlestownMassachusettsUSA
| | - Anne‐Katrin Giese
- Department of NeurologyUniversity Medical Center Hamburg‐EppendorfHamburgGermany
| | - Mark R. Etherton
- J. Philip Kistler Stroke Research CenterMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Brandon L. Hancock
- Athinoula A. Martinos Center for Biomedical Imaging, Department of RadiologyMassachusetts General HospitalCharlestownMassachusettsUSA
| | - Steven J. T. Mocking
- Athinoula A. Martinos Center for Biomedical Imaging, Department of RadiologyMassachusetts General HospitalCharlestownMassachusettsUSA
| | - Elissa C. McIntosh
- Department of PsychiatryJohns Hopkins School of MedicineBaltimoreMarylandUSA
| | - John Attia
- Hunter Medical Research InstituteNewcastleNew South WalesAustralia
- School of Medicine and Public HealthUniversity of NewcastleNewcastleNew South WalesAustralia
| | - John W. Cole
- Department of NeurologyUniversity of Maryland School of Medicine and Veterans Affairs Maryland Health Care SystemBaltimoreMarylandUSA
| | - Amanda Donatti
- School of Medical SciencesUniversity of Campinas (UNICAMP) and the Brazilian Institute of Neuroscience and Neurotechnology (BRAINN)CampinasSão PauloBrazil
| | - Christoph J. Griessenauer
- Department of NeurosurgeryGeisingerDanvillePennsylvaniaUSA
- Research Institute of NeurointerventionParacelsus Medical UniversitySalzburgAustria
| | - Laura Heitsch
- Department of Emergency MedicineWashington University School of MedicineSt LouisMissouriUSA
- Department of NeurologyWashington University School of Medicine & Barnes‐Jewish HospitalSt LouisMissouriUSA
| | - Lukas Holmegaard
- Department of Clinical NeuroscienceInstitute of Neuroscience and Physiology, Sahlgrenska Academy, University of GothenburgGothenburgSweden
- Department of NeurologySahlgrenska University HospitalGothenburgSweden
| | - Katarina Jood
- Department of Clinical NeuroscienceInstitute of Neuroscience and Physiology, Sahlgrenska Academy, University of GothenburgGothenburgSweden
- Department of NeurologySahlgrenska University HospitalGothenburgSweden
| | - Jordi Jimenez‐Conde
- Department of Neurology, Neurovascular Research Group (NEUVAS), IMIM‐Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques). Department of Medicine and Life Sciences (MELIS)Universitat Pompeu FabraBarcelonaSpain
| | - Steven J. Kittner
- Department of NeurologyUniversity of Maryland School of Medicine and Veterans Affairs Maryland Health Care SystemBaltimoreMarylandUSA
| | - Robin Lemmens
- Department of NeurosciencesKU Leuven – University of Leuven, Experimental Neurology and Leuven Research Institute for Neuroscience and Disease (LIND)LeuvenBelgium
- Department of Neurology, VIB, Vesalius Research CenterLaboratory of Neurobiology, University Hospitals LeuvenLeuvenBelgium
| | - Christopher R. Levi
- School of Medicine and Public HealthUniversity of NewcastleNewcastleNew South WalesAustralia
- Department of NeurologyJohn Hunter HospitalNewcastleNew South WalesAustralia
| | - Caitrin W. McDonough
- Department of Pharmacotherapy and Translational Research and Center for PharmacogenomicsUniversity of FloridaGainesvilleFloridaUSA
| | | | - Chia‐Ling Phuah
- Department of NeurologyWashington University School of Medicine & Barnes‐Jewish HospitalSt LouisMissouriUSA
| | - Stefan Ropele
- Department of Neurology, Clinical Division of NeurogeriatricsMedical University GrazGrazAustria
| | - Jonathan Rosand
- J. Philip Kistler Stroke Research CenterMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of RadiologyMassachusetts General HospitalCharlestownMassachusettsUSA
- Henry and Allison McCance Center for Brain HealthMassachusetts General HospitalBostonMassachusettsUSA
| | - Jaume Roquer
- Department of Neurology, Neurovascular Research Group (NEUVAS), IMIM‐Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques). Department of Medicine and Life Sciences (MELIS)Universitat Pompeu FabraBarcelonaSpain
| | - Tatjana Rundek
