Unsupervised discovery of ischemic stroke phenotypes from multimodal MRI radiomics

Objective.This study presents a fully unsupervised and label-independent radiomic pipeline designed to group different types of ischemic stroke lesions using multimodal Magnetic Resonance Imaging (MRI) . The aim is to address lesion heterogeneity and the absence of annotated outcomes, particularly in settings with limited resources.Approach. Three MRI sequences were analyzed: Fluid Attenuated Inversion Recovery (FLAIR), Apparent Diffusion Coefficient (ADC), and Susceptibility Weighted Imaging (SWI). Lesion identification was performed using percentile-based thresholds, and feature selection was guided by variance filtering with a minimum threshold of 0.001. The complexity of the data was reduced using Uniform Manifold Approximation and Projection (UMAP). Grouping of the lesions was conducted using K-means++, agglomerative hierarchical clustering with Ward linkage, and spectral clustering with a nearest neighbour affinity matrix. The quality and stability of the identified clusters were rigorously evaluated using established internal validation metrics. Feature significance was determined using Kruskal-Wallis testing with Bonferroni correction.Main results. The combination of UMAP with Agglomerative clustering produced the highest silhouette scores of 0.784 for three clusters and 0.778 for five clusters. Consensus stability was optimal, with a Proportion of Ambiguous Clustering score (PAC) of 0.000. Kruskal-Wallis analysis identified 25 significant features for the three-cluster solution and 36 for the five-cluster solution. The most discriminative features originated from ADC and SWI sequences. The five-cluster model revealed finer phenotypic separation and identified five borderline cases with low silhouette coefficients, indicating transitional lesion patterns.Significance. This unsupervised framework enables biologically meaningful lesion stratification without reliance on manual segmentation or outcome labels. It offers a scalable solution for deployment in low-resource environments and provides a robust foundation for future diagnostic and prognostic modelling in stroke imaging.