MRI radiomic signature of white matter hyperintensities is associated with clinical phenotypes

Objective: Neuroimaging measurements of brain structural integrity are thought to be surrogates for brain health, but precise assessments require dedicated advanced image acquisitions. By means of quantitatively describing conventional images, radiomic analyses hold potential for evaluating brain health. We sought to: (1) evaluate radiomics to assess brain structural integrity by predicting white matter hyperintensities burdens (WMH) and (2) uncover associations between predictive radiomic features and clinical phenotypes. Methods: We analyzed a multi-site cohort of 4,163 acute ischemic strokes (AIS) patients with T2-FLAIR MR images with total brain and WMH segmentations. Radiomic features were extracted from normal-appearing brain tissue (brain mask-WMH mask). Radiomics-based prediction of personalized WMH burden was done using ElasticNet linear regression. We built a radiomic signature of WMH with stable selected features predictive of WMH burden and then related this signature to clinical variables using canonical correlation analysis (CCA). Results: Radiomic features were predictive of WMH burden ( Conclusion: Radiomics extracted from T2-FLAIR images of AIS patients capture microstructural damage of the cerebral parenchyma and correlate with clinical phenotypes, suggesting different radiographical textural abnormalities per cardiovascular risk profile. Further research could evaluate radiomics to predict the progression of WMH and for the follow-up of stroke patients\u27 brain health

Deep profiling of multiple ischemic lesions in a large, multi-center cohort : Frequency, spatial distribution, and associations to clinical characteristics

Background purposeA substantial number of patients with acute ischemic stroke (AIS) experience multiple acute lesions (MAL). We here aimed to scrutinize MAL in a large radiologically deep-phenotyped cohort. Materials and methodsAnalyses relied upon imaging and clinical data from the international MRI-GENIE study. Imaging data comprised both Fluid-attenuated inversion recovery (FLAIR) for white matter hyperintensity (WMH) burden estimation and diffusion-weighted imaging (DWI) sequences for the assessment of acute stroke lesions. The initial step featured the systematic evaluation of occurrences of MAL within one and several vascular supply territories. Associations between MAL and important imaging and clinical characteristics were subsequently determined. The interaction effect between single and multiple lesion status and lesion volume was estimated by means of Bayesian hierarchical regression modeling for both stroke severity and functional outcome. ResultsWe analyzed 2,466 patients (age = 63.4 +/- 14.8, 39% women), 49.7% of which presented with a single lesion. Another 37.4% experienced MAL in a single vascular territory, while 12.9% featured lesions in multiple vascular territories. Within most territories, MAL occurred as frequently as single lesions (ratio similar to 1:1). Only the brainstem region comprised fewer patients with MAL (ratio 1:4). Patients with MAL presented with a significantly higher lesion volume and acute NIHSS (7.7 vs. 1.7 ml and 4 vs. 3, p(FDR) < 0.001). In contrast, patients with a single lesion were characterized by a significantly higher WMH burden (6.1 vs. 5.3 ml, p(FDR) = 0.048). Functional outcome did not differ significantly between patients with single versus multiple lesions. Bayesian analyses suggested that the association between lesion volume and stroke severity between single and multiple lesions was the same in case of anterior circulation stroke. In case of posterior circulation stroke, lesion volume was linked to a higher NIHSS only among those with MAL. ConclusionMultiple lesions, especially those within one vascular territory, occurred more frequently than previously reported. Overall, multiple lesions were distinctly linked to a higher acute stroke severity, a higher total DWI lesion volume and a lower WMH lesion volume. In posterior circulation stroke, lesion volume was linked to a higher stroke severity in multiple lesions only.Peer reviewe

