Radiomics-Based Predictive Nomogram for Assessing the Risk of Intracranial Aneurysms

Sricharan S Veeturi; Arshaq Saleem; Diego Ojeda; Elena Sagues; Sebastian Sanchez; Andres Gudino; Elad I Levy; David Hasan; Adnan H Siddiqui; Vincent M Tutino; Edgar A Samaniego

doi:10.21203/rs.3.rs-4350156/v1

Back

Radiomics-Based Predictive Nomogram for Assessing the Risk of Intracranial Aneurysms

Preprint

Open access

Radiomics-Based Predictive Nomogram for Assessing the Risk of Intracranial Aneurysms

Sricharan S Veeturi, Arshaq Saleem, Diego Ojeda, Elena Sagues, Sebastian Sanchez, Andres Gudino, Elad I Levy, David Hasan, Adnan H Siddiqui, Vincent M Tutino, …

Research square

05/10/2024

DOI: 10.21203/rs.3.rs-4350156/v1

PMCID: PMC11100888

PMID: 38766264

Files and links (1)

url

https://doi.org/10.21203/rs.3.rs-4350156/v1View

Preprint (Author's original)This preprint has not been evaluated by subject experts through peer review. Preprints may undergo extensive changes and/or become peer-reviewed journal articles. Open Access

Abstract

Background: Aneurysm wall enhancement (AWE) has the potential to be used as an imaging biomarker for the risk stratification of intracranial aneurysms (IAs). Radiomics provides a refined approach to quantify and further characterize AWE's textural features. This study examines the performance of AWE quantification combined with clinical information in detecting symptomatic IAs. Methods: Ninety patients harboring 104 IAs (29 symptomatic and 75 asymptomatic) underwent high-resolution magnetic resonance imaging (HR-MRI). The assessment of AWE was performed using two different methods: 3D-AWE mapping and composite radiomics-based score (RadScore). The dataset was split into training and testing subsets. The testing set was used to build two different nomograms using each modality of AWE assessment combined with patients' demographic information and aneurysm morphological data. Finally, each nomogram was evaluated on an independent testing set. Results: A total of 22 radiomic features were significantly different between symptomatic and asymptomatic IAs. The 3D-AWE Mapping nomogram achieved an area under the curve (AUC) of 0.77 (63% accuracy, 78% sensitivity and 58% specificity). The RadScore nomogram exhibited a better performance, achieving an AUC of 0.83 (77% accuracy, 89% sensitivity and 73% specificity). Conclusions : Combining AWE quantification through radiomic analysis with patient demographic data in a clinical nomogram achieved high accuracy in detecting symptomatic IAs.

Details

Title: Subtitle: Radiomics-Based Predictive Nomogram for Assessing the Risk of Intracranial Aneurysms
Creators: Sricharan S Veeturi - University at Buffalo, State University of New York
Arshaq Saleem
Diego Ojeda - University of Iowa
Elena Sagues - University of Iowa
Sebastian Sanchez - Yale University
Andres Gudino - University of Iowa
Elad I Levy - University at Buffalo, State University of New York
David Hasan - Duke University
Adnan H Siddiqui - University at Buffalo, State University of New York
Vincent M Tutino - University at Buffalo, State University of New York
Edgar A Samaniego - University of Iowa
Resource Type: Preprint
Publication Details: Research square
DOI: 10.21203/rs.3.rs-4350156/v1
PMID: 38766264
PMCID: PMC11100888
Language: English
Date posted: 05/10/2024
Academic Unit: Neurology; Radiology; Iowa Neuroscience Institute; Neurosurgery
Record Identifier: 9984628050702771

Metrics

16 Record Views