Evidence map›Paper›PMID 40663164›Full record

ArticleNeuroradiology2025

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

Sricharan S Veeturi, Kerry E Poppenberg, Nandor K Pinter, Vinay Jaikumar, Elad I Levy, Adnan H Siddiqui, Vincent M Tutino

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PubMed Full text at PMC Publisher
In one paragraph

Article in Neuroradiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Computational Pipeline in Neuroradiomics.Methods in molecular biology (Clifton, N.J.) · 2026
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Sricharan S VeeturiDepartment of Pathology and Anatomical Sciences, University at Buffalo, State University of New York, Buffalo, USA.
Kerry E PoppenbergCanon Stroke and Vascular Research Center, University at Buffalo, State University of New York, Buffalo, USA.
Nandor K PinterCanon Stroke and Vascular Research Center, University at Buffalo, State University of New York, Buffalo, USA.
Vinay JaikumarCanon Stroke and Vascular Research Center, University at Buffalo, State University of New York, Buffalo, USA.
Elad I LevyCanon Stroke and Vascular Research Center, University at Buffalo, State University of New York, Buffalo, USA.
Adnan H SiddiquiCanon Stroke and Vascular Research Center, University at Buffalo, State University of New York, Buffalo, USA.
Vincent M TutinoDepartment of Pathology and Anatomical Sciences, University at Buffalo, State University of New York, Buffalo, USA. vincentt@buffalo.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeAneurysm 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.

methodsPatient 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.

resultsOur 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.

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

Indexed as

GenomicsIntracranial AneurysmMagnetic Resonance ImagingAdultAgedBiomarkersContrast MediaFemaleHumansMaleMiddle AgedPilot ProjectsRisk AssessmentBiomarkersContrast MediaAneurysm risk stratificationAneurysm wall enhancementBlood transcriptomicsIntracranial aneurysmsRadiogenomics

Identifiers

PMID40663164
PMCPMC12546524

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.