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Interpretable machine learning model to predict rupture of small intracranial aneurysms and facilitate clinical decision

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Abstract

Estimating whether to treat the rupture risk of small intracranial aneurysms (IAs) with size ≤ 7 mm in diameter is difficult but crucial. We aimed to construct and externally validate a convenient machine learning (ML) model for assessing the rupture risk of small IAs. One thousand four patients with small IAs recruited from two hospitals were included in our retrospective research. The patients at hospital 1 were stratified into training (70%) and internal validation set (30%) randomly, and the patients at hospital 2 were used for external validation. We selected predictive features using the least absolute shrinkage and selection operator (LASSO) method and constructed five ML models applying diverse algorithms including random forest classifier (RFC), categorical boosting (CatBoost), support vector machine (SVM) with linear kernel, light gradient boosting machine (LightGBM), and extreme gradient boosting (XGBoost). The Shapley Additive Explanations (SHAP) analysis provided interpretation for the best ML model. The training, internal, and external validation cohorts included 658, 282, and 64 IAs, respectively. The best performance was presented by SVM as AUC of 0.817 in the internal [95% confidence interval (CI), 0.769–0.866] and 0.893 in the external (95% CI, 0.808–0.979) validation cohorts, which overperformed compared with the PHASES score significantly (all P < 0.001). SHAP analysis showed maximum size, location, and irregular shape were the top three important features to predict rupture. Our SVM model based on readily accessible features presented satisfying ability of discrimination in predicting the rupture IAs with small size. Morphological parameters made important contributions to prediction result.

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Funding

This study was funded by the Special Scientific Research Fund Project of Jiangsu Research Hospital Association — Precisely Drug use — CSPC Pharmaceutical Group Co., Ltd. [grant number JY202001]; Nanjing Pharmaceutical Association — Changzhou Siyao — Hospital Pharmaceutical Research Fund [grant number 2021YX014]; National Natural Science Foundation of China [grant number 82173899].

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Author notes

  1. WeiGen Xiong, TingTing Chen, and Jun Li contributed equally to this work.

Authors and Affiliations

  1. School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China

    WeiGen Xiong & TingTing Chen

  2. Department of Pharmacy, Nanjing First Hospital, Nanjing Medical University, Nanjing, China

    WeiGen Xiong, TingTing Chen, Jun Li, YueZhang Wu, Qiong Jie, RunZe Qiu, ZeYue Xu, JianJun Zou & HongWei Fan

  3. Department of Pharmacology, Nanjing First Hospital, China Pharmaceutical University, Nanjing, China

    Jun Li, YueZhang Wu, Qiong Jie, RunZe Qiu, ZeYue Xu, JianJun Zou & HongWei Fan

  4. Department of Neurology, The First Affiliated Hospital (People’s Hospital of Hunan Province), Hunan Normal University, Changsha, China

    Lan Xiang, Cheng Zhang, Liang Xiang & ZhiHong Zhao

  5. Department of Neurosurgery, The Second Affiliated Hospital, Nanjing Medical University, Nanjing, China

    YingBin Li & Dong Chu

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Contributions

JJZ, HWF, and ZZH conceived and designed the study. WGX, TTC, and JL contributed equally to this work. WGX, TTC, and JL conducted the literature review. TTC performed data analysis. WGX drafted the manuscript. LX, CZ, LiX, YBL, and DC collected the data. YZW, QJ, RZQ, and ZYX polished this article. All the authors have read and agreed to the published version of the manuscript.

Corresponding authors

Correspondence to JianJun Zou, HongWei Fan or ZhiHong Zhao.

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The ethics committee of Hunan Provincial People's Hospital has approved this study ([2015]-10) and waived the requirement of written informed consent.

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Xiong, W., Chen, T., Li, J. et al. Interpretable machine learning model to predict rupture of small intracranial aneurysms and facilitate clinical decision. Neurol Sci 43, 6371–6379 (2022). https://doi.org/10.1007/s10072-022-06351-x

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