- Department of Neurology and Evelyn F. McKnight Brain Institute, Miller School of MedicineUniversity of MiamiMiamiFloridaUSA
| | - Ralph L. Sacco
- Department of Neurology and Evelyn F. McKnight Brain Institute, Miller School of MedicineUniversity of MiamiMiamiFloridaUSA
| | - Reinhold Schmidt
- Department of Neurology, Clinical Division of NeurogeriatricsMedical University GrazGrazAustria
| | - Pankaj Sharma
- Institute of Cardiovascular Research, Royal Holloway University of London (ICR2UL)EghamUK
- St Peter's and Ashford HospitalsAshfordUK
| | - Agnieszka Slowik
- Department of NeurologyJagiellonian University Medical CollegeKrakowPoland
| | - Alessandro Sousa
- School of Medical SciencesUniversity of Campinas (UNICAMP) and the Brazilian Institute of Neuroscience and Neurotechnology (BRAINN)CampinasSão PauloBrazil
| | - Tara M. Stanne
- Department of Laboratory Medicine, Institute of Biomedicine, the Sahlgrenska AcademyUniversity of GothenburgGothenburgSweden
| | - Daniel Strbian
- Department of NeurologyHelsinki University Hospital and University of HelsinkiHelsinkiFinland
| | - Turgut Tatlisumak
- Department of Clinical NeuroscienceInstitute of Neuroscience and Physiology, Sahlgrenska Academy, University of GothenburgGothenburgSweden
- Department of NeurologySahlgrenska University HospitalGothenburgSweden
| | - Vincent Thijs
- Stroke DivisionFlorey Institute of Neuroscience and Mental HealthHeidelbergAustralia
- Department of NeurologyAustin HealthHeidelbergAustralia
| | - Achala Vagal
- Department of RadiologyUniversity of Cincinnati College of MedicineCincinnatiOhioUSA
| | - Johan Wasselius
- Department of Clinical Sciences Lund, RadiologyLund UniversityLundSweden
- Department of Radiology, NeuroradiologySkåne University HospitalLundSweden
| | - Daniel Woo
- Department of Neurology and Rehabilitation MedicineUniversity of Cincinnati College of MedicineCincinnatiOhioUSA
| | - Ramin Zand
- Department of NeurologyPennsylvania State UniversityHersheyPennsylvaniaUSA
| | - Patrick F. McArdle
- Division of Endocrinology, Diabetes and Nutrition, Department of MedicineUniversity of Maryland School of MedicineBaltimoreMarylandUSA
| | - Bradford B. Worrall
- Departments of Neurology and Public Health SciencesUniversity of VirginiaCharlottesvilleVirginiaUSA
| | - Christina Jern
- Department of NeurologyJagiellonian University Medical CollegeKrakowPoland
- Department of Clinical Genetics and GenomicsSahlgrenska University HospitalGothenburgSweden
| | - Arne G. Lindgren
- Department of NeurologySkåne University HospitalLundSweden
- Department of Clinical Sciences Lund, NeurologyLund UniversityLundSweden
| | | | - Ona Wu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of RadiologyMassachusetts General HospitalCharlestownMassachusettsUSA
| | - Natalia S. Rost
- J. Philip Kistler Stroke Research CenterMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | | |
Collapse
|
10
|
Edwards LS, Cappelen-Smith C, Cordato D, Bivard A, Churilov L, Lin L, Chen C, Garcia-Esperon C, Butcher K, Kleinig T, Choi PMC, Cheng X, Dong Q, Aviv RI, Parsons MW. Optimal CT perfusion thresholds for core and penumbra in acute posterior circulation infarction. Front Neurol 2023; 14:1092505. [PMID: 36846146 PMCID: PMC9947562 DOI: 10.3389/fneur.2023.1092505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 01/23/2023] [Indexed: 02/11/2023] Open
Abstract
