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BackgroundIt remains unclear if deep-learning-based white matter lesion (DL-WML) volume can predict outcome after ischemic stroke.PurposeWe aimed to develop, validate, and evaluate DL-WML volume in NCCT as a risk factor and IVT effect modifier compared to the Fazekas scale (WML-Faz) in patients also receiving EVT in an EVT-capable center.MethodsA deep-learning model for WML segmentation in NCCT was developed and validated internally and externally. The volumetric correspondence of DL-WML volume per mL was reported relative to expert annotation with the intraclass correlation coefficient (ICC) and a Bland-Altman analysis reporting bias and limits of agreement (LoA). In a post-hoc analysis of the MR CLEAN No-IV trial, univariable and multivariable regression models were used to report (un)adjusted common odds ratios ([a]cOR) to associate DL-WML volume and WML-Faz with the occurrence of symptomatic-intracerebral hemorrhage (sICH) and 90-day functional outcome with the modified Rankin Scale (mRS).ResultsDL-WML volumes were comparable with those of the ground truth for both the internal test set (10/20(50%) male, age median:72[IQR:67-85], ICC mean:0.91 95%CI:[0.87;0.94];bias:-3mL LoA:[-12mL;7mL]) and the external test set (36/101(36%) male, age median:59[IQR:42-73], ICC mean:0.87 95%CI:[0.71;0.95];bias:-2mL LoA:[-11mL;7mL]). 516 patients from the MR CLEAN No-IV trial (291/516(56%) male, age median:71 IQR:[62-79],) were analyzed. Both DL-WML volume and WML-Faz were associated with sICH (DL-WML volume acOR:1.31 95%CI[1.08;1.60], WML-Faz acOR:1.53 95%CI[1.02;2.31]) and mRS (DL-WML volume acOR:0.84 95%CI[0.76;0.94], WML-Faz acOR:0.73 95%CI[0.60;0.88]). Only for the unadjusted analysis, WML-Faz was an IVT effect modifier (p=0.046), DL-WML was not (p=0.274).ConclusionDL-WML volume and WML-Faz had a similar relationship with functional outcome and sICH.Summary statementDeep-learning-based white matter lesion volume in non-contrast CT can substitute human ratings to prognosticate symptomatic intracerebral hemorrhage and functional outcome at 90-days in acute ischemic stroke patients.Key pointsThis was the first study to show that white matter lesion volume in non-contrast CT based on deep-learning segmentations (DL-WML volume) was associated with symptomatic intracerebral hemorrhages and a worse functional outcome.Compared to the Fazekas scale, regression models using DL-WML volume had a similar fit to the data.White matter lesion load might be associated with more symptomatic intracerebral hemorrhages if IVT was given before EVT.

ObjectiveCognitive and motor impairments are the key clinical manifestations of cerebral small vessel disease (SVD), but their interrelations and combined effects on functional outcome have not been elucidated. We investigated the associations between cognitive and motor functions and their interactions and mediating effects on instrumental activities of daily living (IADL) and quality of life in older individuals with various degrees of white matter hyperintensities (WMH).MethodsParticipants of the Helsinki Small Vessel Disease Study (n=152) were assessed according to an extensive clinical, neuropsychological and MRI protocol. Cognitive composite scores for global cognition, processing speed, executive functions and memory were constructed from multiple tests within each domain. Physical examination included measures of gait speed, balance (single-leg-stance) and functional mobility (timed-up-and-go test). IADL was evaluated with a proxy-based Amsterdam IADL questionnaire and quality of life with a self-report EUROHIS-Qol index. Volumes of WMH and gray matter (GM) were obtained with automated segmentation. Sets of linear regression analyses were used to model the associations between motor and cognitive performances, WMH and GM volumes, and IADL and quality of life.ResultsDomain-specific cognitive and motor functions had strong interrelations with each other, and they were significantly associated with IADL, quality of life as well as WMH and GM volumes. A consistent pattern on significant interactions between cognitive and motor functions was found on IADL, but not on quality of life. In particular, low cognitive scores together with decline in the timed-up-and-go test and gait speed were strongly related to impaired IADL. The association of WMH volume with IADL was mediated by global cognition, whereas the association of GM volume with IADL was mediated by global cognition and timed up-and-go performance.ConclusionThe results highlight the complex interplay and synergism between motor and cognitive abilities on functional outcome in SVD. The combined effect of motor and cognitive disturbances on IADL is likely to be greater than the individual effects of each of the two impairments. WMH and brain atrophy contribute to disability through cognitive and motor impairment.

