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@ARTICLE{Fay:299567,
      author       = {L. Fay and T. Hepp and M. T. Winkelmann and A. Peters and
                      M. Heier and T. Niendorf and T. Pischon and B. Endemann and
                      J. Schulz-Menger and L. Krist and M. B. Schulze and R.
                      Mikolajczyk and A. Wienke and N. Obi and B. C. Silenou and
                      B. Lange and H.-U. Kauczor and W. Lieb and H. Baurecht and
                      M. Leitzmann and K. Trares$^*$ and H. Brenner$^*$ and K. B.
                      Michels and S. Jaskulski and H. Völzke and K. Nikolaou and
                      C. L. Schlett and F. Bamberg and M. Lescan and B. Yang and
                      T. Küstner and S. Gatidis},
      title        = {{D}eterminants of ascending aortic morphology:
                      {C}ross-sectional deep learning-based analysis on 25,073
                      non-contrast-enhanced {NAKO} {MRI} studies.},
      journal      = {European heart journal - cardiovascular imaging},
      volume       = {26},
      number       = {5},
      issn         = {2047-2404},
      address      = {Oxford},
      publisher    = {Oxford University Press},
      reportid     = {DKFZ-2025-00508},
      pages        = {895-907},
      year         = {2025},
      note         = {2025 Apr 30;26(5):895-907},
      abstract     = {Understanding 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.},
      keywords     = {Thoracic aorta (Other) / aortic organ (Other) / automated
                      shape analysis (Other) / causality (Other) / deep learning
                      (Other) / non-contrast-enhanced magnetic resonance
                      angiography (Other)},
      cin          = {C070},
      ddc          = {610},
      cid          = {I:(DE-He78)C070-20160331},
      pnm          = {313 - Krebsrisikofaktoren und Prävention (POF4-313)},
      pid          = {G:(DE-HGF)POF4-313},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40052574},
      doi          = {10.1093/ehjci/jeaf081},
      url          = {https://inrepo02.dkfz.de/record/299567},
}