Stability Assessment of Intracranial Aneurysms Using Machine Learning Based on Clinical and Morphological Features
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ORIGINAL ARTICLE
Stability Assessment of Intracranial Aneurysms Using Machine Learning Based on Clinical and Morphological Features Wei Zhu 1 & Wenqiang Li 1 & Zhongbin Tian 1 & Yisen Zhang 1 & Kun Wang 1 & Ying Zhang 1 & Jian Liu 1 & Xinjian Yang 1 Received: 19 February 2020 / Revised: 19 March 2020 / Accepted: 19 March 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Machine learning (ML) as a novel approach could help clinicians address the challenge of accurate stability assessment of unruptured intracranial aneurysms (IAs). We developed multiple ML models for IA stability assessment and compare their performances. We enrolled 1897 consecutive patients with unstable (n = 528) and stable (n = 1539) IAs. Thirteen patient-specific clinical features and eighteen aneurysm morphological features were extracted to generate support vector machine (SVM), random forest (RF), and feed-forward artificial neural network (ANN) models. The discriminatory performances of the models were compared with statistical logistic regression (LR) model and the PHASES score in IA stability assessment. Based on the receiver operating characteristic (ROC) curve and area under the curve (AUC) values for each model in the test set, the AUC values for RF, SVM, and ANN were 0.850 (95% CI 0.806–0.893), 0.858 (95 %CI 0.816–0.900), and 0.867 (95% CI 0.828– 0.906), demonstrating good discriminatory ability. All ML models exhibited superior performance compared with the statistical LR and the PHASES score (the AUC values were 0.830 and 0.589, respectively; RF versus PHASES, P < 0.001; RF versus LR, P = 0.038). Important features contributing to the stability discrimination included three clinical features (location, sidewall/ bifurcation type, and presence of symptoms) and three morphological features (undulation index, height-width ratio, and irregularity). These findings demonstrate the potential of ML to augment the clinical decision-making process for IA stability assessment, which may enable more optimal management for patients with IAs in the future. Keywords Intracranial aneurysms . Risk evaluation . Artificial intelligence . Machine learning . Unstable aneurysm
Introduction Intracranial aneurysms (IAs) affect about 3–5% of the adult population [1]. As the application of non-invasive imaging modalities as diagnostic tools has increased over the past This work was originated from Beijing Neurosurgical Institute and Beijing Tian Tan Hospital, No.119 South Fourth Ring West Road, Fengtai District, Beijing Electronic supplementary material The online version of this article (https://doi.org/10.1007/s12975-020-00811-2) contains supplementary material, which is available to authorized users. * Jian Liu [email protected] * Xinjian Yang [email protected] 1
Department of Interventional Neuroradiology, Beijing Neurosurgical Institute and Beijing Tiantan Hospital, Capital Medical University, No.119 South Fourth Ring West Road, Fengtai District, Beijing 100050, China
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