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000301902 1001_ $$00009-0005-9893-645X$$aChen, Yazhou$$b0
000301902 245__ $$aDeep Learning Reveals Liver MRI Features Associated With PNPLA3 I148M in Steatotic Liver Disease.
000301902 260__ $$aOxford$$bWiley-Blackwell$$c2025
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000301902 520__ $$aSteatotic 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.
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000301902 650_7 $$2Other$$adeep learning
000301902 650_7 $$2Other$$amedical imaging process
000301902 650_7 $$2Other$$asingle nucleotide polymorphism
000301902 650_7 $$2Other$$asteatotic liver disease
000301902 650_7 $$0EC 3.1.1.3$$2NLM Chemicals$$aPNPLA3 protein, human
000301902 650_7 $$0EC 3.1.1.3$$2NLM Chemicals$$aLipase
000301902 650_7 $$2NLM Chemicals$$aMembrane Proteins
000301902 650_7 $$0EC 2.3.-$$2NLM Chemicals$$aAcyltransferases
000301902 650_7 $$0EC 3.1.1.4$$2NLM Chemicals$$aPhospholipases A2, Calcium-Independent
000301902 650_2 $$2MeSH$$aHumans
000301902 650_2 $$2MeSH$$aDeep Learning
000301902 650_2 $$2MeSH$$aLipase: genetics
000301902 650_2 $$2MeSH$$aMembrane Proteins: genetics
000301902 650_2 $$2MeSH$$aMagnetic Resonance Imaging
000301902 650_2 $$2MeSH$$aMale
000301902 650_2 $$2MeSH$$aFemale
000301902 650_2 $$2MeSH$$aLiver: diagnostic imaging
000301902 650_2 $$2MeSH$$aMiddle Aged
000301902 650_2 $$2MeSH$$aFatty Liver: genetics
000301902 650_2 $$2MeSH$$aFatty Liver: diagnostic imaging
000301902 650_2 $$2MeSH$$aAdult
000301902 650_2 $$2MeSH$$aGenetic Predisposition to Disease
000301902 650_2 $$2MeSH$$aAged
000301902 650_2 $$2MeSH$$aNon-alcoholic Fatty Liver Disease: genetics
000301902 650_2 $$2MeSH$$aNon-alcoholic Fatty Liver Disease: diagnostic imaging
000301902 650_2 $$2MeSH$$aAcyltransferases
000301902 650_2 $$2MeSH$$aPhospholipases A2, Calcium-Independent
000301902 7001_ $$aLaevens, Benjamin P M$$b1
000301902 7001_ $$aLemainque, Teresa$$b2
000301902 7001_ $$aMüller-Franzes, Gustav Anton$$b3
000301902 7001_ $$00009-0004-9115-3228$$aSeibel, Tobias$$b4
000301902 7001_ $$aDlugosch, Carola$$b5
000301902 7001_ $$aClusmann, Jan$$b6
000301902 7001_ $$aKoop, Paul-Henry$$b7
000301902 7001_ $$aGong, Rongpeng$$b8
000301902 7001_ $$aLiu, Yuanyuan$$b9
000301902 7001_ $$aJakhar, Niharika$$b10
000301902 7001_ $$aCao, Feng$$b11
000301902 7001_ $$00009-0004-4767-9775$$aSchophaus, Simon$$b12
000301902 7001_ $$aRaju, Thriveni Basavanapura$$b13
000301902 7001_ $$aRaptis, Anastasia Artemis$$b14
000301902 7001_ $$00009-0007-4586-7133$$avan Haag, Felix$$b15
000301902 7001_ $$00009-0002-9926-6070$$aJoy, Joel$$b16
000301902 7001_ $$aLoomba, Rohit$$b17
000301902 7001_ $$00000-0001-8909-0345$$aValenti, Luca$$b18
000301902 7001_ $$aKather, Jakob Nikolas$$b19
000301902 7001_ $$0P:(DE-He78)1e33961c8780aca9b76d776d1fdc1ebb$$aBrinker, Titus J$$b20$$udkfz
000301902 7001_ $$aHerzog, Moritz$$b21
000301902 7001_ $$aCosta, Ivan G$$b22
000301902 7001_ $$aHernando, Diego$$b23
000301902 7001_ $$aSchneider, Kai Markus$$b24
000301902 7001_ $$aTruhn, Daniel$$b25
000301902 7001_ $$aSchneider, Carolin V$$b26
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