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Background <p>Hemorrhagic complications (HCs) remain a major cause of morbidity in endovascular treatment (EVT) for brain arteriovenous malformation (bAVM), while reliable EVT-specific risk assessment remains challenging. This study aimed to develop and internally evaluate an exploratory model integrating digital subtraction angiography (DSA) radiomics and clinical features to assess the risk of EVT-related HCs.</p> Methods <p>This retrospective study included 620 consecutive bAVM patients who underwent first-time EVT between 2011 and 2024. Radiomics features were extracted from anteroposterior and lateral DSA images of the nidus. Clinical-only, radiomics-only, and combined models were developed using four algorithms with stratified five-fold cross-validation on the training set and evaluated on the test set. Model interpretability was performed using SHapley Additive exPlanations (SHAP).</p> Results <p>HCs occurred in 100 patients (16.1%). On the test set, the XGBoost-based fusion model achieved the best overall performance (AUC 0.81), showing a modest improvement over the radiomics-only XGBoost model while outperforming all clinical-only models. Radiomics features contributed substantially to model performance, with clinical variables providing complementary information.</p> Conclusions <p>A model integrating DSA radiomics and clinical features provides an exploratory approach to assessing the risk of EVT-related HCs in patients with bAVM. These findings suggest the potential value of DSA-derived radiomics for individualized peri-procedural risk assessment, although external validation is required before clinical application.</p>

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DSA radiomics and clinical features for hemorrhagic risk assessment in bAVM embolization

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月29日

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  • Ni Zeng,
  • Caihong Li,
  • Jianbin Zhu,
  • Qiuxian Wang,
  • Zhibo Wen

摘要

Background

Hemorrhagic complications (HCs) remain a major cause of morbidity in endovascular treatment (EVT) for brain arteriovenous malformation (bAVM), while reliable EVT-specific risk assessment remains challenging. This study aimed to develop and internally evaluate an exploratory model integrating digital subtraction angiography (DSA) radiomics and clinical features to assess the risk of EVT-related HCs.

Methods

This retrospective study included 620 consecutive bAVM patients who underwent first-time EVT between 2011 and 2024. Radiomics features were extracted from anteroposterior and lateral DSA images of the nidus. Clinical-only, radiomics-only, and combined models were developed using four algorithms with stratified five-fold cross-validation on the training set and evaluated on the test set. Model interpretability was performed using SHapley Additive exPlanations (SHAP).

Results

HCs occurred in 100 patients (16.1%). On the test set, the XGBoost-based fusion model achieved the best overall performance (AUC 0.81), showing a modest improvement over the radiomics-only XGBoost model while outperforming all clinical-only models. Radiomics features contributed substantially to model performance, with clinical variables providing complementary information.

Conclusions

A model integrating DSA radiomics and clinical features provides an exploratory approach to assessing the risk of EVT-related HCs in patients with bAVM. These findings suggest the potential value of DSA-derived radiomics for individualized peri-procedural risk assessment, although external validation is required before clinical application.