Geometry Quantification for Growth Assessment of Abdominal Aortic Aneurysms under Surveillance.

Document Type

Article

Publication Date

3-9-2026

Publication Title

Science Reports

Abstract

Abdominal Aortic Aneurysms (AAAs) are localized expansions of the abdominal aorta with complex growth patterns, which offer challenges in accurately characterizing aneurysm growth and morphological evolution using traditional maximum diameter [Formula: see text] assessments. Alternative geometric indices and advanced modeling techniques have emerged to address the limitations inherent to diameter-based metrics. The objective of the present work is to elucidate AAA growth dynamics by comparing linear and exponential growth models and examining their correlations with changes in a geometry-driven rupture proxy index (GDRPI), emphasizing additional geometric indices beyond [Formula: see text]. In a cohort of 40 patients, exponential models exhibited strong correlations with multiple geometric indices, with aneurysm volume (V) and thrombus-related indices demonstrating high predictive relevance. Thrombus-related indices strongly correlated with the maximum value of GDRPI, highlighting their potential utility as supplementary surveillance markers. Growth trajectories showed substantial inter-patient variability; aneurysms with mean volumes exceeding 75 [Formula: see text] predominantly followed linear growth, while smaller aneurysms with mean volumes of 50 [Formula: see text] displayed ambiguous growth patterns. Mixed effects techniques significantly improved growth prediction accuracy (mean [Formula: see text] for linear growth models and [Formula: see text] for exponential growth models). Although linear models slightly outperformed exponential models, both effectively described patient-specific AAA progression when used with mixed effects techniques. Integrating thrombus-related indices and mixed effects growth modeling offers a promising pathway for enhanced geometric characterization of AAAs under surveillance, potentially improving longitudinal growth assessment and individual monitoring strategies.

DOI

10.1038/s41598-026-41340-6

ISSN

2045-2322

PubMed ID

41803225

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