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CT Angiography Radiomics Combining Traditional Risk Factors to Predict Brain Arteriovenous Malformation Rupture: a Machine Learning, Multicenter Study

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Abstract

This study aimed to develop a machine learning model for predicting brain arteriovenous malformation (bAVM) rupture using a combination of traditional risk factors and radiomics features. This multicenter retrospective study enrolled 586 patients with unruptured bAVMs from 2010 to 2020. All patients were grouped into the hemorrhage (n = 368) and non-hemorrhage (n = 218) groups. The bAVM nidus were segmented on CT angiography images using Slicer software, and radiomic features were extracted using Pyradiomics. The dataset included a training set and an independent testing set. The machine learning model was developed on the training set and validated on the testing set by merging numerous base estimators and a final estimator based on the stacking method. The area under the receiver operating characteristic (ROC) curve, precision, and the f1 score were evaluated to determine the performance of the model. A total of 1790 radiomics features and 8 traditional risk factors were contained in the original dataset, and 241 features remained for model training after L1 regularization filtering. The base estimator of the ensemble model was Logistic Regression, whereas the final estimator was Random Forest. In the training set, the area under the ROC curve of the model was 0.982 (0.967–0.996) and 0.893 (0.826–0.960) in the testing set. This study indicated that radiomics features are a valuable addition to traditional risk factors for predicting bAVM rupture. In the meantime, ensemble learning can effectively improve the performance of a prediction model.

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Data Availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Funding

This study was funded by the National Key Research and Disease Program (CN): Early Identification and Screening of Panvascular Diseases (2021YFC2500502)

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Authors and Affiliations

  1. Department of Neurosurgery, Beijing Hospital, National Center of Gerontology; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China

    Shaosen Zhang, Junjie Wang & Dong Zhang

  2. Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China

    Shengjun Sun & Xiaochen Wang

  3. Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China

    Qian Zhang, Yuanren Zhai & Peicong Ge

  4. Department of Neurosurgery, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China

    Zhiyong Shi

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  1. Shaosen Zhang
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Correspondence to Dong Zhang.

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Zhang, S., Wang, J., Sun, S. et al. CT Angiography Radiomics Combining Traditional Risk Factors to Predict Brain Arteriovenous Malformation Rupture: a Machine Learning, Multicenter Study. Transl. Stroke Res. (2023). https://doi.org/10.1007/s12975-023-01166-0

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