Determinants of ascending aortic morphology: Cross-sectional deep learning-based analysis on 25,073 non-contrast-enhanced MRI of NAKO

Abstract

ABSTRACTBackgroundPathologies of the thoracic aorta are associated with chronic cardiovascular disease and can be of life-threatening nature. Understanding determinants of thoracic aortic morphology is crucial for precise diagnostics and preventive and therapeutic approaches. This study aimed to automatically characterize ascending aortic morphology based on 3D non-contrast-enhanced magnetic resonance angiography (NE-MRA) data from the large epidemiological cross-sectional German National Cohort (NAKO) and to investigate possible determinants of mid-ascending aortic diameter (mid-AAoD).MethodsDeep learning was used to automatically segment the thoracic aorta and extract ascending aortic length, volume, and diameter from 25,073 NE-MRAs. Descriptive statistics, correlation analyses, and multivariable regression were used to investigate statistical relationships between mid-AAoD and demographic factors, hypertension, diabetes, alcohol, and tobacco consumption. Additionally, automated causal discovery analysis using the Peter-Clark algorithm was performed to identify possible causal interactions.ResultsMales exhibited significantly larger mid-AAoD than females (M: 35.5±4.8 mm, F: 33.3±4.5 mm). Age and body surface area (BSA) were positively correlated with mid-AAoD. Hypertensive and diabetic subjects showed higher mid-AAoD. Hypertension was linked to higher mid-AAoD regardless of age and BSA, while diabetes and mid-AAoD were uncorrelated across age-stratified subgroups. Daily alcohol consumption and smoking history exceeding 16.5 pack-years exhibited highest mid-AAoD. Causal analysis revealed that age, BSA, hypertension, and alcohol consumption are possibly causally related to mid-AAoD, while diabetes and smoking are likely spuriously correlated.ConclusionsMid-AAoD varies significantly within the unique large-scale NAKO population depending on demographic factors, individual health, and lifestyle. This work provides a proof-of-concept for automated causal analysis which can help disentangle observed correlations and identify potential causal determinants of ascending aortic morphology.CLINICAL PERSPECTIVENon-contrast-enhanced magnetic resonance angiography (NE-MRA) is a highly effective and safe imaging technique for evaluating vascular structures without using contrast agents. We propose in this work an automated analysis of the acquired NE-MRA to extract the thoracic aortic shape in 3D and the computation of its morphology. Quantitative description of morphology in the whole thoracic aortic shape supports a fast and precise prophylactic surgery decision as well as the evaluation of thoracic aortic determinants that impact the morphology. Beyond investigation of correlations between determinants and morphological changes in the thoracic aorta, the identification of causal relationships is essential for effective diagnoses and therapy planning. Since correlation does not imply causation, confounding factors may exist that create spurious correlations which lead to wrong conclusions and biased diagnosis. Hence, causal investigations are indispensable to identify the causal determinants towards morphological changes in the thoracic aorta.