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Background <p>High-resolution vessel wall imaging (HR-VWI) enables in vivo assessment of aneurysm wall pathology, but conventional evaluation remains largely qualitative. This study aimed to develop and validate an HR-VWI-based radiomics model using aneurysm wall and parent artery wall features to identify symptomatic intracranial aneurysms (SIAs) and improve risk stratification.</p> Methods <p>A total of 410 patients with 446 intracranial aneurysms (IAs), comprising symptomatic (<i>n</i> = 112) and asymptomatic (<i>n</i> = 334) intracranial aneurysms from two centers, were included in this retrospective study. HR-VWI images were preprocessed to extract regions of interest from both the aneurysm wall and the parent artery (PA). Radiomic features were subsequently extracted using Pyradiomics, yielding a comprehensive set of 851 features per ROI. Feature selection was performed through a multi-stage process involving variance analysis, independent t-tests, and ElasticNet regularization. Based on these selected features, three imaging models were developed, including Radscore_IA (for the IA), Radscore_PA (for the PA), and Radscore_IA_PA (a combined model). Moreover, aneurysm location was incorporated as a morphological parameter into the refined models: Radscore_LOC_IA, Radscore_LOC_PA, and Radscore_LOC_IA_PA. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), with the PHASES score serving as a comparative benchmark.</p> Results <p>Radiomics features derived from the IA (<i>n</i> = 7) and PA (<i>n</i> = 3) associated with SIAs were identified. In the validation cohort, the AUC values for SIA identification were as follows: PHASES (0.679), Radscore_IA (0.837), Radscore_PA (0.820), Radscore_IA_PA (0.878), Radscore_LOC_IA (0.852), Radscore_LOC_PA (0.842), and Radscore_LOC_IA_PA (0.888). The Radscore_LOC_IA_PA model exhibited the best performance, outperforming other models. Calibration and decision curve analyses confirmed the robustness and clinical application of all developed models.</p> Conclusions <p>This study presents an innovative HR-VWI radiomics-based model for identifying high-risk SIA, Radscore_LOC_IA_PA, which integrates radiomics features from the IA wall and PA wall along with aneurysm location. Compared to traditional stratification methods, the model exhibits showed improved discrimination to identify high-risk SIA, enabling more accurate risk stratification and clinical management strategies for this patient population.</p>

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Radiomics model for risk stratification of intracranial aneurysm: a high-resolution vessel wall imaging-based study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月12日

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  • Zhiming Zhou,
  • Qingyu WU,
  • Yilin Deng,
  • Yan Wang,
  • Shuangyan Jiang,
  • Chang Yan,
  • Wenqing Yuan,
  • Zihang Wang,
  • Ting Chen

摘要

Background

High-resolution vessel wall imaging (HR-VWI) enables in vivo assessment of aneurysm wall pathology, but conventional evaluation remains largely qualitative. This study aimed to develop and validate an HR-VWI-based radiomics model using aneurysm wall and parent artery wall features to identify symptomatic intracranial aneurysms (SIAs) and improve risk stratification.

Methods

A total of 410 patients with 446 intracranial aneurysms (IAs), comprising symptomatic (n = 112) and asymptomatic (n = 334) intracranial aneurysms from two centers, were included in this retrospective study. HR-VWI images were preprocessed to extract regions of interest from both the aneurysm wall and the parent artery (PA). Radiomic features were subsequently extracted using Pyradiomics, yielding a comprehensive set of 851 features per ROI. Feature selection was performed through a multi-stage process involving variance analysis, independent t-tests, and ElasticNet regularization. Based on these selected features, three imaging models were developed, including Radscore_IA (for the IA), Radscore_PA (for the PA), and Radscore_IA_PA (a combined model). Moreover, aneurysm location was incorporated as a morphological parameter into the refined models: Radscore_LOC_IA, Radscore_LOC_PA, and Radscore_LOC_IA_PA. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), with the PHASES score serving as a comparative benchmark.

Results

Radiomics features derived from the IA (n = 7) and PA (n = 3) associated with SIAs were identified. In the validation cohort, the AUC values for SIA identification were as follows: PHASES (0.679), Radscore_IA (0.837), Radscore_PA (0.820), Radscore_IA_PA (0.878), Radscore_LOC_IA (0.852), Radscore_LOC_PA (0.842), and Radscore_LOC_IA_PA (0.888). The Radscore_LOC_IA_PA model exhibited the best performance, outperforming other models. Calibration and decision curve analyses confirmed the robustness and clinical application of all developed models.

Conclusions

This study presents an innovative HR-VWI radiomics-based model for identifying high-risk SIA, Radscore_LOC_IA_PA, which integrates radiomics features from the IA wall and PA wall along with aneurysm location. Compared to traditional stratification methods, the model exhibits showed improved discrimination to identify high-risk SIA, enabling more accurate risk stratification and clinical management strategies for this patient population.