Predicting postinterventional rupture of intracranial aneurysms using arteriography-derived radiomic features after pipeline embolization

Chao Ma; Shikai Liang; Fei Liang; Ligong Lu; Haoyu Zhu; Xianli Lv; Xuejun Yang; Chuhan Jiang; Yupeng Zhang

doi:10.3389/fneur.2024.1327127

Predicting postinterventional rupture of intracranial aneurysms using arteriography-derived radiomic features after pipeline embolization

Front Neurol. 2024 Mar 7:15:1327127. doi: 10.3389/fneur.2024.1327127. eCollection 2024.

Authors

Chao Ma^#^{1

2}, Shikai Liang^#^{1

2

3}, Fei Liang⁴, Ligong Lu⁵, Haoyu Zhu⁶, Xianli Lv², Xuejun Yang^{2

3}, Chuhan Jiang⁶, Yupeng Zhang⁶

Affiliations

¹ School of Clinical Medicine, Tsinghua University, Beijing, China.
² Department of Neurosurgery, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China.
³ Institute for Intelligent Healthcare, Tsinghua University, Beijing, China.
⁴ Department of Vascular Surgery and Interventional Radiology, Peking University Third Hospital, Beijing, China.
⁵ Guangdong Provincial Key Laboratory of Tumor Interventional Diagnosis and Treatment, Zhuhai Interventional Medical Center, Zhuhai Hospital, Affiliated with Jinan University, Zhuhai, China.
⁶ Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.

^# Contributed equally.

Abstract

Background and purpose: Postinterventional rupture of intracranial aneurysms (IAs) remains a severe complication after flow diverter treatment. However, potential hemodynamic mechanisms underlying independent predictors for postinterventional rupture of IAs remain unclear. In this study, we employed arteriography-derived radiomic features to predict this complication.

Methods: We included 64 patients who underwent pipeline flow diversion for intracranial aneurysms, distinguishing between 16 patients who experienced postinterventional rupture and 48 who did not. We performed propensity score matching based on clinical and morphological factors to match these patients with 48 patients with postinterventional unruptured IAs at a 1:3 ratio. Postinterventional digital subtraction angiography were used to create five arteriography-derived perfusion parameter maps and then radiomics features were obtained from each map. Informative features were selected through the least absolute shrinkage and selection operator method with five-fold cross-validation. Subsequently, radiomics scores were formulated to predict the occurrence of postinterventional IA ruptures. Prediction performance was evaluated with the training and test datasets using area under the curve (AUC) and confusion matrix-derived metrics.

Results: Overall, 1,459 radiomics features were obtained, and six were selected. The resulting radiomics scores had high efficacy in distinguishing the postinterventional rupture group. The AUC and Youden index were 0.912 (95% confidence interval: 0.767-1.000) and 0.847 for the training dataset, respectively, and 0.938 (95% confidence interval, 0.806-1.000) and 0.800 for the testing dataset, respectively.

Conclusion: Radiomics scores generated using arteriography-derived radiomic features effectively predicted postinterventional IA ruptures and may aid in differentiating IAs at high risk of postinterventional rupture.

Keywords: angiography; hemodynamics; intracranial aneurysm; perfusion; propensity score.

Grants and funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This work was supported by the Institute for Intelligent Healthcare, Tsinghua University (Grant number: 2022ZLB007), and the Beijing Natural Science Foundation Project (Grant number: 7212007).