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Related Experiment Video

Updated: May 16, 2026

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery
09:41

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

Published on: May 20, 2016

Predicting Aneurysm Occlusion After Pipeline Embolization: an Ensemble Model Using Angiographic Parametric Imaging.

Haoyu Zhu1, Yuqi Song1, Yupeng Zhang1

  • 1From the Department of Neurosurgery (H.Z., Z.Y.), Peking University First Hospital, Beijing, China; Beijing Neurosurgical Institute (Y.S., X.F., Z.Z., F.H., J.Z., Yong Zhang, C.J.), Capital Medical University, Beijing, China and Department of Neurosurgery (Y.S., Yupeng Zhang, X.F., Z.Z., F.H., J.Z., Yong Zhang, C.J.), Beijing Tiantan Hospital, Capital Medical University, Beijing, China.

AJNR. American Journal of Neuroradiology
|May 14, 2026
PubMed
Summary

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An ensemble model integrating clinical, morphological, and hemodynamic features accurately predicts Pipeline Embolization Device (PED) outcomes for intracranial aneurysms (IAs). This tool aids in personalized treatment planning and risk stratification for better patient care.

Area of Science:

  • Neuroendovascular treatment
  • Medical imaging analysis
  • Machine learning in medicine

Background:

  • The Pipeline Embolization Device (PED) is crucial for treating intracranial aneurysms (IAs), particularly wide-neck ones.
  • Incomplete occlusion remains a challenge after PED treatment.
  • Predicting treatment outcomes is vital for patient management.

Purpose of the Study:

  • To develop an ensemble model for predicting post-PED occlusion in intracranial aneurysms.
  • Integrate clinical, morphological, and hemodynamic features for enhanced prediction accuracy.
  • Improve risk stratification and personalize treatment strategies for IA patients.

Main Methods:

  • Retrospective analysis of 306 patients with IAs treated with PED.
  • Extraction of clinical, morphological, and hemodynamic data from imaging (DSA, 3D rotational angiography).

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Published on: August 12, 2025

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Last Updated: May 16, 2026

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Published on: May 20, 2016

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  • Development of a weighted soft-voting ensemble model using five algorithms and SHAP for feature importance.

    • Main Results:

    • The ensemble model achieved an AUC of 0.902 in the external test set.
    • High accuracy (88.9%), sensitivity (93.9%), and specificity (71.4%) were observed.
    • Branch involvement, daughter sacs, and older age were independent predictors of incomplete occlusion.

    Conclusions:

    • Quantitative hemodynamic and morphological features derived from DSA are valuable biomarkers for PED efficacy.
    • The developed ensemble model demonstrates robust predictive performance.
    • The model can assist in risk stratification and personalized treatment planning for intracranial aneurysms.