Construction and Evaluation of Multiple Radiomics Models for Identifying the Instability of Intracranial Aneurysms Based on CTA - Li, Ran; Zhou, Pengyu; Chen, Xinyue; Mossa-Basha, Mahmud; Zhu, Chengcheng; Wang, Yuting (Frontiers in Neurology, 2022)
Background and aims: Identifying unruptured intracranial aneurysm instability is crucial for therapeutic decision-making. This study aims to evaluate the role of Radiomics and traditional morphological features in identifying aneurysm instability by constructing and comparing multiple models. Materials and methods: A total of 227 patients with 254 intracranial aneurysms evaluated by CTA were included. Aneurysms were divided into unstable and stable groups using comprehensive criteria: the unstable group was defined as aneurysms with near-term rupture, growth during follow-up, or caused compressive symptoms; those without the aforementioned conditions were grouped as stable aneurysms. Aneurysms were randomly divided into training and test sets at a 1:1 ratio. Radiomics and traditional morphological features (maximum diameter, irregular shape, aspect ratio, size ratio, location, etc.) were extracted. Three basic models and two integrated models were constructed after corresponding statistical analysis. Model A used traditional morphological parameters. Model B used Radiomics features. Model C used the Radiomics features related to aneurysm morphology. Furthermore, integrated models of traditional and Radiomics features were built (model A+B, model A+C). The area under curves (AUC) of each model was calculated and compared. Results: There were 31 (13.7%) patients harboring 36 (14.2%) unstable aneurysms, 15 of which ruptured post-imaging, 16 with growth on serial imaging, and 5 with compressive symptoms, respectively. Four traditional morphological features, six Radiomics features, and three Radiomics-derived morphological features were identified. The classification of aneurysm stability was as follows: the AUC of the training set and test set in models A, B, and C are 0.888(95 % CI 0.808-0.967) and 0.818(95 % CI 0.705-0.932), 0.865(95 % CI 0.777-0.952) and 0.739(95 % CI 0.636-0.841), 0.605(95 % CI 0.470-0.740) and 0.552(95 % CI 0.401-0.703), respectively. The AUC of integrated Model A+B was numerically slightly higher than any single model, whereas Model A+C was not. Conclusions: A radiomics and traditional morphology integrated model seems to be an effective tool for identifying intracranial aneurysm instability, whereas the use of Radiomics-derived morphological features alone is not recommended. Radiomics-based models were not superior to the traditional morphological features model.