Michel Lea J, Rospleszcz Susanne, Reisert Marco, Rau Alexander, Nattenmueller Johanna, Rathmann Wolfgang, Schlett Christopher L, Peters Annette, Bamberg Fabian, Weiss Jakob
Department of Diagnostic and Interventional Radiology, University Hospital Freiburg, Freiburg, Germany.
Department of Epidemiology, Institute for Medical Information Processing, Biometry, and Epidemiology, Ludwig-Maximilians-University Munich, Munich, Germany.
PLOS Digit Health. 2024 Jan 16;3(1):e0000429. doi: 10.1371/journal.pdig.0000429. eCollection 2024 Jan.
Diabetes is a global health challenge, and many individuals are undiagnosed and not aware of their increased risk of morbidity/mortality although dedicated tests are available, which indicates the need for novel population-wide screening approaches. Here, we developed a deep learning pipeline for opportunistic screening of impaired glucose metabolism using routine magnetic resonance imaging (MRI) of the liver and tested its prognostic value in a general population setting.
In this retrospective study a fully automatic deep learning pipeline was developed to quantify liver shape features on routine MR imaging using data from a prospective population study. Subsequently, the association between liver shape features and impaired glucose metabolism was investigated in individuals with prediabetes, type 2 diabetes and healthy controls without prior cardiovascular diseases. K-medoids clustering (3 clusters) with a dissimilarity matrix based on Euclidean distance and ordinal regression was used to assess the association between liver shape features and glycaemic status.
The deep learning pipeline showed a high performance for liver shape analysis with a mean Dice score of 97.0±0.01. Out of 339 included individuals (mean age 56.3±9.1 years; males 58.1%), 79 (23.3%) and 46 (13.6%) were classified as having prediabetes and type 2 diabetes, respectively. Individuals in the high risk cluster using all liver shape features (n = 14) had a 2.4 fold increased risk of impaired glucose metabolism after adjustment for cardiometabolic risk factors (age, sex, BMI, total cholesterol, alcohol consumption, hypertension, smoking and hepatic steatosis; OR 2.44 [95% CI 1.12-5.38]; p = 0.03). Based on individual shape features, the strongest association was found between liver volume and impaired glucose metabolism after adjustment for the same risk factors (OR 1.97 [1.38-2.85]; p<0.001).
Deep learning can estimate impaired glucose metabolism on routine liver MRI independent of cardiometabolic risk factors and hepatic steatosis.
糖尿病是一项全球性的健康挑战，尽管有专门的检测方法，但许多人未被诊断出来，也未意识到自己发病/死亡风险的增加，这表明需要新的全人群筛查方法。在此，我们开发了一种深度学习流程，用于利用肝脏的常规磁共振成像（MRI）对葡萄糖代谢受损进行机会性筛查，并在一般人群中测试其预后价值。
在这项回顾性研究中，利用一项前瞻性人群研究的数据，开发了一种全自动深度学习流程，以量化常规磁共振成像上的肝脏形状特征。随后，在患有糖尿病前期、2型糖尿病的个体以及无心血管疾病史的健康对照中，研究肝脏形状特征与葡萄糖代谢受损之间的关联。使用基于欧几里得距离的相异矩阵的K-中心点聚类（3个聚类）和有序回归来评估肝脏形状特征与血糖状态之间的关联。
深度学习流程在肝脏形状分析方面表现出高性能，平均Dice评分为97.0±0.01。在纳入的339名个体（平均年龄56.3±9.1岁；男性占58.1%）中，分别有79名（23.3%）和46名（13.6%）被分类为患有糖尿病前期和2型糖尿病。在调整了心脏代谢危险因素（年龄、性别、体重指数、总胆固醇、饮酒、高血压、吸烟和肝脂肪变性）后，使用所有肝脏形状特征的高风险聚类中的个体（n = 14）葡萄糖代谢受损风险增加了2.4倍（比值比2.44 [95%置信区间1.12 - 5.38]；p = 0.03）。基于个体形状特征，在调整相同风险因素后，发现肝脏体积与葡萄糖代谢受损之间的关联最强（比值比1.97 [1.38 - 2.85]；p<0.001）。
深度学习能够在常规肝脏MRI上独立于心脏代谢危险因素和肝脂肪变性来评估葡萄糖代谢受损情况。