Sex-specific lesion pattern of functional outcomes after stroke

Relying on neuroimaging and clinical data of 822 acute stroke patients, Bonkhoff et al. report substantially more detrimental effects of lesions in left-hemispheric posterior circulation regions on functional outcomes in women compared to men. These findings may motivate a sex-specific clinical stroke management to improve outcomes in the longer term. Stroke represents a considerable burden of disease for both men and women. However, a growing body of literature suggests clinically relevant sex differences in the underlying causes, presentations and outcomes of acute ischaemic stroke. In a recent study, we reported sex divergences in lesion topographies: specific to women, acute stroke severity was linked to lesions in the left-hemispheric posterior circulation. We here determined whether these sex-specific brain manifestations also affect long-term outcomes. We relied on 822 acute ischaemic patients [age: 64.7 (15.0) years, 39% women] originating from the multi-centre MRI-GENIE study to model unfavourable outcomes (modified Rankin Scale >2) based on acute neuroimaging data in a Bayesian hierarchical framework. Lesions encompassing bilateral subcortical nuclei and left-lateralized regions in proximity to the insula explained outcomes across men and women (area under the curve = 0.81). A pattern of left-hemispheric posterior circulation brain regions, combining left hippocampus, precuneus, fusiform and lingual gyrus, occipital pole and latero-occipital cortex, showed a substantially higher relevance in explaining functional outcomes in women compared to men [mean difference of Bayesian posterior distributions (men - women) = -0.295 (90% highest posterior density interval = -0.556 to -0.068)]. Once validated in prospective studies, our findings may motivate a sex-specific approach to clinical stroke management and hold the promise of enhancing outcomes on a population level.Peer reviewe

MRI Radiomic Signature of White Matter Hyperintensities Is Associated With Clinical Phenotypes

Objective: Neuroimaging measurements of brain structural integrity are thought to be surrogates for brain health, but precise assessments require dedicated advanced image acquisitions. By means of quantitatively describing conventional images, radiomic analyses hold potential for evaluating brain health. We sought to: (1) evaluate radiomics to assess brain structural integrity by predicting white matter hyperintensities burdens (WMH) and (2) uncover associations between predictive radiomic features and clinical phenotypes. Methods: We analyzed a multi-site cohort of 4,163 acute ischemic strokes (AIS) patients with T2-FLAIR MR images with total brain and WMH segmentations. Radiomic features were extracted from normal-appearing brain tissue (brain mask-WMH mask). Radiomics-based prediction of personalized WMH burden was done using ElasticNet linear regression. We built a radiomic signature of WMH with stable selected features predictive of WMH burden and then related this signature to clinical variables using canonical correlation analysis (CCA). Results: Radiomic features were predictive of WMH burden (R-2 = 0.855 +/- 0.011). Seven pairs of canonical variates (CV) significantly correlated the radiomics signature of WMH and clinical traits with respective canonical correlations of 0.81, 0.65, 0.42, 0.24, 0.20, 0.15, and 0.15 (FDR-corrected p-values(CV1-6) < 0.001, p-value(CV7) = 0.012). The clinical CV1 was mainly influenced by age, CV2 by sex, CV3 by history of smoking and diabetes, CV4 by hypertension, CV5 by atrial fibrillation (AF) and diabetes, CV6 by coronary artery disease (CAD), and CV7 by CAD and diabetes. Conclusion: Radiomics extracted from T2-FLAIR images of AIS patients capture microstructural damage of the cerebral parenchyma and correlate with clinical phenotypes, suggesting different radiographical textural abnormalities per cardiovascular risk profile. Further research could evaluate radiomics to predict the progression of WMH and for the follow-up of stroke patients' brain health.Peer reviewe

The relevance of rich club regions for functional outcome post-stroke is enhanced in women

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 similar to 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 dub regions were associated with increased odds of unfavorable outcome. These effects were spatially specific and more pronounced in women.Peer reviewe

Caractérisation radiomics en IRM de l'infarctus et de la santé cérébrale des patients victimes d'AVC ischémique

