A preliminary investigation of radiomics differences between ruptured and unruptured intracranial aneurysms
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IMAGING INFORMATICS AND ARTIFICIAL INTELLIGENCE
A preliminary investigation of radiomics differences between ruptured and unruptured intracranial aneurysms Chubin Ou 1,2 & Winston Chong 3 & Chuan-Zhi Duan 2 & Xin Zhang 2 & Michael Morgan 1 & Yi Qian 1 Received: 23 March 2020 / Revised: 7 August 2020 / Accepted: 18 September 2020 # European Society of Radiology 2020
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. Key Points • Significant radiomics differences exist between ruptured and unruptured intracranial aneurysms. • Radiomics shape features can significantly improve rupture prediction performance over conventional morphology-based prediction model. The inclusion of histogram and texture radiomics features can further improve the performance. • A simplified model with two variables achieved a similar level of performance as the more complex ones. Our prediction model can serve as a promising tool for the risk management of intracranial aneurysms. Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00330-020-07325-3) contains supplementary material, which is available to authorized users. * Yi Qian [email protected] 1
Faculty of Medicine and Health Sciences, Macquarie University, Sydney, New South Wales, Australia
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