Classifying Ruptured Middle Cerebral Artery Aneurysms With a Machine Learning Based, Radiomics-Morphological Model: A Multicentral Study.

Dongqin Zhu,Kuikui Zheng,Hao Wang,Chao Chen,Nengzhi Xia,Jiafeng Zhou,Yifei Ni,Qiong Li,Yongchun Chen,Xiufen Jia,Boli Lin,Peipei Pang,Yunjun Yang

doi:10.3389/fnins.2021.721268

Abstract

ObjectiveRadiomics and morphological features were associated with aneurysms rupture. However, the multicentral study of their predictive power for specific-located aneurysms rupture is rare. We aimed to determine robust radiomics features related to middle cerebral artery (MCA) aneurysms rupture and evaluate the additional value of combining morphological and radiomics features in the classification of ruptured MCA aneurysms.MethodsA total of 632 patients with 668 MCA aneurysms (423 ruptured aneurysms) from five hospitals were included. Radiomics and morphological features of aneurysms were extracted on computed tomography angiography images. The model was developed using a training dataset (407 patients) and validated with the internal (152 patients) and external validation (73 patients) datasets. The support vector machine method was applied for model construction. Optimal radiomics, morphological, and clinical features were used to develop the radiomics model (R-model), morphological model (M-model), radiomics-morphological model (RM-model), clinical-morphological model (CM-model), and clinical-radiomics-morphological model (CRM-model), respectively. A comprehensive nomogram integrating clinical, morphological, and radiomics predictors was generated.ResultsWe found seven radiomics features and four morphological predictors of MCA aneurysms rupture. The R-model obtained an area under the receiver operating curve (AUC) of 0.822 (95% CI, 0.776, 0.867), 0.817 (95% CI, 0.744, 0.890), and 0.691 (95% CI, 0.567, 0.816) in the training, temporal validation, and external validation datasets, respectively. The RM-model showed an AUC of 0.848 (95% CI, 0.810, 0.885), 0.865 (95% CI, 0.807, 0.924), and 0.721 (95% CI, 0.601, 0.841) in the three datasets. The CRM-model obtained an AUC of 0.856 (95% CI, 0.820, 0.892), 0.882 (95% CI, 0.828, 0.936), and 0.738 (95% CI, 0.618, 0.857) in the three datasets. The CRM-model and RM-model outperformed the CM-model and M-model in the internal datasets (p < 0.05), respectively. But these differences were not statistically significant in the external dataset. Decision curve analysis indicated that the CRM-model obtained the highest net benefit for most of the threshold probabilities.ConclusionRobust radiomics features were determined related to MCA aneurysm rupture. The RM-model exhibited good ability in classifying ruptured MCA aneurysms. Integrating radiomics features into conventional models might provide additional value in ruptured MCA aneurysms classification.

Highlights

Middle cerebral artery (MCA) aneurysm is the most common subtype of unruptured aneurysms (Huttunen et al, 2010; Can et al, 2015)
Exclusion criteria were as follows: fusiform MCA aneurysms, aneurysms combined with vascular diseases, aneurysms with a size
Patients with MCA aneurysms seen in hospital A from January 2009 to December 2019 were allocated to the training and the internal validation datasets

Summary

Introduction

Middle cerebral artery (MCA) aneurysm is the most common subtype of unruptured aneurysms (Huttunen et al, 2010; Can et al, 2015). With the improvement of imaging techniques, unruptured aneurysms have become more frequently detected (Greving et al, 2014). Many unruptured aneurysms stay asymptomatic and never rupture (Korja et al, 2014). The prophylactic treatment such as current endovascular and microsurgical interventions carries the risk of procedurerelated complications (Naggara et al, 2010; Zhu et al, 2020). Once the aneurysm ruptures, the outcome is catastrophic (Vlak et al, 2011). It is vital to screen out rupture-prone aneurysms

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