Background At least 20% of strokes involve the posterior circulation (PC). Compared to the anterior circulation, posterior circulation infarction (POCI) are frequently misdiagnosed. CT perfusion (CTP) has advanced stroke care by improving diagnostic accuracy and expanding eligibility for acute therapies. Clinical decisions are predicated upon precise estimates of the ischaemic penumbra and infarct core. Current thresholds for defining core and penumbra are based upon studies of anterior circulation stroke. We aimed to define the optimal CTP thresholds for core and penumbra in POCI. Methods Data were analyzed from 331-patients diagnosed with acute POCI enrolled in the International-stroke-perfusion-registry (INSPIRE). Thirty-nine patients with baseline multimodal-CT with occlusion of a large PC-artery and follow up diffusion weighted MRI at 24-48 h were included. Patients were divided into two-groups based on artery-recanalization on follow-up imaging. Patients with no or complete recanalisation were used for penumbral and infarct-core analysis, respectively. A Receiver operating curve (ROC) analysis was used for voxel-based analysis. Optimality was defined as the CTP parameter and threshold which maximized the area-under-the-curve. Linear regression was used for volume based analysis determining the CTP threshold which resulted in the smallest mean volume difference between the acute perfusion lesion and follow up MRI. Subanalysis of PC-regions was performed. Results Mean transit time (MTT) and delay time (DT) were the best CTP parameters to characterize ischaemic penumbra (AUC = 0.73). Optimal thresholds for penumbra were a DT >1 s and MTT>145%. Delay time (DT) best estimated the infarct core (AUC = 0.74). The optimal core threshold was a DT >1.5 s. The voxel-based analyses indicated CTP was most accurate in the calcarine (Penumbra-AUC = 0.75, Core-AUC = 0.79) and cerebellar regions (Penumbra-AUC = 0.65, Core-AUC = 0.79). For the volume-based analyses, MTT >160% demonstrated best correlation and smallest mean-volume difference between the penumbral estimate and follow-up MRI (R 2 = 0.71). MTT >170% resulted in the smallest mean-volume difference between the core estimate and follow-up MRI, but with poor correlation (R 2 = 0.11). Conclusion CTP has promising diagnostic utility in POCI. Accuracy of CTP varies by brain region. Optimal thresholds to define penumbra were DT >1 s and MTT >145%. The optimal threshold for core was a DT >1.5 s. However, CTP core volume estimates should be interpreted with caution.
Collapse
Affiliation(s)
- Leon Stephen Edwards
- Department of Neurology and Neurophysiology, Liverpool Hospital, Sydney, NSW, Australia,South Western Sydney Clinical School, University of New South Wales, Sydney, NSW, Australia,Sydney Brain Centre, Ingham Institute of Applied Medical Research, Sydney, NSW, Australia,School of Medicine and Public Health, University of Newcastle, Newcastle, NSW, Australia,*Correspondence: Leon Stephen Edwards ✉
| | - Cecilia Cappelen-Smith
- Department of Neurology and Neurophysiology, Liverpool Hospital, Sydney, NSW, Australia,South Western Sydney Clinical School, University of New South Wales, Sydney, NSW, Australia,Sydney Brain Centre, Ingham Institute of Applied Medical Research, Sydney, NSW, Australia
| | - Dennis Cordato
- Department of Neurology and Neurophysiology, Liverpool Hospital, Sydney, NSW, Australia,South Western Sydney Clinical School, University of New South Wales, Sydney, NSW, Australia,Sydney Brain Centre, Ingham Institute of Applied Medical Research, Sydney, NSW, Australia
| | - Andrew Bivard
- School of Medicine and Public Health, University of Newcastle, Newcastle, NSW, Australia,Melbourne Brain Centre at the Royal Melbourne Hospital, University of Melbourne, Parkville, VIC, Australia
| | - Leonid Churilov
- School of Medicine and Public Health, University of Newcastle, Newcastle, NSW, Australia,Melbourne Brain Centre at the Royal Melbourne Hospital, University of Melbourne, Parkville, VIC, Australia
| | - Longting Lin
- Department of Neurology and Neurophysiology, Liverpool Hospital, Sydney, NSW, Australia,South Western Sydney Clinical School, University of New South Wales, Sydney, NSW, Australia,School of Medicine and Public Health, University of Newcastle, Newcastle, NSW, Australia
| | - Chushuang Chen
- School of Medicine and Public Health, University of Newcastle, Newcastle, NSW, Australia,Melbourne Brain Centre at the Royal Melbourne Hospital, University of Melbourne, Parkville, VIC, Australia
