000299567 001__ 299567
000299567 005__ 20250507140306.0
000299567 0247_ $$2doi$$a10.1093/ehjci/jeaf081
000299567 0247_ $$2pmid$$apmid:40052574
000299567 0247_ $$2ISSN$$a2047-2404
000299567 0247_ $$2ISSN$$a2047-2412
000299567 0247_ $$2altmetric$$aaltmetric:175044579
000299567 037__ $$aDKFZ-2025-00508
000299567 041__ $$aEnglish
000299567 082__ $$a610
000299567 1001_ $$00009-0005-5071-5519$$aFay, Louisa$$b0
000299567 245__ $$aDeterminants of ascending aortic morphology: Cross-sectional deep learning-based analysis on 25,073 non-contrast-enhanced NAKO MRI studies.
000299567 260__ $$aOxford$$bOxford University Press$$c2025
000299567 3367_ $$2DRIVER$$aarticle
000299567 3367_ $$2DataCite$$aOutput Types/Journal article
000299567 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1746619353_3748
000299567 3367_ $$2BibTeX$$aARTICLE
000299567 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000299567 3367_ $$00$$2EndNote$$aJournal Article
000299567 500__ $$a2025 Apr 30;26(5):895-907
000299567 520__ $$aUnderstanding determinants of thoracic aortic morphology is crucial for precise diagnostics and therapeutic approaches. This study aimed to automatically characterize ascending aortic morphology based on 3D non-contrast-enhanced magnetic resonance angiography (NC-MRA) data from the epidemiological cross-sectional German National Cohort (NAKO) and to investigate possible determinants of mid-ascending aortic diameter (mid-AAoD).Deep learning (DL) automatically segmented the thoracic aorta and ascending aortic length, volume, and diameter was extracted from 25,073 NC-MRAs. Statistical analyses investigated relationships between mid-AAoD and demographic factors, hypertension, diabetes, alcohol, and tobacco consumption. Males exhibited significantly larger mid-AAoD than females (M:35.5±4.8mm, F:33.3±4.5mm). Age and body surface area (BSA) were positively correlated with mid-AAoD (age: male: r²=0.20, p<0.001, female: r²=0.16, p<0.001; BSA: male: r²=0.08, p<0.001, female: r²=0.05, p<0.001). Hypertensive and diabetic subjects showed higher mid-AAoD (ΔHypertension = 2.9 ± 0.5mm; ΔDiabetes = 1.5 ± 0.6mm). 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 (male: 37.4±5.1mm, female: 35.0±4.8mm) and smoking history exceeding 16.5 pack-years (male: 36.6±5.0mm, female: 33.9±4.3mm) exhibited highest mid-AAoD. Causal analysis (Peter-Clark algorithm) suggested that age, BSA, hypertension, and alcohol consumption are possibly causally related to mid-AAoD, while diabetes and smoking are likely spuriously correlated.This study demonstrates the potential of DL and causal analysis for understanding ascending aortic morphology. By disentangling observed correlations using causal analysis, this approach identifies possible causal determinants, such as age, BSA, hypertension, and alcohol consumption. These findings can inform targeted diagnostics and preventive strategies, supporting clinical decision-making for cardiovascular health.