Purpose Automated analysis of neuroimaging data is commonly based on magnetic resonance imaging (MRI), but sometimes the availability is limited or a patient might have contradictions to MRI. Therefore, automated analyses of computed tomography (CT) images would be beneficial. Methods We developed an automated method to evaluate medial temporal lobe atrophy (MTA), global cortical atrophy (GCA), and the severity of white matter lesions (WMLs) from a CT scan and compared the results to those obtained from MRI in a cohort of 214 subjects gathered from Kuopio and Helsinki University Hospital registers from 2005 - 2016. Results The correlation coefficients of computational measures between CT and MRI were 0.9 (MTA), 0.82 (GCA), and 0.86 (Fazekas). CT-based measures were identical to MRI-based measures in 60% (MTA), 62% (GCA) and 60% (Fazekas) of cases when the measures were rounded to the nearest full grade variable. However, the difference in measures was 1 or less in 97–98% of cases. Similar results were obtained for cortical atrophy ratings, especially in the frontal and temporal lobes, when assessing the brain lobes separately. Bland–Altman plots and weighted kappa values demonstrated high agreement regarding measures based on CT and MRI. Conclusions MTA, GCA, and Fazekas grades can also be assessed reliably from a CT scan with our method. Even though the measures obtained with the different imaging modalities were not identical in a relatively extensive cohort, the differences were minor. This expands the possibility of using this automated analysis method when MRI is inaccessible or contraindicated.

Purpose Severity of white matter lesion (WML) is typically evaluated on magnetic resonance images (MRI), yet the more accessible, faster, and less expensive method is computed tomography (CT). Our objective was to study whether WML can be automatically segmented from CT images using a convolutional neural network (CNN). The second aim was to compare CT segmentation with MRI segmentation. Methods The brain images from the Helsinki University Hospital clinical image archive were systematically screened to make CT-MRI image pairs. Selection criteria for the study were that both CT and MRI images were acquired within 6 weeks. In total, 147 image pairs were included. We used CNN to segment WML from CT images. Training and testing of CNN for CT was performed using 10-fold cross-validation, and the segmentation results were compared with the corresponding segmentations from MRI. Results A Pearson correlation of 0.94 was obtained between the automatic WML volumes of MRI and CT segmentations. The average Dice similarity index validating the overlap between CT and FLAIR segmentations was 0.68 for the Fazekas 3 group. Conclusion CNN-based segmentation of CT images may provide a means to evaluate the severity of WML and establish a link between CT WML patterns and the current standard MRI-based visual rating scale.

Background and purpose: Cerebral small vessel disease is characterized by progressive white matter hyperintensities (WMH) and cognitive decline. However, variability exists in how individuals maintain cognitive capabilities despite significant neuropathology. The relationships between individual cognitive reserve, psychological resilience and cognitive functioning were examined in subjects with varying degrees of WMH. Methods: In the Helsinki Small Vessel Disease Study, 152 subjects (aged 65–75 years) underwent a comprehensive neuropsychological assessment, evaluation of subjective cognitive complaints and brain magnetic resonance imaging with volumetric WMH evaluation. Cognitive reserve was determined by education (years) and the modified Cognitive Reserve Scale (mCRS). Psychological resilience was evaluated with the Resilience Scale 14. Results: The mCRS total score correlated significantly with years of education (r = 0.23, p

Aim The impact of early degenerative changes of the cervical spine on pain in adulthood is unknown. The objective was to determine whether degeneration in adolescence predicts headache or neck pain in young adulthood. Methods As part of a follow-up of schoolchildren with and without headache, 17-year-old adolescents with headache at least three times a month ( N = 47) and adolescents with no headache ( N = 22) participated in a magnetic resonance imaging (MRI) study of the cervical spine. The same adolescents were re-examined by phone interview at the age of 22 years ( N = 60/69, 87%). Results Mild disc degeneration at the age of 17 years was common, but was not associated with either frequent or intensive headache or neck pain at the age of 22 years. Conclusion: Mild degenerative changes of the cervical spine in 17-year-old adolescents cannot be regarded as a cause of future headache or neck pain.