Development and assessment of machine learning models for predicting recurrence risk after endovascular treatment in patients with intracranial aneurysms.

Shiteng Lin,Daizun Zou,Yang Zou,Jianjun Zou,Hui Liang,Xiaoping Gao,Zhihong Zhao,Jue Hu,Xiaoming Dai,Lan Xiang,Leheng Guo,Xinping Lin

doi:10.1007/s10143-021-01665-4

Abstract

Intracranial aneurysms (IAs) remain a major public health concern and endovascular treatment (EVT) has become a major tool for managing IAs. However, the recurrence rate of IAs after EVT is relatively high, which may lead to the risk for aneurysm re-rupture and re-bleed. Thus, we aimed to develop and assess prediction models based on machine learning (ML) algorithms to predict recurrence risk among patients with IAs after EVT in 6months. Patient population included patients with IAs after EVT between January 2016 and August 2019 in Hunan Provincial People's Hospital, and an adaptive synthetic (ADASYN) sampling approach was applied for the entire imbalanced dataset. We developed five ML models and assessed the models. In addition, we used SHapley Additive exPlanations (SHAP) and local interpretable model-agnostic explanation (LIME) algorithms to determine the importance of the selected features and interpret the ML models. A total of 425 IAs were enrolled into this study, and 66 (15.5%) of which recurred in 6months. Among the five ML models, gradient boosting decision tree (GBDT) model performed best. The area under curve (AUC) of the GBDT model on the testing set was 0.842 (sensitivity: 81.2%; specificity: 70.4%). Our study firstly demonstrated that ML-based models can serve as a reliable tool for predicting recurrence risk in patients with IAs after EVT in 6months, and the GBDT model showed the optimal prediction performance.

Highlights

Intracranial aneurysms (IAs) remains a major public health concern with prevalence of 0.4% − 3% of the general population[27]
Our study firstly demonstrated that machine learning (ML)-based models can serve as a reliable tool for predicting recurrence risk in patients with IAs after endovascular treatment (EVT) in 6 months and the gradient boosting decision tree (GBDT) model showed the optimal prediction performance
Aneurysm recurrence was defined as coil compaction, recanalization, aneurysm regrowth or neck enlargement[8], the angiographic results were evaluated by two neurointerventionalists who were not involved in this study

Summary

Introduction

Intracranial aneurysms (IAs) remains a major public health concern with prevalence of 0.4% − 3% of the general population[27]. Endovascular treatment (EVT) has become a major tool for managing IAs[2, 3]. It is estimated that the recurrence rate for IAs is in a range from 6.1–33.6% after EVT[11, 24]. The prediction of recurrence risk is highly meaningful for preventing recurrence and better treatment strategies. Aneurysm Recanalization Stratification Scale (ARSS) [17] have been proposed to predict the recurrence risk after EVT. ARSS is a composite risk score model that has demonstrated moderate discrimination with a C-statistic of 0.799. Nonlinearity and complexity of characteristic variables have not been fully considered in the traditional scoring model that based on the regression analysis. New models may be needed to achieve higher accuracy and to deal with nonlinear relationships

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Conclusion