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

Frontiers in Neuroscience

Yunjun Yang¹  Xiufen Jia²  Yongchun Chen²  Jiafeng Zhou²  Nengzhi Xia²  Hao Wang²  Kuikui Zheng²  Chao Chen²  Dongqin Zhu²  Boli Lin²  Qiong Li³  Peipei Pang⁴  Yifei Ni⁵ 

[1] Department of Nuclear Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China;Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China;Department of Radiology, Wenzhou Central Hospital, Wenzhou, China;GE Healthcare China Co., Ltd., Shanghai, China;The First School of Medicine, Wenzhou Medical University, Wenzhou, China;

关键词: computed tomography angiography;    decision support techniques;    intracranial aneurysm;    machine learning;    middle cerebral artery;    nomograms;   

DOI  :  10.3389/fnins.2021.721268

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown