| 1. |
- Bourached, Anthony, et al.
(författare)
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Scaling behaviours of deep learning and linear algorithms for the prediction of stroke severity
- 2023
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Ingår i: BRAIN COMMUNICATIONS. - 2632-1297. ; 6:1
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Tidskriftsartikel (refereegranskat)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 similar to 20% when increasing the sample size 9x [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. Bourached et al. contrast linear and deep learning-based algorithms in their prediction performances of stroke severity depending on the training set sample sizes. They find that linear regression outperforms deep learning-based algorithms for smaller training samples comprising lesion location information of 100 patients, while deep learning excels in the case of larger samples (N = 900).
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| 2. |
- Giese, Anne Katrin, et al.
(författare)
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Design and rationale for examining neuroimaging genetics in ischemic stroke : The MRI-GENIE study
- 2017
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Ingår i: Neurology: Genetics. - 2376-7839. ; 3:5
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Tidskriftsartikel (refereegranskat)abstract
- Objective: To describe the design and rationale for the genetic analysis of acute and chronic cerebrovascular neuroimaging phenotypes detected on clinical MRI in patients with acute ischemic stroke (AIS) within the scope of the MRI-GENetics Interface Exploration (MRI-GENIE) study. Methods: MRI-GENIE capitalizes on the existing infrastructure of the Stroke Genetics Network (SiGN). In total, 12 international SiGN sites contributedMRIs of 3,301 patients with AIS. Detailed clinical phenotyping with the web-based Causative Classification of Stroke (CCS) system and genome-wide genotyping data were available for all participants. Neuroimaging analyses include themanual and automated assessments of established MRI markers. A high-throughputMRI analysis pipeline for the automated assessment of cerebrovascular lesions on clinical scans will be developed in a subset of scans for both acute and chronic lesions, validated against gold standard, and applied to all available scans. The extracted neuroimaging phenotypes will improve characterization of acute and chronic cerebrovascular lesions in ischemic stroke, including CCS subtypes, and their effect on functional outcomes after stroke. Moreover, genetic testing will uncover variants associated with acute and chronic MRI manifestations of cerebrovascular disease.Conclusions: The MRI-GENIE study aims to develop, validate, and distribute the MRI analysis platform for scans acquired as part of clinical care for patients with AIS, which will lead to (1) novel genetic discoveries in ischemic stroke, (2) strategies for personalized stroke risk assessment, and (3) personalized stroke outcome assessment.
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| 3. |
- Kamnitsas, Konstantinos, et al.
(författare)
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Transductive Image Segmentation : Self-training and Effect of Uncertainty Estimation
- 2021
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Ingår i: Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health - 3rd MICCAI Workshop, DART 2021, and 1st MICCAI Workshop, FAIR 2021, Held in Conjunction with MICCAI 2021, Proceedings. - Cham : Springer International Publishing. - 1611-3349 .- 0302-9743. - 9783030877217 ; 12968 LNCS, s. 79-89
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Konferensbidrag (refereegranskat)abstract
- Semi-supervised learning (SSL) uses unlabeled data during training to learn better models. Previous studies on SSL for medical image segmentation focused mostly on improving model generalization to unseen data. In some applications, however, our primary interest is not generalization but to obtain optimal predictions on a specific unlabeled database that is fully available during model development. Examples include population studies for extracting imaging phenotypes. This work investigates an often overlooked aspect of SSL, transduction. It focuses on the quality of predictions made on the unlabeled data of interest when they are included for optimization during training, rather than improving generalization. We focus on the self-training framework and explore its potential for transduction. We analyze it through the lens of Information Gain and reveal that learning benefits from the use of calibrated or under-confident models. Our extensive experiments on a large MRI database for multi-class segmentation of traumatic brain lesions shows promising results when comparing transductive with inductive predictions. We believe this study will inspire further research on transductive learning, a well-suited paradigm for medical image analysis.
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| 4. |
- Kornaropoulos, Evgenios N, et al.
