Introduction: Intracranial aneurysms (IAs) are a common vascular pathology and are associated with a risk of rupture, which is often fatal. Aneurysm growth is considered a surrogate of rupture risk; therefore, the study aimed to develop and evaluate prediction models of future artificial intelligence (AI) growth based on baseline aneurysm morphology as a computer-aided treatment decision support.Materials and methods: Follow-up CT angiography (CTA) and magnetic resonance angiography (MRA) angiograms of 39 patients with 44 IAs were classified by an expert as growing and stable (25/19). From the angiograms vascular surface meshes were extracted and the aneurysm shape was characterized by established morphologic features and novel deep shape features. The features corresponding to the baseline aneurysms were used to predict future aneurysm growth using univariate thresholding, multivariate random forest and multi-layer perceptron (MLP) learning, and deep shape learning based on the PointNet++ model.Results: The proposed deep shape feature learning method achieved an accuracy of 0.82 (sensitivity = 0.96, specificity = 0.63), while the multivariate learning and univariate thresholding methods were inferior with an accuracy of up to 0.68 and 0.63, respectively.Conclusion: High-performing classification of future growing IAs renders the proposed deep shape features learning approach as the key enabling tool to manage rupture risk in the “no treatment” paradigm of patient follow-up imaging.
Introduction: In cases where the risk of intracranial aneurysms (IA) rupture is low or secondary to other patient health concerns, unruptured IA may be monitored through imaging. In this work, we applied different computational methods to detect IA growth and compared the results to clinical findings. Hypothesis: We hypothesize that automated methods of IA growth detection are comparable to clinical assessment. Methods: The study cohort consisted of 20 female patients with saccular IA diagnosed between 2005-2011 in UCLA Medical Center. 6 were located at the PcoA, 10 at the superior hypophyseal artery, and 4 at the ophthalmic artery. 8 IA were determined to be growing. Baseline IA size was 3.85±4.30 mm. For each case, initial and first follow-up CTA image studies (interval 2.50±2.75 yrs) were analyzed. Cohort follow-up continued for an average of 8.5±5.75 yrs. Automated methods to detect IA growth included maximum diameter (HMAX), surface area (SA), volume (V), and a novel 2-stage morphing approach which deforms the baseline IA surface mesh to that of the subsequent scan and yields a set of characteristics that describe the changes: dMPL, dSA, dV, and dICDD. Statistical methods used included the Mann-Whitney U test and Chi-Square Test with significance set at p <0.01, and ROC AUC analysis. Results: The stable and growth groups did not significantly differ with respect to case details and medical history, including IA size, location, imaging interval, age, family history, stroke, hypertension, thyroid disease, cancer, and atherosclerosis. Clinically determined change in IA diameter (p=0.007, AUC=0.927), computed HMAX (p=0.0002, AUC=0.958), SA (p=0.001, AUC=0.917), V (p=0.001, AUC=0.927), and dSA (p=0.005, AUC=0.865) were significantly different between the groups. The duration of follow-up significantly differed between the groups (p<0.01), largely due to treatment of growing IA. During follow-up only one IA changing from stable to growing, and 5 of 6 subsequently treated IA were from within the initial growth group. Conclusion: Several automated measures provided comparable performance to clinical size when assessing IA growth. HMAX in particular may be useful to assist clinical evaluation, as it was slightly more effective than recorded clinical size alone.
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