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@ARTICLE{Chen:301902,
      author       = {Y. Chen and B. P. M. Laevens and T. Lemainque and G. A.
                      Müller-Franzes and T. Seibel and C. Dlugosch and J.
                      Clusmann and P.-H. Koop and R. Gong and Y. Liu and N. Jakhar
                      and F. Cao and S. Schophaus and T. B. Raju and A. A. Raptis
                      and F. van Haag and J. Joy and R. Loomba and L. Valenti and
                      J. N. Kather and T. J. Brinker$^*$ and M. Herzog and I. G.
                      Costa and D. Hernando and K. M. Schneider and D. Truhn and
                      C. V. Schneider},
      title        = {{D}eep {L}earning {R}eveals {L}iver {MRI} {F}eatures
                      {A}ssociated {W}ith {PNPLA}3 {I}148{M} in {S}teatotic
                      {L}iver {D}isease.},
      journal      = {Liver international},
      volume       = {45},
      number       = {7},
      issn         = {1478-3223},
      address      = {Oxford},
      publisher    = {Wiley-Blackwell},
      reportid     = {DKFZ-2025-01172},
      pages        = {e70164},
      year         = {2025},
      abstract     = {Steatotic liver disease (SLD) is the most common liver
                      disease worldwide, affecting $30\%$ of the global
                      population. It is strongly associated with the interplay of
                      genetic and lifestyle-related risk factors. The genetic
                      variant accounting for the largest fraction of SLD
                      heritability is PNPLA3 I148M, which is carried by $23\%$ of
                      the western population and increases the risk of SLD two to
                      three-fold. However, identification of variant carriers is
                      not part of routine clinical care and prevents patients from
                      receiving personalised care.We analysed MRI images and
                      common genetic variants in PNPLA3, TM6SF2, MTARC1, HSD17B13
                      and GCKR from a cohort of 45 603 individuals from the UK
                      Biobank. Proton density fat fraction (PDFF) maps were
                      generated using a water-fat separation toolbox, applied to
                      the magnitude and phase MRI data. The liver region was
                      segmented using a U-Net model trained on 600 manually
                      segmented ground truth images. The resulting liver masks and
                      PDFF maps were subsequently used to calculate liver PDFF
                      values. Individuals with (PDFF ≥ $5\%)$ and without SLD
                      (PDFF < $5\%)$ were selected as the study cohort and used to
                      train and test a Vision Transformer classification model
                      with five-fold cross validation. We aimed to differentiate
                      individuals who are homozygous for the PNPLA3 I148M variant
                      from non-carriers, as evaluated by the area under the
                      receiver operating characteristic curve (AUROC). To ensure a
                      clear genetic contrast, all heterozygous individuals were
                      excluded. To interpret our model, we generated attention
                      maps that highlight the regions that are most predictive of
                      the outcomes.Homozygosity for the PNPLA3 I148M variant
                      demonstrated the best predictive performance among five
                      variants with AUROC of 0.68 $(95\%$ CI: 0.64-0.73) in SLD
                      patients and 0.57 $(95\%$ CI: 0.52-0.61) in non-SLD
                      patients. The AUROCs for the other SNPs ranged from 0.54 to
                      0.57 in SLD patients and from 0.52 to 0.54 in non-SLD
                      patients. The predictive performance was generally higher in
                      SLD patients compared to non-SLD patients. Attention maps
                      for PNPLA3 I148M carriers showed that fat deposition in
                      regions adjacent to the hepatic vessels, near the liver
                      hilum, plays an important role in predicting the presence of
                      the I148M variant.Our study marks novel progress in the
                      non-invasive detection of homozygosity for PNPLA3 I148M
                      through the application of deep learning models on MRI
                      images. Our findings suggest that PNPLA3 I148M might affect
                      the liver fat distribution and could be used to predict the
                      presence of PNPLA3 variants in patients with fatty liver.
                      The findings of this research have the potential to be
                      integrated into standard clinical practice, particularly
                      when combined with clinical and biochemical data from other
                      modalities to increase accuracy, enabling easier
                      identification of at-risk individuals and facilitating the
                      development of tailored interventions for PNPLA3
                      I148M-associated liver disease.},
      keywords     = {Humans / Deep Learning / Lipase: genetics / Membrane
                      Proteins: genetics / Magnetic Resonance Imaging / Male /
                      Female / Liver: diagnostic imaging / Middle Aged / Fatty
                      Liver: genetics / Fatty Liver: diagnostic imaging / Adult /
                      Genetic Predisposition to Disease / Aged / Non-alcoholic
                      Fatty Liver Disease: genetics / Non-alcoholic Fatty Liver
                      Disease: diagnostic imaging / Acyltransferases /
                      Phospholipases A2, Calcium-Independent / deep learning
                      (Other) / medical imaging process (Other) / single
                      nucleotide polymorphism (Other) / steatotic liver disease
                      (Other) / PNPLA3 protein, human (NLM Chemicals) / Lipase
                      (NLM Chemicals) / Membrane Proteins (NLM Chemicals) /
                      Acyltransferases (NLM Chemicals) / Phospholipases A2,
                      Calcium-Independent (NLM Chemicals)},
      cin          = {C140},
      ddc          = {610},
      cid          = {I:(DE-He78)C140-20160331},
      pnm          = {313 - Krebsrisikofaktoren und Prävention (POF4-313)},
      pid          = {G:(DE-HGF)POF4-313},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40478199},
      pmc          = {pmc:PMC12143367},
      doi          = {10.1111/liv.70164},
      url          = {https://inrepo02.dkfz.de/record/301902},
}