(författare)
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Sensitivity of Diffusion MRI to White Matter Pathology : Influence of Diffusion Protocol, Magnetic Field Strength, and Processing Pipeline in Systemic Lupus Erythematosus
- 2022
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Ingår i: Frontiers in Neurology. - : Frontiers Media SA. - 1664-2295. ; 13, s. 1-20
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Tidskriftsartikel (refereegranskat)abstract
- There are many ways to acquire and process diffusion MRI (dMRI) data for group studies, but it is unknown which maximizes the sensitivity to white matter (WM) pathology. Inspired by this question, we analyzed data acquired for diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) at 3T (3T-DTI and 3T-DKI) and DTI at 7T in patients with systemic lupus erythematosus (SLE) and healthy controls (HC). Parameter estimates in 72 WM tracts were obtained using TractSeg. The impact on the sensitivity to WM pathology was evaluated for the diffusion protocol, the magnetic field strength, and the processing pipeline. Sensitivity was quantified in terms of Cohen's d for group comparison. Results showed that the choice of diffusion protocol had the largest impact on the effect size. The effect size in fractional anisotropy (FA) across all WM tracts was 0.26 higher when derived by DTI than by DKI and 0.20 higher in 3T compared with 7T. The difference due to the diffusion protocol was larger than the difference due to magnetic field strength for the majority of diffusion parameters. In contrast, the difference between including or excluding different processing steps was near negligible, except for the correction of distortions from eddy currents and motion which had a clearly positive impact. For example, effect sizes increased on average by 0.07 by including motion and eddy correction for FA derived from 3T-DTI. Effect sizes were slightly reduced by the incorporation of denoising and Gibbs-ringing removal (on average by 0.011 and 0.005, respectively). Smoothing prior to diffusion model fitting generally reduced effect sizes. In summary, 3T-DTI in combination with eddy current and motion correction yielded the highest sensitivity to WM pathology in patients with SLE. However, our results also indicated that the 3T-DKI and 7T-DTI protocols used here may be adjusted to increase effect sizes.
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| 5. |
- Newcombe, Virginia F J, et al.
(författare)
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Post-acute blood biomarkers and disease progression in traumatic brain injury.
- 2022
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Ingår i: Brain : a journal of neurology. - : Oxford University Press (OUP). - 1460-2156. ; 145:6, s. 2064-2076
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Tidskriftsartikel (refereegranskat)abstract
- There is substantial interest in the potential for traumatic brain injury to result in progressive neurological deterioration. While blood biomarkers such as glial fibrillary acid protein and neurofilament light have been widely explored in characterising acute traumatic brain injury, their use in the chronic phase is limited. Given increasing evidence that these proteins may be markers of ongoing neurodegeneration in a range of diseases, we examined their relationship to imaging changes and functional outcome in the months to years following traumatic brain injury. Two-hundred and three patients were recruited in two separate cohorts; six months post-injury (n=165); and >5 years post-injury (n=38; 12 of whom also provided data ∼8 months post-TBI). Subjects underwent blood biomarker sampling (n=199) and magnetic resonance imaging (n=172; including diffusion tensor imaging). Data from patient cohorts were compared to 59 healthy volunteers and 21 non-brain injury trauma controls. Mean diffusivity and fractional anisotropy were calculated in cortical grey matter, deep grey matter and whole brain white matter. Accelerated brain ageing was calculated at a whole brain level as the predicted age difference defined using T1-weighted images, and at a voxel-based level as the annualised Jacobian determinants in white matter and grey matter, referenced to a population of 652 healthy control subjects. Serum neurofilament light concentrations were elevated in the early chronic phase. While GFAP values were within the normal range at ∼8 months, many patients showed a secondary and temporally distinct elevations up to >5 years after injury. Biomarker elevation at six months was significantly related to metrics of microstructural injury on diffusion tensor imaging. Biomarker levels at ∼8 months predicted white matter volume loss at >5 years, and annualised brain volume loss between ∼8 months and 5 years. Patients who worsened functionally between ∼8 months and >5 years showed higher than predicted brain age and elevated neurofilament light levels. Glial fibrillary acid protein and neurofilament light levels can remain elevated months to years after traumatic brain injury, and show distinct temporal profiles. These elevations correlate closely with microstructural injury in both grey and white matter on contemporaneous quantitative diffusion tensor imaging. Neurofilament light elevations at ∼8 months may predict ongoing white matter and brain volume loss over >5 years of follow up. If confirmed, these findings suggest that blood biomarker levels at late time points could be used to identify traumatic brain injury survivors who are at high risk of progressive neurological damage.
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| 6. |
- Richter, Sophie, et al.