Ischemic stroke (IS) is a major cause of disability and mortality worldwide and is therefore a global public health issue. Its prognosis depends on various factors but mainly on the extent of the ischemic injury and the condition of the underlying brain. While MRI can be used to characterize these aspects, its interpretation is subjective, which can impact treatment decisions. Therefore, there is a need to develop objective and quantitative imaging biomarkers for ischemic injury and brain health to improve the diagnosis, prognosis, and treatment of stroke. Radiomics, which involves the automatic extraction of texture parameters from medical imaging, can provide these biomarkers by quantitatively describing MRI images. In this thesis, we evaluated the performance of radiomics in characterizing infarcts and brain health in patients with ischemic stroke.Our first study aimed to develop a biomarker to quantify the DWI-FLAIR mismatch in the MRI characterization of IS. In cases where the onset or presentation of IS is unknown or delayed, treatment is only indicated in patients without FLAIR intra-lesional hyperintensity. We conducted a radiomics analysis of infarcts in 103 patients using FLAIR and diffusion imaging and predicted the consensual visual interpretation of FLAIR lesion signal by two experts using a machine learning algorithm. Although the inter-observer agreement was modest (Cohen's Κ=.58), we identified two radiomics variables (FLAIR kurtosis and Cluster Shade) that were predictive of FLAIR lesion positivity. This radiomics signature represents an innovative potential therapeutic biomarker for the management of patients with IS.Our second study allowed us to evaluate the radiomics approach in quantifying the cerebral burden of disease. Using an artificial intelligence algorithm on 4163 patients from an international multicenter cohort of patients with IS, we showed that T2-FLAIR radiomics of healthy-looking parenchyma predicted the volume of white matter hyperintensities (R2=0.855±0.011). Thus, radiomics analysis of T2-FLAIR imaging can identify brain alterations beyond those visible on morphological sequences. Additionally, we suggested that certain cardiovascular risk profiles had specific textural expressions. Finally, age was the clinical trait best represented by radiomics, which allowed us to conceptualize our next article.In our third study, we investigated brain age as a biomarker of brain health in patients with IS. Using the same cohort of 4163 patients with a machine learning model, we predicted the chronological age of patients from T2-FLAIR radiomics. Then, we studied the difference between the predicted age, called brain age, and chronological age and derived an age-independent biomarker: relative brain age (RBA). We showed that patients with brains that appeared older had significantly more cardiovascular risk factors (hypertension, diabetes mellitus, smoking, history of stroke). Finally, we demonstrated that RBA was independently associated with poststroke functional prognosis, even after adjusting for chronological age, NIHSS, and history of stroke (respective adjusted odds ratios: 0.76, 0.58, 0.48, 0.55; all p-values<.001).In conclusion, this thesis provides examples of the joint application of radiomics and artificial intelligence in characterizing ischemic lesions and brain health in patients with ischemic stroke. Our results suggest that these innovative techniques could help guide the management of these patients.L'AVC ischémique (AVCi) est une cause majeure de handicap et de mortalité dans le monde et est ainsi un problème global de santé publique. Son pronostic est multifactoriel mais dépend principalement de la lésion ischémique et de l'état du cerveau sous-jacent. Leur caractérisation peut être réalisée en IRM mais son interprétation reste subjective et cette variabilité peut impacter le soin. Il est donc nécessaire de développer des biomarqueurs d'imagerie objectifs et quantitatifs de la lésion ischémique et de la santé cérébrale afin d'améliorer le diagnostic, le pronostic, et le traitement de l'AVCi. Les radiomics, l'extraction automatique de paramètres texturaux à partir d'imagerie médicale, peut fournir ces biomarqueurs, en décrivant quantitativement l'image IRM. Durant ma thèse, nous avons donc évalué les performances des radiomics pour caractériser l'infarctus et la santé cérébrale des patients victimes d'AVC ischémique.L'objectif de notre premier travail a été de développer un biomarqueur pour quantifier la discordance DWI-FLAIR dans la caractérisation IRM des AVCi. En effet, dans les AVCi de début inconnu ou de présentation tardive, le traitement n'est indiqué que chez les patients ne présentant pas d'hypersignal FLAIR intra-lésionnel. Nous avons conduit une analyse radiomics des infarctus de 103 patients en FLAIR et en diffusion et avons prédit, grâce à un algorithme d'apprentissage machine, l'interprétation visuelle par deux experts du signal FLAIR lésionnel. Alors que l'accord inter-observateur était modeste (Cohen Κ=0.58), nous avons identifié deux variables radiomics (FLAIR kurtosis et Cluster Shade) prédictives de la positivité lésionnelle en FLAIR. Cette signature radiomics représente un potentiel biomarqueur thérapeutique innovant pour le soin des patients victimes d'AVCi.Notre second travail nous a permis d'évaluer l'approche radiomics dans la quantification de la charge lésionnelle neurovasculaire. A partir de 4163 patients issus d'une cohorte multicentrique internationale de patients victimes d'AVCi, et grâce à un algorithme d'intelligence artificielle, nous avons montré que les radiomics T2-FLAIR du parenchyme d'allure saine étaient prédictifs du volume de leucopathie (R2=0.855±0.011). Ainsi, l'analyse radiomics des imageries T2-FLAIR permet d'identifier des altérations cérébrales au-delà des anomalies visibles sur les séquences morphologiques. De plus, nous avons suggéré que certains profils de risques cardiovasculaires avaient une expression texturale spécifique. Enfin, l'âge était particulièrement bien capturé par les radiomics ce qui nous a permis de conceptualiser notre article suivant.Durant notre troisième travail, nous avons étudié l'âge cérébral comme biomarqueur de santé cérébrale chez les patients victimes d'AVCi. En exploitant la même cohorte de 4163 patients avec un modèle d'apprentissage machine, nous avons prédit l'âge chronologique des patients à partir des radiomics T2-FLAIR. Puis, nous avons étudié la différence entre l'âge prédit, appelé âge cérébral, et l'âge chronologique et avons dérivé une variable indépendante de l'âge : l'âge cérébral relatif. Nous avons montré que les patients dont le cerveau avait l'air plus âgé avaient significativement plus de facteurs de risques cardiovasculaires (hypertension, diabète, tabagisme, antécédent d'AVC). Enfin, nous avons montré que l'âge cérébral relatif était associé au pronostic fonctionnel de l'AVC de manière indépendante à l'âge chronologique, au NIHSS, et aux antécédents d'AVC (Odds-ratios ajustés : 0.76, 0.58, 0.48, 0.55; p-values<0.001).En conclusion, ma thèse montre des exemples applicatifs des radiomics et de l'intelligence artificielle dans la caractérisation des lésions ischémiques et de la santé cérébrale chez des patients victimes d'AVC ischémiques. Nos résultats suggèrent que ces techniques innovantes pourraient aider à orienter le soin de ces patients