000299567 536__ $$0G:(DE-HGF)POF4-313$$a313 - Krebsrisikofaktoren und Prävention (POF4-313)$$cPOF4-313$$fPOF IV$$x0
000299567 588__ $$aDataset connected to CrossRef, PubMed, , Journals: inrepo02.dkfz.de
000299567 650_7 $$2Other$$aThoracic aorta
000299567 650_7 $$2Other$$aaortic organ
000299567 650_7 $$2Other$$aautomated shape analysis
000299567 650_7 $$2Other$$acausality
000299567 650_7 $$2Other$$adeep learning
000299567 650_7 $$2Other$$anon-contrast-enhanced magnetic resonance angiography
000299567 7001_ $$aHepp, Tobias$$b1
000299567 7001_ $$aWinkelmann, Moritz T$$b2
000299567 7001_ $$aPeters, Annette$$b3
000299567 7001_ $$aHeier, Margit$$b4
000299567 7001_ $$00000-0001-7584-6527$$aNiendorf, Thoralf$$b5
000299567 7001_ $$aPischon, Tobias$$b6
000299567 7001_ $$aEndemann, Beate$$b7
000299567 7001_ $$00000-0003-3100-1092$$aSchulz-Menger, Jeanette$$b8
000299567 7001_ $$aKrist, Lilian$$b9
000299567 7001_ $$aSchulze, Matthias B$$b10
000299567 7001_ $$00000-0002-3125-7859$$aMikolajczyk, Rafael$$b11
000299567 7001_ $$aWienke, Andreas$$b12
000299567 7001_ $$00000-0002-0903-9142$$aObi, Nadia$$b13
000299567 7001_ $$aSilenou, Bernard C$$b14
000299567 7001_ $$aLange, Berit$$b15
000299567 7001_ $$aKauczor, Hans-Ulrich$$b16
000299567 7001_ $$aLieb, Wolfgang$$b17
000299567 7001_ $$aBaurecht, Hansjörg$$b18
000299567 7001_ $$aLeitzmann, Michael$$b19
000299567 7001_ $$0P:(DE-He78)b09508a4c4afe85c57dd131eefa689ea$$aTrares, Kira$$b20$$udkfz
000299567 7001_ $$0P:(DE-He78)90d5535ff896e70eed81f4a4f6f22ae2$$aBrenner, Hermann$$b21$$udkfz
000299567 7001_ $$aMichels, Karin B$$b22
000299567 7001_ $$aJaskulski, Stefanie$$b23
000299567 7001_ $$00000-0001-7003-399X$$aVölzke, Henry$$b24
000299567 7001_ $$aNikolaou, Konstantin$$b25
000299567 7001_ $$aSchlett, Christopher L$$b26
000299567 7001_ $$00000-0002-7460-3942$$aBamberg, Fabian$$b27
000299567 7001_ $$aLescan, Mario$$b28
000299567 7001_ $$aYang, Bin$$b29
000299567 7001_ $$aKüstner, Thomas$$b30
000299567 7001_ $$aGatidis, Sergios$$b31
000299567 773__ $$0PERI:(DE-600)2647943-6$$a10.1093/ehjci/jeaf081$$gp. jeaf081$$n5$$p895-907$$tEuropean heart journal - cardiovascular imaging$$v26$$x2047-2404$$y2025
000299567 909CO $$ooai:inrepo02.dkfz.de:299567$$pVDB
000299567 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)b09508a4c4afe85c57dd131eefa689ea$$aDeutsches Krebsforschungszentrum$$b20$$kDKFZ
000299567 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)90d5535ff896e70eed81f4a4f6f22ae2$$aDeutsches Krebsforschungszentrum$$b21$$kDKFZ
000299567 9131_ $$0G:(DE-HGF)POF4-313$$1G:(DE-HGF)POF4-310$$2G:(DE-HGF)POF4-300$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$aDE-HGF$$bGesundheit$$lKrebsforschung$$vKrebsrisikofaktoren und Prävention$$x0
000299567 9141_ $$y2025
000299567 915__ $$0StatID:(DE-HGF)0420$$2StatID$$aNationallizenz$$d2025-01-06$$wger
000299567 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bEUR HEART J-CARD IMG : 2022$$d2025-01-06
000299567 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2025-01-06
000299567 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2025-01-06
000299567 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2025-01-06
000299567 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2025-01-06
000299567 915__ $$0StatID:(DE-HGF)1110$$2StatID$$aDBCoverage$$bCurrent Contents - Clinical Medicine$$d2025-01-06
000299567 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2025-01-06
000299567 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2025-01-06
000299567 915__ $$0StatID:(DE-HGF)9905$$2StatID$$aIF >= 5$$bEUR HEART J-CARD IMG : 2022$$d2025-01-06
000299567 9201_ $$0I:(DE-He78)C070-20160331$$kC070$$lC070 Klinische Epidemiologie und Alternf.$$x0
000299567 980__ $$ajournal
000299567 980__ $$aVDB
000299567 980__ $$aI:(DE-He78)C070-20160331
000299567 980__ $$aUNRESTRICTED