(författare)
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Serum biomarkers identify critically ill traumatic brain injury patients for MRI
- 2022
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Ingår i: Critical Care. - : BioMed Central (BMC). - 1364-8535 .- 1466-609X. ; 26:1
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Magnetic resonance imaging (MRI) carries prognostic importance after traumatic brain injury (TBI), especially when computed tomography (CT) fails to fully explain the level of unconsciousness. However, in critically ill patients, the risk of deterioration during transfer needs to be balanced against the benefit of detecting prognostically relevant information on MRI. We therefore aimed to assess if day of injury serum protein biomarkers could identify critically ill TBI patients in whom the risks of transfer are compensated by the likelihood of detecting management-altering neuroimaging findings.METHODS: Data were obtained from the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) study. Eligibility criteria included: TBI patients aged ≥ 16 years, Glasgow Coma Score (GCS) < 13 or patient intubated with unrecorded pre-intubation GCS, CT with Marshall score < 3, serum biomarkers (GFAP, NFL, NSE, S100B, Tau, UCH-L1) sampled ≤ 24 h of injury, MRI < 30 days of injury. The degree of axonal injury on MRI was graded using the Adams-Gentry classification. The association between serum concentrations of biomarkers and Adams-Gentry stage was assessed and the optimum threshold concentration identified, assuming different minimum sensitivities for the detection of brainstem injury (Adams-Gentry stage 3). A cost-benefit analysis for the USA and UK health care settings was also performed. RESULTS: Among 65 included patients (30 moderate-severe, 35 unrecorded) axonal injury was detected in 54 (83%) and brainstem involvement in 33 (51%). In patients with moderate-severe TBI, brainstem injury was associated with higher concentrations of NSE, Tau, UCH-L1 and GFAP. If the clinician did not want to miss any brainstem injury, NSE could have avoided MRI transfers in up to 20% of patients. If a 94% sensitivity was accepted considering potential transfer-related complications, GFAP could have avoided 30% of transfers. There was no added net cost, with savings up to £99 (UK) or $612 (US). No associations between proteins and axonal injury were found in intubated patients without a recorded pre-intubation GCS.CONCLUSIONS: Serum protein biomarkers show potential to safely reduce the number of transfers to MRI in critically ill patients with moderate-severe TBI at no added cost.
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| 7. |
- Wu, Ona, et al.
(författare)
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Big Data Approaches to Phenotyping Acute Ischemic Stroke Using Automated Lesion Segmentation of Multi-Center Magnetic Resonance Imaging Data
- 2019
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Ingår i: Stroke. - 1524-4628. ; 50:7, s. 1734-1741
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Tidskriftsartikel (refereegranskat)abstract
- Background and Purpose- We evaluated deep learning algorithms' segmentation of acute ischemic lesions on heterogeneous multi-center clinical diffusion-weighted magnetic resonance imaging (MRI) data sets and explored the potential role of this tool for phenotyping acute ischemic stroke. Methods- Ischemic stroke data sets from the MRI-GENIE (MRI-Genetics Interface Exploration) repository consisting of 12 international genetic research centers were retrospectively analyzed using an automated deep learning segmentation algorithm consisting of an ensemble of 3-dimensional convolutional neural networks. Three ensembles were trained using data from the following: (1) 267 patients from an independent single-center cohort, (2) 267 patients from MRI-GENIE, and (3) mixture of (1) and (2). The algorithms' performances were compared against manual outlines from a separate 383 patient subset from MRI-GENIE. Univariable and multivariable logistic regression with respect to demographics, stroke subtypes, and vascular risk factors were performed to identify phenotypes associated with large acute diffusion-weighted MRI volumes and greater stroke severity in 2770 MRI-GENIE patients. Stroke topography was investigated. Results- The ensemble consisting of a mixture of MRI-GENIE and single-center convolutional neural networks performed best. Subset analysis comparing automated and manual lesion volumes in 383 patients found excellent correlation (ρ=0.92; P<0.0001). Median (interquartile range) diffusion-weighted MRI lesion volumes from 2770 patients were 3.7 cm3 (0.9-16.6 cm3). Patients with small artery occlusion stroke subtype had smaller lesion volumes ( P<0.0001) and different topography compared with other stroke subtypes. Conclusions- Automated accurate clinical diffusion-weighted MRI lesion segmentation using deep learning algorithms trained with multi-center and diverse data is feasible. Both lesion volume and topography can provide insight into stroke subtypes with sufficient sample size from big heterogeneous multi-center clinical imaging phenotype data sets.
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