MRI radiomics characterization of cerebral infarcts and brain health in ischemic stroke patients

L'AVC ischémique (AVCi) est une cause majeure de handicap et de mortalité dans le monde et est ainsi un problème global de santé publique. Son pronostic est multifactoriel mais dépend principalement de la lésion ischémique et de l'état du cerveau sous-jacent. Leur caractérisation peut être réalisée en IRM mais son interprétation reste subjective et cette variabilité peut impacter le soin. Il est donc nécessaire de développer des biomarqueurs d'imagerie objectifs et quantitatifs de la lésion ischémique et de la santé cérébrale afin d'améliorer le diagnostic, le pronostic, et le traitement de l'AVCi. Les radiomics, l'extraction automatique de paramètres texturaux à partir d'imagerie médicale, peut fournir ces biomarqueurs, en décrivant quantitativement l'image IRM. Durant ma thèse, nous avons donc évalué les performances des radiomics pour caractériser l'infarctus et la santé cérébrale des patients victimes d'AVC ischémique.L'objectif de notre premier travail a été de développer un biomarqueur pour quantifier la discordance DWI-FLAIR dans la caractérisation IRM des AVCi. En effet, dans les AVCi de début inconnu ou de présentation tardive, le traitement n'est indiqué que chez les patients ne présentant pas d'hypersignal FLAIR intra-lésionnel. Nous avons conduit une analyse radiomics des infarctus de 103 patients en FLAIR et en diffusion et avons prédit, grâce à un algorithme d'apprentissage machine, l'interprétation visuelle par deux experts du signal FLAIR lésionnel. Alors que l'accord inter-observateur était modeste (Cohen Κ=0.58), nous avons identifié deux variables radiomics (FLAIR kurtosis et Cluster Shade) prédictives de la positivité lésionnelle en FLAIR. Cette signature radiomics représente un potentiel biomarqueur thérapeutique innovant pour le soin des patients victimes d'AVCi.Notre second travail nous a permis d'évaluer l'approche radiomics dans la quantification de la charge lésionnelle neurovasculaire. A partir de 4163 patients issus d'une cohorte multicentrique internationale de patients victimes d'AVCi, et grâce à un algorithme d'intelligence artificielle, nous avons montré que les radiomics T2-FLAIR du parenchyme d'allure saine étaient prédictifs du volume de leucopathie (R2=0.855±0.011). Ainsi, l'analyse radiomics des imageries T2-FLAIR permet d'identifier des altérations cérébrales au-delà des anomalies visibles sur les séquences morphologiques. De plus, nous avons suggéré que certains profils de risques cardiovasculaires avaient une expression texturale spécifique. Enfin, l'âge était particulièrement bien capturé par les radiomics ce qui nous a permis de conceptualiser notre article suivant.Durant notre troisième travail, nous avons étudié l'âge cérébral comme biomarqueur de santé cérébrale chez les patients victimes d'AVCi. En exploitant la même cohorte de 4163 patients avec un modèle d'apprentissage machine, nous avons prédit l'âge chronologique des patients à partir des radiomics T2-FLAIR. Puis, nous avons étudié la différence entre l'âge prédit, appelé âge cérébral, et l'âge chronologique et avons dérivé une variable indépendante de l'âge : l'âge cérébral relatif. Nous avons montré que les patients dont le cerveau avait l'air plus âgé avaient significativement plus de facteurs de risques cardiovasculaires (hypertension, diabète, tabagisme, antécédent d'AVC). Enfin, nous avons montré que l'âge cérébral relatif était associé au pronostic fonctionnel de l'AVC de manière indépendante à l'âge chronologique, au NIHSS, et aux antécédents d'AVC (Odds-ratios ajustés : 0.76, 0.58, 0.48, 0.55; p-values<0.001).En conclusion, ma thèse montre des exemples applicatifs des radiomics et de