| | - Carlos Garcia-Esperon
- School of Medicine and Public Health, University of Newcastle, Newcastle, NSW, Australia,Department of Neurology, John Hunter Hospital, Newcastle, NSW, Australia,Stroke and Brain Injury Group, Hunter Medical Research Institute and the University of Newcastle, Newcastle, NSW, Australia
| | - Kenneth Butcher
- Prince of Wales Clinical School, University of New South Wales, Sydney, NSW, Australia
| | - Tim Kleinig
- Department of Neurology, Royal Adelaide Hospital, Adelaide, SA, Australia
| | - Phillip M. C. Choi
- Department of Neurosciences, Box Hill Hospital, Eastern Health Clinical School, Monash University, Melbourne, VIC, Australia
| | - Xin Cheng
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
| | - Qiang Dong
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
| | - Richard I. Aviv
- Division of Neuroradiology, Department of Radiology, University of Ottawa and The Ottawa Hospital, Ottawa, ON, Canada
| | - Mark William Parsons
- Department of Neurology and Neurophysiology, Liverpool Hospital, Sydney, NSW, Australia,South Western Sydney Clinical School, University of New South Wales, Sydney, NSW, Australia,Sydney Brain Centre, Ingham Institute of Applied Medical Research, Sydney, NSW, Australia,School of Medicine and Public Health, University of Newcastle, Newcastle, NSW, Australia
| |
Collapse
|
11
|
Guo Y, Yang Y, Cao F, Wang M, Luo Y, Guo J, Liu Y, Zeng X, Miu X, Zaman A, Lu J, Kang Y. A Focus on the Role of DSC-PWI Dynamic Radiomics Features in Diagnosis and Outcome Prediction of Ischemic Stroke. J Clin Med 2022; 11:jcm11185364. [PMID: 36143010 PMCID: PMC9504165 DOI: 10.3390/jcm11185364] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 09/06/2022] [Accepted: 09/08/2022] [Indexed: 12/18/2022] Open
Abstract
Background: The ability to accurately detect ischemic stroke and predict its neurological recovery is of great clinical value. This study intended to evaluate the performance of whole-brain dynamic radiomics features (DRF) for ischemic stroke detection, neurological impairment assessment, and outcome prediction. Methods: The supervised feature selection (Lasso) and unsupervised feature-selection methods (five-feature dimension-reduction algorithms) were used to generate four experimental groups with DRF in different combinations. Ten machine learning models were used to evaluate their performance by ten-fold cross-validation. Results: In experimental group_A, the best AUCs (0.873 for stroke detection, 0.795 for NIHSS assessment, and 0.818 for outcome prediction) were obtained by outstanding DRF selected by Lasso, and the performance of significant DRF was better than the five-feature dimension-reduction algorithms. The selected outstanding dimension-reduction DRF in experimental group_C obtained a better AUC than dimension-reduction DRF in experimental group_A but were inferior to the outstanding DRF in experimental group_A. When combining the outstanding DRF with each dimension-reduction DRF (experimental group_B), the performance can be improved in ischemic stroke detection (best AUC = 0.899) and NIHSS assessment (best AUC = 0.835) but failed in outcome prediction (best AUC = 0.806). The performance can be further improved when combining outstanding DRF with outstanding dimension-reduction DRF (experimental group_D), achieving the highest AUC scores in all three evaluation items (0.925 for stroke detection, 0.853 for NIHSS assessment, and 0.828 for outcome prediction). By the method in this study, comparing the best AUC of Ft-test in experimental group_A and the best_AUC in experimental group_D, the AUC in stroke detection increased by 19.4% (from 0.731 to 0.925), the AUC in NIHSS assessment increased by 20.1% (from 0.652 to 0.853), and the AUC in prognosis prediction increased by 14.9% (from 0.679 to 0.828). This study provided a potential clinical tool for detailed clinical diagnosis and outcome prediction before treatment.
