Simultaneous lesion and neuroanatomy segmentation in Multiple Sclerosis using deep neural networks

Segmentation of both white matter lesions and deep grey matter structures is an important task in the quantification of magnetic resonance imaging in multiple sclerosis. Typically these tasks are performed separately: in this paper we present a single segmentation solution based on convolutional neural networks (CNNs) for providing fast, reliable segmentations of multimodal magnetic resonance images into lesion classes and normal-appearing grey- and white-matter structures. We show substantial, statistically significant improvements in both Dice coefficient and in lesion-wise specificity and sensitivity, compared to previous approaches, and agreement with individual human raters in the range of human inter-rater variability. The method is trained on data gathered from a single centre: nonetheless, it performs well on data from centres, scanners and field-strengths not represented in the training dataset. A retrospective study found that the classifier successfully identified lesions missed by the human raters. Lesion labels were provided by human raters, while weak labels for other brain structures (including CSF, cortical grey matter, cortical white matter, cerebellum, amygdala, hippocampus, subcortical GM structures and choroid plexus) were provided by Freesurfer 5.3. The segmentations of these structures compared well, not only with Freesurfer 5.3, but also with FSL-First and Freesurfer 6.0

Association of the 24‐Hour National Institutes of Health Stroke Scale After Mechanical Thrombectomy With Early and Long‐Term Survival

Background The National Institutes of Health Stroke Scale (NIHSS) obtained 24 hours after ischemic stroke is a good indicator for functional outcome and early mortality, but the correlation with long‐term survival is less clear. We analyzed the correlation of the NIHSS after 24 hours (24h NIHSS) and early clinical neurological development after mechanical thrombectomy with early and long‐term survival as well as its predictive power on survival. Methods We reviewed a prospective observational registry for all patients undergoing mechanical thrombectomy between January 2010 and December 2018. Vital status was extracted from the Swiss Population Registry. Adjusted hazard ratio (aHR) and crude hazard ratios were calculated using Cox regression. To assess predictive power of the 24h NIHSS, different Random Survival Forest models were evaluated. Results We included 957 patients (median follow‐up 1376 days). Patients with lower 24h NIHSS and major early neurological improvement had substantially better survival rates. We observed significantly higher aHR for death in patients with 24h NIHSS 12 to 15 (aHR, 1.78; 95% CI, 1.1–2.89), with 24h NIHSS 16 to 21 (aHR, 2.54, 95% CI, 1.59–4.06), and with 24h NIHSS >21 (aHR, 5.74; 95% CI, 3.47–9.5). The 24h NIHSS showed the best performance predicting mortality (receiver operating characteristic area under the curve at 3 months [0.85±0.034], at 1 year [0.82±0.029], at 2 years [0.82±0.031], and at 5 years [0.83±0.035]), followed by NIHSS change. Conclusions Patients with acute ischemic stroke achieving a low 24h NIHSS or major early neurological improvement after mechanical thrombectomy had markedly lower long‐term mortality. Furthermore, 24h NIHSS had the best predictive power for early and long‐term survival in our machine learning–based prediction

Assessing Spinal Cerebrospinal Fluid Leaks in Spontaneous Intracranial Hypotension With a Scoring System Based on Brain Magnetic Resonance Imaging Findings.

Importance Various signs may be observed on brain magnetic resonance imaging (MRI) in patients with spontaneous intracranial hypotension (SIH). However, the lack of a classification system integrating these findings limits decision making in clinical practice. Objective To develop a probability score based on the most relevant brain MRI findings to assess the likelihood of an underlying spinal cerebrospinal fluid (CSF) leak in patients with SIH. Design, Setting, and Participants This case-control study in consecutive patients investigated for SIH was conducted at a single hospital department from February 2013 to October 2017. Patients with missing brain MRI data were excluded. Three blinded readers retrospectively reviewed the brain MRI scans of patients with SIH and a spinal CSF leak, patients with orthostatic headache without a CSF leak, and healthy control participants, evaluating 9 quantitative and 7 qualitative signs. A predictive diagnostic score based on multivariable backward logistic regression analysis was then derived. Its performance was validated internally in a prospective cohort of patients who had clinical suspicion for SIH. Main Outcomes and Measures Likelihood of a spinal CSF leak based on the proposed diagnostic score. Results A total of 152 participants (101 female [66.4%]; mean [SD] age, 46.1 [14.3] years) were studied. These included 56 with SIH and a spinal CSF leak, 16 with orthostatic headache without a CSF leak, 60 control participants, and 20 patients in the validation cohort. Six imaging findings were included in the final scoring system. Three were weighted as major (2 points each): pachymeningeal enhancement, engorgement of venous sinus, and effacement of the suprasellar cistern of 4.0 mm or less. Three were considered minor (1 point each): subdural fluid collection, effacement of the prepontine cistern of 5.0 mm or less, and mamillopontine distance of 6.5 mm or less. Patients were classified into groups at low, intermediate, or high probability of having a spinal CSF leak, with total scores of 2 points or fewer, 3 to 4 points, and 5 points or more, respectively, on a scale of 9 points. The discriminatory ability of the proposed score could be demonstrated in the validation cohort. Conclusions and Relevance This 3-tier predictive scoring system is based on the 6 most relevant brain MRI findings and allows assessment of the likelihood (low, intermediate, or high) of a positive spinal imaging result in patients with SIH. It may be useful in identifying patients with SIH who are leak positive and in whom further invasive myelographic examinations are warranted before considering targeted therapy