l'intelligence artificielle dans la caractérisation des lésions ischémiques et de la santé cérébrale chez des patients victimes d'AVC ischémiques. Nos résultats suggèrent que ces techniques innovantes pourraient aider à orienter le soin de ces patients.Ischemic stroke (IS) is a major cause of disability and mortality worldwide and is therefore a global public health issue. Its prognosis depends on various factors but mainly on the extent of the ischemic injury and the condition of the underlying brain. While MRI can be used to characterize these aspects, its interpretation is subjective, which can impact treatment decisions. Therefore, there is a need to develop objective and quantitative imaging biomarkers for ischemic injury and brain health to improve the diagnosis, prognosis, and treatment of stroke. Radiomics, which involves the automatic extraction of texture parameters from medical imaging, can provide these biomarkers by quantitatively describing MRI images. In this thesis, we evaluated the performance of radiomics in characterizing infarcts and brain health in patients with ischemic stroke.Our first study aimed to develop a biomarker to quantify the DWI-FLAIR mismatch in the MRI characterization of IS. In cases where the onset or presentation of IS is unknown or delayed, treatment is only indicated in patients without FLAIR intra-lesional hyperintensity. We conducted a radiomics analysis of infarcts in 103 patients using FLAIR and diffusion imaging and predicted the consensual visual interpretation of FLAIR lesion signal by two experts using a machine learning algorithm. Although the inter-observer agreement was modest (Cohen's Κ=.58), we identified two radiomics variables (FLAIR kurtosis and Cluster Shade) that were predictive of FLAIR lesion positivity. This radiomics signature represents an innovative potential therapeutic biomarker for the management of patients with IS.Our second study allowed us to evaluate the radiomics approach in quantifying the cerebral burden of disease. Using an artificial intelligence algorithm on 4163 patients from an international multicenter cohort of patients with IS, we showed that T2-FLAIR radiomics of healthy-looking parenchyma predicted the volume of white matter hyperintensities (R2=0.855±0.011). Thus, radiomics analysis of T2-FLAIR imaging can identify brain alterations beyond those visible on morphological sequences. Additionally, we suggested that certain cardiovascular risk profiles had specific textural expressions. Finally, age was the clinical trait best represented by radiomics, which allowed us to conceptualize our next article.In our third study, we investigated brain age as a biomarker of brain health in patients with IS. Using the same cohort of 4163 patients with a machine learning model, we predicted the chronological age of patients from T2-FLAIR radiomics. Then, we studied the difference between the predicted age, called brain age, and chronological age and derived an age-independent biomarker: relative brain age (RBA). We showed that patients with brains that appeared older had significantly more cardiovascular risk factors (hypertension, diabetes mellitus, smoking, history of stroke). Finally, we demonstrated that RBA was independently associated with poststroke functional prognosis, even after adjusting for chronological age, NIHSS, and history of stroke (respective adjusted odds ratios: 0.76, 0.58, 0.48, 0.55; all p-values<.001).In conclusion, this thesis provides examples of the joint application of radiomics and artificial intelligence in characterizing ischemic lesions and brain health in patients with ischemic stroke. Our results suggest that these innovative techniques could help guide the management of these patients

