Evaluation of radiogenomics for risk stratification of intracranial aneurysms: a pilot study

TL;DR

This pilot study explores combining imaging and blood-based biomarkers to better assess the risk of intracranial aneurysms.

Contribution

The study introduces a radiogenomics approach integrating radiomics and gene expression data for aneurysm risk stratification.

Findings

12 radiomics features and 97 genes were significantly different between symptomatic and asymptomatic aneurysms.

Radiogenomics features provided better separation of high- and low-risk aneurysms than radiomics alone.

A significant correlation was found between 7 radiomics features and 38 differentially expressed genes.

Abstract

Aneurysm wall enhancement (AWE) is an imaging biomarker that could aid in risk stratification of intracranial aneurysms (IAs) In this pilot study, we explored the potential of a radiogenomics approach by combining blood-based biomarkers and AWE for better risk stratification of IAs.

Patient specific vessel wall imaging scans and whole blood samples were obtained, and IAs were classified as high-risk or low-risk using two different metrics: symptomatic status (3 symptomatic vs. 13 asymptomatic) and PHASES score (4 with a high score vs. 12 with a low score). Radiomics features (RFs) were extracted from the pre- and post-contrast MRI for all IA sac walls, and significantly different RFs were identified through univariate analysis. RNA sequencing from whole blood samples for these patients was also performed to identify differentially expressed genes (DEGs) between high and low-risk IA groups. Principal component analysis (PCA) and clustering analysis were applied, using both risk metrics, to evaluate discriminatory power. Lastly, ontological and correlation analyses were carried out to investigate biological mechanisms associated with the DEGs.

Our analysis of 16 IAs identified 12 RFs and 97 genes that were significantly different between symptomatic and asymptomatic IAs (RF: p-value < 0.05; DEG: fold-change > 2, p-value < 0.01). Examining risk with respect to PHASES score, we identified 6 significant radiomics features and 38 differentially expressed genes. Through principal component analysis and clustering analysis, we found that DEGs only and radiogenomics features produced a better separation between high- and low-risk than RFs alone for both risk metrics. Furthermore, we found a significant correlation between 7 unique RFs and 38 DEGs.

We demonstrated that a radiogenomics approach can help in better risk stratification of IAs.

The online version contains supplementary material available at 10.1007/s00234-025-03702-1.

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