Segmentation of Peripheral Nerves From Magnetic Resonance Neurography: A Fully-Automatic, Deep Learning-Based Approach

Diagnosis of peripheral neuropathies relies on neurological examinations, electrodiagnostic studies, and since recently magnetic resonance neurography (MRN). The aim of this study was to develop and evaluate a fully-automatic segmentation method of peripheral nerves of the thigh. T2-weighted sequences without fat suppression acquired on a 3 T MR scanner were retrospectively analyzed in 10 healthy volunteers and 42 patients suffering from clinically and electrophysiologically diagnosed sciatic neuropathy. A fully-convolutional neural network was developed to segment the MRN images into peripheral nerve and background tissues. The performance of the method was compared to manual inter-rater segmentation variability. The proposed method yielded Dice coefficients of 0.859 ± 0.061 and 0.719 ± 0.128, Hausdorff distances of 13.9 ± 26.6 and 12.4 ± 12.1 mm, and volumetric similarities of 0.930 ± 0.054 and 0.897 ± 0.109, for the healthy volunteer and patient cohorts, respectively. The complete segmentation process requires less than one second, which is a significant decrease to manual segmentation with an average duration of 19 ± 8 min. Considering cross-sectional area or signal intensity of the segmented nerves, focal and extended lesions might be detected. Such analyses could be used as biomarker for lesion burden, or serve as volume of interest for further quantitative MRN techniques. We demonstrated that fully-automatic segmentation of healthy and neuropathic sciatic nerves can be performed from standard MRN images with good accuracy and in a clinically feasible time

Rebound After Fingolimod and a Single Daclizumab Injection in a Patient Retrospectively Diagnosed With NMO Spectrum Disorder—MRI Apparent Diffusion Coefficient Maps in Differential Diagnosis of Demyelinating CNS Disorders

Objective: Differential diagnosis of neuromyelitis optica spectrum disorders (NMOSD) and multiple sclerosis (MS) or mimics can be challenging, especially in patients with atypical presentations and negative serostatus for aquaporin-4 antibodies (AQP4-Ab). This brief research report describes magnetic resonance imaging (MRI) findings focusing on quantitative apparent diffusion coefficient (ADC) histogram analysis as a potential tool to differentiate NMOSD from MS.Methods: Longitudinal MRI data obtained during routine clinical examinations were retrospectively analyzed in a patient with histologically determined cerebral NMOSD, a patient with an acute tumefactive MS lesion, and a patient with ischemic stroke. Histogram analyses of ADC maps were evaluated.Results: A patient diagnosed with MS experienced a severe rebound after fingolimod withdrawal and a single daclizumab injection. Cerebral NMOSD manifestation was confirmed by brain biopsy. However, the patient did not fulfill consensus criteria for NMOSD and was AQP4-Ab negative. Comparison of ADC histogram analyses of this patient with those from a patient with MS and one with ischemic stroke revealed differential ADC characteristics: namely a more pronounced and prolonged ADC leftward shift in inflammatory than in ischemic pathology, even more accentuated in NMOSD versus MS.Conclusion: ADC map histograms and ADC threshold values for different conditions may be useful for differentiation of large inflammatory brain lesions and further studies are merited

Prediction of delayed reperfusion in patients with incomplete reperfusion following thrombectomy.

