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已发表论文
血管周围脂肪组织影像组学预测腹主动脉瘤破裂：一项多中心研究
Authors Feng Y , Wu M, An H, Geng Y, Zhang H, Lu J, Wu X, Xu L, Yang Y
Received 25 August 2025
Accepted for publication 25 December 2025
Published 8 January 2026 Volume 2026:22 559381
DOI https://doi.org/10.2147/VHRM.S559381
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Akash Batta
Yuan Feng,1 Mengchao Wu,1 Hongfei An,1 Yihe Geng,1 Hanfang Zhang,1 Jinghan Lu,1 Xuejun Wu,2 Lei Xu,3 Yaoguo Yang1
1Department of Vascular Surgery, Beijing Anzhen Hospital, Capital Medical University, Beijing, People’s Republic of China; 2Department of Vascular Surgery, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong, People’s Republic of China; 3Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, People’s Republic of China
Correspondence: Yaoguo Yang, Department of Vascular Surgery, Beijing Anzhen Hospital, Capital Medical University, No. 2, Anzhen Road, Beijing, 100032, People’s Republic of China, Email yangyaoguo@ccmu.edu.cn
Objective: Compared to stable abdominal aortic aneurysms (AAA), the inflammatory response in perivascular adipose tissue (PVAT) may be exacerbated prior to rupture, leading to functional and structural alterations that manifest as imaging disparities. Radiomics enables the extraction of images features, which can be integrated with machine learning(ML) to construct models for clinical decision support. This study investigated the potential of radiomic features derived from PVAT to predict AAA rupture.
Methods: A retrospective analysis was conducted using aortic Computed Tomography Angiography (CTA) images from two centers, comprising patients with either stable or ruptured AAA who had pre-rupture CTA scans. These images were allocated to a development set and an external validation set. After radiomic feature extraction, statistically significant features between the two groups were subjected to dimensionality reduction. Subsequently, ten common ML models were constructed and validated using both internal and external validation sets.
Results: The development set comprised 37 ruptured patients and 155 non-ruptured patients. The external test set included 6 ruptured patients and 30 non-ruptured patients. A total of 107 radiomic features were extracted per patient, of which 18 exhibited statistically significant differences between groups. After dimensionality reduction, 5 representative features were selected. The constructed models achieved an average accuracy of 0.76 and an average AUC of 0.81 in the internal test set, while the external test set yielded an average accuracy of 0.73 and an average AUC of 0.77.
Conclusion: Significant differences exist in PVAT characteristics between ruptured and non-ruptured AAA patients, supporting the feasibility of using radiomic features for rupture prediction with reasonable accuracy.
Keywords: abdominal aortic aneurysm, computational models, cardiovascular disease, perivascular adipose tissue, machine learning
1Department of Vascular Surgery, Beijing Anzhen Hospital, Capital Medical University, Beijing, People’s Republic of China; 2Department of Vascular Surgery, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong, People’s Republic of China; 3Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, People’s Republic of China
Correspondence: Yaoguo Yang, Department of Vascular Surgery, Beijing Anzhen Hospital, Capital Medical University, No. 2, Anzhen Road, Beijing, 100032, People’s Republic of China, Email yangyaoguo@ccmu.edu.cn
Objective: Compared to stable abdominal aortic aneurysms (AAA), the inflammatory response in perivascular adipose tissue (PVAT) may be exacerbated prior to rupture, leading to functional and structural alterations that manifest as imaging disparities. Radiomics enables the extraction of images features, which can be integrated with machine learning(ML) to construct models for clinical decision support. This study investigated the potential of radiomic features derived from PVAT to predict AAA rupture.
Methods: A retrospective analysis was conducted using aortic Computed Tomography Angiography (CTA) images from two centers, comprising patients with either stable or ruptured AAA who had pre-rupture CTA scans. These images were allocated to a development set and an external validation set. After radiomic feature extraction, statistically significant features between the two groups were subjected to dimensionality reduction. Subsequently, ten common ML models were constructed and validated using both internal and external validation sets.
Results: The development set comprised 37 ruptured patients and 155 non-ruptured patients. The external test set included 6 ruptured patients and 30 non-ruptured patients. A total of 107 radiomic features were extracted per patient, of which 18 exhibited statistically significant differences between groups. After dimensionality reduction, 5 representative features were selected. The constructed models achieved an average accuracy of 0.76 and an average AUC of 0.81 in the internal test set, while the external test set yielded an average accuracy of 0.73 and an average AUC of 0.77.
Conclusion: Significant differences exist in PVAT characteristics between ruptured and non-ruptured AAA patients, supporting the feasibility of using radiomic features for rupture prediction with reasonable accuracy.
Keywords: abdominal aortic aneurysm, computational models, cardiovascular disease, perivascular adipose tissue, machine learning