A preliminary investigation of radiomics differences between ruptured and unruptured intracranial aneurysms

Chubin Ou, Winston Chong, Chuan-Zhi Duan, Xin Zhang, Michael Morgan, Yi Qian

    Research output: Contribution to journalArticlepeer-review

    9 Citations (Scopus)

    Abstract

    Objectives Prediction of intracranial aneurysm rupture is important in the management of unruptured aneurysms. The application of radiomics in predicting aneurysm rupture remained largely unexplored. This study aims to evaluate the radiomics differences between ruptured and unruptured aneurysms and explore its potential use in predicting aneurysm rupture. Methods One hundred twenty-two aneurysms were included in the study (93 unruptured). Morphological and radiomics features were extracted for each case. Statistical analysis was performed to identify significant features which were incorporated into prediction models constructed with a machine learning algorithm. To investigate the usefulness of radiomics features, three models were constructed and compared. The baseline model A was constructed with morphological features, while model B was constructed with addition of radiomics shape features and model C with more radiomics features. Multivariate analysis was performed for the ten most important variables in model C to identify independent risk factors. A simplified model based on independent risk factors was constructed for clinical use. Results Five morphological features and 89 radiomics features were significantly associated with rupture. Model A, model B, and model C achieved the area under the receiver operating characteristic curve of 0.767, 0.807, and 0.879, respectively. Model C was significantly better than model A and model B (p < 0.001). Multivariate analysis identified two radiomics features which were used to construct the simplified model showing an AUROC of 0.876. Conclusions Radiomics signatures were different between ruptured and unruptured aneurysms. The use of radiomics features, especially texture features, may significantly improve rupture prediction performance.
    Original languageEnglish
    Pages (from-to)2716-2725
    Number of pages10
    JournalEuropean Radiology
    Volume31
    Issue number5
    Early online date14 Oct 2020
    DOIs
    Publication statusPublished - May 2021

    Keywords

    • Intracranial aneurysm
    • Radiomics
    • Rupture
    • Stroke
    • Machine learning

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