From Anatomy to Aneurysm: Morphological-Hemodynamic Coupling and Predictive Modeling in Aberrant Splenic Artery
Year: 2026
Type: preprint
Study design: Observational Study
Abstract: Background Aberrant splenic artery (SA) is an extremely rare anatomical variant in which the SA arises from the superior mesenteric artery (SMA) rather than the celiac axis. Individuals with this anatomical variant are at markedly increased risk of developing SA aneurysms (SAAs). However, no systematic investigation into its underlying pathogenesis has been conducted to date, nor has a pathogenesis-based predictive model been developed to assess future aneurysm risk in this population. Objectives This study aimed to systematically investigate the morphological and hemodynamic mechanisms by which aberrant SA anatomy predisposes to local SAA formation, and to develop an interpretable predictive model for individualized risk stratification based on these mechanisms. Methods This multicenter retrospective cohort study enrolled 195 patients with aberrant SA from 16 centers across China between January 2008 and June 2026, including 94 with concomitant aberrant SAAs and 101 without. 3D vascular models were reconstructed from CTA images, and 12 morphological parameters were measured. Computational fluid dynamics simulations were performed to analyze 14 hemodynamic parameters. Ultimately, 5 morphological parameters were selected to construct a two-stage interpretable machine learning model for predicting the occurrence and location of SAAs. Results Morphologically, aberrant SAAs were significantly larger (diameter: 20.4mm vs. 17.5mm, P <0.001; length: 20.5mm vs. 17.9mm, P =0.002) and predominantly located in the proximal SA, whereas common SAAs were mainly located in the distal splenic hilum. Furthermore, the aberrant SA exhibited a smaller branching angle with its parent vessel (22.3° vs. 19.7°, P =0.010), an increased SA-to-SMA cross-sectional area ratio (0.57 vs. 0.47, P =0.008), and a higher tortuosity index ( P <0.001). Hemodynamic analysis demonstrated significantly lower TAWSS at the proximal SA in aberrant anatomy ( P <0.001), whereas the OSI at the SA-parent vessel junction was significantly elevated ( P =0.004) and inversely correlated with the branching angle ( P <0.001, r=-0.686). The interpretable machine learning model, built on morphological and hemodynamic principles, achieved an AUROC of 0.828 for predicting aberrant SAA occurrence and was deployed as an interactive web application (the zs_abeSA model) for clinical use. Conclusions Aberrant SA anatomy is an independent risk factor for SAA formation. The pathogenic mechanism involves a cascade from morphological remodeling to hemodynamic derangement, ultimately leading to aneurysm formation at the proximal SA. The zs_abeSA model provides a practical tool for individualized risk assessment, with direct implications for early screening and surveillance strategy development.
Source: medRxiv
Authors: Ran Xu, Shouji Qiu, Tianyue Pan, Zhaohui Hua, Ding Yuan, Xiaolong Wei, Meng Ye, Yaoguo Yang, Minyi Yin, Ye Tian, Qingbo Fang, Youfei Qi, Donghua Ji, Hongping Deng, Haotian Chi, Yuan Feng, Haozhe Qi, Tiehao Wang, Li Chen, Mojia Hu, Wenzhen Yang, Yuqi Yi, Ruchen Li, Tao Lyu, Wei Wang, Donglin Li, Lixin Wang
Institutions: Fudan University, Zhongshan Hospital, Zhengzhou University, First Affiliated Hospital of Zhengzhou University, Sichuan University, West China Hospital of Sichuan University, Changhai Hospital, Shanghai Jiao Tong University, Renji Hospital, Capital Medical University, Beijing Anzhen Hospital, Shanghai Ninth People's Hospital, Xinjiang Medical University, First Affiliated Hospital of Xinjiang Medical University, People's Hospital of Xinjiang Uygur Autonomous Region, Hainan General Hospital, Hainan Medical University, First Affiliated Hospital of Dalian Medical University, Renmin Hospital of Wuhan University, Central South University, Xiangya Hospital Central South University, Shandong University, Shandong Provincial Hospital, Shanghai Chest Hospital, First Affiliated Hospital Zhejiang University, Zhejiang University
Language: English
Subfield: Surgery
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7212271592