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Updated: Jun 25, 2025
Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
Published on: September 25, 2019
Dynamic Evolution of Infarct Volumes at MRI in Ischemic Stroke Due to Large Vessel Occlusion
Fanny Munsch1, David Planes1, Hikaru Fukutomi1
1From the Institut de Bio-imagerie IBIO (F.M., J.D., V.D., T.T.), University Bordeaux; Neuroimagerie Diagnostique et Thérapeutique (D.P., G.M., T.C., V.D., T.T.), CHU de Bordeaux, France; Kansai Electric Power Hospital (H.F.), Osaka, Japan; Inserm CIC-IT U1433 (E.M., B.C.), CHRU Nancy; Institut de Psychiatrie et Neurosciences de Paris (IPNP) (P.S.), INSERM U1266; Département de Neurologie (P.S.), Hopital Fondation Rothschild, Paris; Institut des Maladies Neurodégénératives (V.P.), CNRS, UMR 5293, Bordeaux INP (P.C.), LABRI, CNRS, UMR5800, and Neurocentre Magendie (V.D., T.T.), INSERM U1215, Univ. Bordeaux; Service de Neurologie et Unité de Neuro Vasculaire (B.L.), Hôpital FOCH, Suresnes; Unité Neurovasculaire (J.M.O.), CHU de Toulouse; Unité Neurovasculaire (I.S.), CHU de Bordeaux; CNRS (M.T.D.S.), UMR-5293, Univ. Bordeaux; and Brain Connectivity and Behaviour Laboratory (M.T.D.S.), Paris, France.
Background And Objectives:
The typical infarct volume trajectories in stroke patients, categorized as slow or fast progressors, remain largely unknown. This study aimed to reveal the characteristic spatiotemporal evolutions of infarct volumes caused by large vessel occlusion (LVO) and show that such growth charts help anticipate clinical outcomes.
Methods:
We conducted a secondary analysis from prospectively collected databases (FRAME, 2017-2019; ETIS, 2015-2022). We selected acute MRI data from anterior LVO stroke patients with witnessed onset, which were divided into training and independent validation datasets. In the training dataset, using Gaussian mixture analysis, we classified the patients into 3 growth groups based on their rate of infarct growth (diffusion volume/time-to-imaging). Subsequently, we extrapolated pseudo-longitudinal models of infarct growth for each group and generated sequential frequency maps to highlight the spatial distribution of infarct growth. We used these charts to attribute a growth group to the independent patients from the validation dataset. We compared their 3-month modified Rankin scale (mRS) with the predicted values based on a multivariable regression model from the training dataset that used growth group as an independent variable.
Results:
We included 804 patients (median age 73.0 years [interquartile range 61.2-82.0 years]; 409 men). The training dataset revealed nonsupervised clustering into 11% (74/703) slow, 62% (437/703) intermediate, and 27% (192/703) fast progressors. Infarct volume evolutions were best fitted with a linear (r = 0.809; p < 0.001), cubic (r = 0.471; p < 0.001), and power (r = 0.63; p < 0.001) function for the slow, intermediate, and fast progressors, respectively. Notably, the deep nuclei and insular cortex were rapidly affected in the intermediate and fast groups with further cortical involvement in the fast group. The variable growth group significantly predicted the 3-month mRS (multivariate odds ratio 0.51; 95% CI 0.37-0.72, p < 0.0001) in the training dataset, yielding a mean area under the receiver operating characteristic curve of 0.78 (95% CI 0.66-0.88) in the independent validation dataset.
Discussion:
We revealed spatiotemporal archetype dynamic evolutions following LVO stroke according to 3 growth phenotypes called slow, intermediate, and fast progressors, providing insight into anticipating clinical outcome. We expect this could help in designing neuroprotective trials aiming at modulating infarct growth before EVT.