BACKGROUND The clinical course of patients with incomplete reperfusion after thrombectomy, defined as an expanded Thrombolysis in Cerebral Infarction (eTICI) score of 2a-2c, is heterogeneous. Patients showing delayed reperfusion (DR) have good clinical outcomes, almost comparable to patients with ad-hoc TICI3 reperfusion. We aimed to develop and internally validate a model that predicts DR occurrence in order to inform physicians about the likelihood of a benign natural disease progression. PATIENTS AND METHODS Single-center registry analysis including all consecutive, study-eligible patients admitted between 02/2015 and 12/2021. Preliminary variable selection for the prediction of DR was performed using bootstrapped stepwise backward logistic regression. Interval validation was performed with bootstrapping and the final model was developed using a random forests classification algorithm. Model performance metrics are reported with discrimination, calibration, and clinical decision curves. Primary outcome was concordance statistics as a measure of goodness of fit for the occurrence of DR. RESULTS A total of 477 patients (48.8% female, mean age 74 years) were included, of whom 279 (58.5%) showed DR on 24 follow-up. The model's discriminative ability for predicting DR was adequate (C-statistics 0.79 [95% CI: 0.72-0.85]). Variables with strongest association with DR were: atrial fibrillation (aOR 2.06 [95% CI: 1.23-3.49]), Intervention-To-Follow-Up time (aOR 1.06 [95% CI: 1.03-1.10]), eTICI score (aOR 3.49 [95% CI: 2.64-4.73]), and collateral status (aOR 1.33 [95% CI: 1.06-1.68]). At a risk threshold of R = 30%, use of the prediction model could potentially reduce the number of additional attempts in one out of four patients who will have spontaneous DR, without missing any patients who do not show spontaneous DR on follow-up. CONCLUSIONS The model presented here shows fair predictive accuracy for estimating chances of DR after incomplete thrombectomy. This may inform treating physicians on the chances of a favorable natural disease progression if no further reperfusion attempts are made

Association of Intravenous Thrombolysis with Delayed Reperfusion After Incomplete Mechanical Thrombectomy.

PURPOSE Treatment of distal vessel occlusions causing incomplete reperfusion after mechanical thrombectomy (MT) is debated. We hypothesized that pretreatment with intravenous thrombolysis (IVT) may facilitate delayed reperfusion (DR) of residual vessel occlusions causing incomplete reperfusion after MT. METHODS Retrospective analysis of patients with incomplete reperfusion after MT, defined as extended thrombolysis in cerebral infarction (eTICI) 2a-2c, and available perfusion follow-up imaging at 24 ± 12 h after MT. DR was defined as absence of any perfusion deficit on time-sensitive perfusion maps, indicating the absence of any residual occlusion. The association of IVT with the occurrence of DR was evaluated using a logistic regression analysis adjusted for confounders. Sensitivity analyses based on IVT timing (time between IVT start and the occurrence incomplete reperfusion following MT) were performed. RESULTS In 368 included patients (median age 73.7 years, 51.1% female), DR occurred in 225 (61.1%). Atrial fibrillation, higher eTICI grade, better collateral status and longer intervention-to-follow-up time were all associated with DR. IVT did not show an association with the occurrence of DR (aOR 0.80, 95% CI 0.44-1.46, even in time-sensitive strata, aOR 2.28 [95% CI 0.65-9.23] and aOR 1.53 [95% CI 0.52-4.73] for IVT to incomplete reperfusion following MT timing <80 and <100 min, respectively). CONCLUSION A DR occurred in 60% of patients with incomplete MT at ~24 h and did not seem to occur more often in patients receiving pretreatment IVT. Further research on potential associations of IVT and DR after MT is required

Importance of Delayed Reperfusions in Patients With Incomplete Thrombectomy.

BACKGROUND There is paucity of data regarding the effects of delayed reperfusion (DR) on clinical outcomes in patients with incomplete reperfusion following mechanical thrombectomy. We hypothesized that DR has a strong association with clinical outcome in patients with incomplete reperfusion after mechanical thrombectomy (expanded Thrombolysis in Cerebral Infarction, 2a-2c). METHODS Single-institution's stroke registry retrospective analysis of patients admitted from February 2015 to December 2020. DR was defined as the absence of any perfusion delay on ≈24-hour contrast-enhanced follow-up perfusion imaging, whereas persistent perfusion deficit denotes a perfusion delay corresponding to the catheter angiographic deficit directly after the intervention. The association of perfusion outcome (DR versus persistent perfusion deficit) with the occurrence of new infarcts and 90-day functional independence (modified Rankin Scale score 0-2) was evaluated using logistic regression analyses. Comparison of predictive accuracy was evaluated by calculating area under the curve for models with and without perfusion outcome. RESULTS In 566 patients (mean age 74, 49.6% female), new infarcts in the incomplete reperfusion areas were less common in DR versus persistent perfusion deficit patients (small punctiform: 17.1% versus 25%, large confluent: 7.9% versus 63.2%; P=0.001). After adjustment for confounders, DR was a strong predictor of functional independence (adjusted odds ratio, 2.37 [95% CI 1.34-4.23]). There was a significant improvement in predictive accuracy of functional independence when perfusion outcome was added to expanded Thrombolysis in Cerebral Infarction alone (area under the curve 0.57 versus 0.62, P=0.01). CONCLUSIONS Occurrence of DR is closely associated with tissue outcome and functional independence. DR may be an independent prognostic parameter, suggesting it as a potential outcome surrogate for medical rescue therapies