MRI radiomic signature of white matter hyperintensities is associated with clinical phenotypes

Objective: Neuroimaging measurements of brain structural integrity are thought to be surrogates for brain health, but precise assessments require dedicated advanced image acquisitions. By means of quantitatively describing conventional images, radiomic analyses hold potential for evaluating brain health. We sought to: (1) evaluate radiomics to assess brain structural integrity by predicting white matter hyperintensities burdens (WMH) and (2) uncover associations between predictive radiomic features and clinical phenotypes. Methods: We analyzed a multi-site cohort of 4,163 acute ischemic strokes (AIS) patients with T2-FLAIR MR images with total brain and WMH segmentations. Radiomic features were extracted from normal-appearing brain tissue (brain mask-WMH mask). Radiomics-based prediction of personalized WMH burden was done using ElasticNet linear regression. We built a radiomic signature of WMH with stable selected features predictive of WMH burden and then related this signature to clinical variables using canonical correlation analysis (CCA). Results: Radiomic features were predictive of WMH burden (R 2 = 0.855 ± 0.011). Seven pairs of canonical variates (CV) significantly correlated the radiomics signature of WMH and clinical traits with respective canonical correlations of 0.81, 0.65, 0.42, 0.24, 0.20, 0.15, and 0.15 (FDR-corrected p-values CV 1 - 6 < 0.001, p-value CV 7 = 0.012). The clinical CV1 was mainly influenced by age, CV2 by sex, CV3 by history of smoking and diabetes, CV4 by hypertension, CV5 by atrial fibrillation (AF) and diabetes, CV6 by coronary artery disease (CAD), and CV7 by CAD and diabetes. Conclusion: Radiomics extracted from T2-FLAIR images of AIS patients capture microstructural damage of the cerebral parenchyma and correlate with clinical phenotypes, suggesting different radiographical textural abnormalities per cardiovascular risk profile. Further research could evaluate radiomics to predict the progression of WMH and for the follow-up of stroke patients' brain health

Impact of prodromal symptoms on the prognosis of patients with basilar artery occlusion treated with mechanical thrombectomy

International audienceIntroduction:Even with reperfusion therapies, the prognosis of patients with basilar artery occlusion (BAO) related stroke remains poor. We aimed to test the hypothesis that the presence of prodromal symptoms, an easily available anamnestic data, is a key determinant of poor functional outcome.Patients and methods:Data from patients with BAO treated in Lille, France, with mechanical thrombectomy (MT) between 2015 and 2021 were prospectively collected. The presence of prodromal symptoms was defined by previous transient neurological deficit or gradual progressive clinical worsening preceding a secondary sudden clinical worsening. We compared the characteristics of patients with and without prodromal symptoms. We built multivariate logistic regression models to study the association between the presence of prodromal symptoms and functional (mRS 0–3 and mortality), and procedural (successful recanalization and early reocclusion) outcomes.Results:Among the 180 patients, 63 (35%) had prodromal symptoms, most frequently a vertigo. Large artery atherosclerosis was the predominant cause of stroke (41.3%). The presence of prodromal symptoms was an independent predictor of worse 90-day functional outcome (mRS 0–3: 25.4% vs 47.0%, odds ratio (OR) 0.39; 95% confidence interval (CI) 0.16–0.86) and 90-day mortality (OR 2.17; 95% CI 1.02–4.65). Despite similar successful recanalization rate, the proportion of early basilar artery reocclusion was higher in patients with prodromal symptoms (23.8% vs 5.6%, p = 0.002).Discussion and conclusion:More than one third of BAO patients treated with MT had prodromal symptoms, especially patients with large-artery atherosclerosis. Clinicians should systematically screen for prodromal symptoms given the poor related functional outcome and increased risk of early basilar artery reocclusion