Prediction of Intracranial Aneurysm Rupture Risk Using Non-Invasive Radiomics Analysis Based on Follow-Up Magnetic Resonance Angiography Images: A Preliminary Study  
[PERSON]  
1 Division of Medical Quantum Science, Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, Fukuoka 819-0395, Japan 12 Kokura Memorial Hospital, 3-2-1 Asano, Kokavakita-ku, Kitakyushu-shi 802-8555, Japan 2  
[PERSON]  
3 Division of Medical Quantum Science, Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan 3  
[PERSON]  
1 Division of Medical Quantum Science, Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, Fukuoka 819-0395, Japan 1  
[PERSON]  
1 Division of Medical Quantum Science, Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, Fukuoka 819-0395, Japan 1  
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If the aneurysm rupture risk can be non-invasively predicted, then preventive treatments can be carried out for high-risk patients, but unnecessary treatments can be avoided for low-risk patients. Magnetic resonance angiography (MRA) can non-invasively detect and monitor unruptured intracranial aneurysms (mean detection rate: 96%) for follow-up purposes without the use of a contrast agent or exposure of patients to radiation [5]. On the contrary, computed tomography angiography (CTA) requires a contrast agent and X-rays, but it can be utilized for detecting and monitoring unruptured intracranial aneurysms. MRA using a high magnetic field 3.0 Tesla MRI device has 99.3% sensitivity and 96.9% specificity for aneurysm detection [6], and there is no significant difference in the sensitivity and specificity between MRA and CTA [7]. Therefore, the prediction or monitoring of an aneurysm rupture risk based on non-invasive MRA is more appropriate. Moreover, computational prediction approaches should be developed in quantitative ways to mitigate intra- and inter-observer variability [8]. In recent years, radiomics studies have been performed to predict aneurysm rupture risk using CTA and digital subtraction angiography (DSA) [9; 10; 11; 12; 13; 14; 15; 16; 17]. [PERSON] et al. evaluated the feasibility of applying machine learning to predict aneurysm stability with morphological features derived from radiomics based on DSA [9]. [PERSON] et al. assessed the performance of radiomics-derived morphological features in predicting aneurysm rupture status using DSA [10]. [PERSON] et al. developed a CTA-based radiomics approach for the classification of intracranial aneurysm ruptures [11]. [PERSON] et al. aimed to determine robust radiomic features associated with middle cerebral artery (MCA) aneurysm ruptures and evaluate the additional value of combining morphological and radiomic features in the classification of ruptured MCA aneurysms [12]. [PERSON] et al. developed and validated a morphology-based radiomic signature nomogram to assess the risk of aneurysm ruptures [13]. [PERSON] et al. evaluated the radiomic differences between ruptured and unruptured aneurysms and explored their potential use in predicting aneurysm ruptures with CTA [14] and developed deep learning models with a limited amount of labeled data for predicting the rupture risk of untreated aneurysms [15]. [PERSON] et al. attempted to explore a strategy to enhance radiomics performance and facilitate its adoption for aneurysm risk stratification [16]. [PERSON] et al. investigated a novel semiautomatic prediction model for the rupture risk prediction of intracranial aneurysms with DSA [17].