Collapse
Affiliation(s)
- Yingwei Guo
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China
| | - Yingjian Yang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China
| | - Fengqiu Cao
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China
| | - Mingming Wang
- Department of Radiology, Shanghai Fourth People’s Hospital Affiliated to Tongji University School of Medicine, Shanghai 200434, China
| | - Yu Luo
- Department of Radiology, Shanghai Fourth People’s Hospital Affiliated to Tongji University School of Medicine, Shanghai 200434, China
- Correspondence: (Y.L.); (Y.K.); Tel.: +86-13-94-047-2926 (Y.K.)
| | - Jia Guo
- Department of Psychiatry, Columbia University, New York, NY 10027, USA
| | - Yang Liu
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China
| | - Xueqiang Zeng
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China
- School of Applied Technology, Shenzhen University, Shenzhen 518060, China
| | - Xiaoqiang Miu
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China
| | - Asim Zaman
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China
- School of Applied Technology, Shenzhen University, Shenzhen 518060, China
| | - Jiaxi Lu
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China
- School of Applied Technology, Shenzhen University, Shenzhen 518060, China
| | - Yan Kang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, China
- Engineering Research Centre of Medical Imaging and Intelligent Analysis, Ministry of Education, Shenyang 110169, China
- Correspondence: (Y.L.); (Y.K.); Tel.: +86-13-94-047-2926 (Y.K.)
| |
Collapse
|
12
|
Bonkhoff AK, Grefkes C. Precision medicine in stroke: towards personalized outcome predictions using artificial intelligence. Brain 2021; 145:457-475. [PMID: 34918041 PMCID: PMC9014757 DOI: 10.1093/brain/awab439] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Revised: 11/02/2021] [Accepted: 11/21/2021] [Indexed: 11/16/2022] Open
Abstract
Stroke ranks among the leading causes for morbidity and mortality worldwide. New and continuously improving treatment options such as thrombolysis and thrombectomy have revolutionized acute stroke treatment in recent years. Following modern rhythms, the next revolution might well be the strategic use of the steadily increasing amounts of patient-related data for generating models enabling individualized outcome predictions. Milestones have already been achieved in several health care domains, as big data and artificial intelligence have entered everyday life. The aim of this review is to synoptically illustrate and discuss how artificial intelligence approaches may help to compute single-patient predictions in stroke outcome research in the acute, subacute and chronic stage. We will present approaches considering demographic, clinical and electrophysiological data, as well as data originating from various imaging modalities and combinations thereof. We will outline their advantages, disadvantages, their potential pitfalls and the promises they hold with a special focus on a clinical audience. Throughout the review we will highlight methodological aspects of novel machine-learning approaches as they are particularly crucial to realize precision medicine. We will finally provide an outlook on how artificial intelligence approaches might contribute to enhancing favourable outcomes after stroke.
Collapse
Affiliation(s)
- Anna K Bonkhoff
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Christian Grefkes
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre Juelich, Juelich, Germany.,Department of Neurology, University Hospital Cologne.,Medical Faculty, University of Cologne, Germany
| |
Collapse
|