Department of Diagnostic and Interventional Radiology, University Hospital, University of Tübingen, Tübingen, Germany.
Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
Acad Radiol. 2021 Nov;28 Suppl 1:S1-S10. doi: 10.1016/j.acra.2020.06.030. Epub 2020 Aug 14.
To investigate radiomics features of hepatic fat as potential biomarkers of type 2 diabetes mellitus (T2DM) and metabolic syndrome (MetS) in individuals without overt cardiovascular disease, and benchmarking against hepatic proton density fat fraction (PDFF) and the body mass index (BMI).
This study collected liver radiomics features of 310 individuals that were part of a case-controlled imaging substudy embedded in a prospective cohort. Individuals had known T2DM (n = 39; 12.6 %) and MetS (n = 107; 34.5 %) status, and were divided into stratified training (n = 232; 75 %) and validation (n = 78; 25 %) sets. Six hundred eighty-four MRI radiomics features were extracted for each liver volume of interest (VOI) on T-weighted dual-echo Dixon relative fat water content (rfwc) maps. Test-retest and inter-rater variance was simulated by additionally extracting radiomics features using noise augmented rfwc maps and deformed volume of interests. One hundred and seventy-one features with test-retest reliability (ICC(1,1)) and inter-rater agreement (ICC(3,k)) of ≥0.85 on the training set were considered stable. To construct predictive random forest (RF) models, stable features were filtered using univariate RF analysis followed by sequential forward aggregation. The predictive performance was evaluated on the independent validation set with area under the curve of the receiver operating characteristic (AUROC) and balanced accuracy (Accuracy).
On the validation set, the radiomics RF models predicted T2DM with AUROC of 0.835 and Accuracy of 0.822 and MetS with AUROC of 0.838 and Accuracy of 0.787, outperforming the RF models trained on the benchmark parameters PDFF and BMI.
Hepatic radiomics features may serve as potential imaging biomarkers for T2DM and MetS.
本研究旨在探讨无明显心血管疾病个体中肝脏脂肪的放射组学特征是否可作为 2 型糖尿病（T2DM）和代谢综合征（MetS）的潜在生物标志物，并与质子密度脂肪分数（PDFF）和体重指数（BMI）进行基准比较。
本研究纳入了一项前瞻性队列研究的影像学子研究中的 310 名个体的肝脏放射组学特征。这些个体已知患有 T2DM（n=39；12.6%）和 MetS（n=107；34.5%），并分为分层训练集（n=232；75%）和验证集（n=78；25%）。在 T1 加权双回波 Dixon 相对水脂含量（rfwc）图上对每个感兴趣的肝脏体积（VOI）提取了 684 个 MRI 放射组学特征。通过使用噪声增强 rfwc 图和变形 VOI 额外提取放射组学特征，模拟了测试-再测试和观察者间的方差。在训练集上具有测试-再测试可靠性（ICC(1,1)）和观察者间一致性（ICC(3,k)）≥0.85 的 171 个特征被认为是稳定的。为了构建预测随机森林（RF）模型，使用单变量 RF 分析对稳定特征进行过滤，然后进行顺序前向聚合。使用验证集的曲线下面积（AUROC）和平衡准确性（Accuracy）来评估预测性能。
在验证集上，放射组学 RF 模型预测 T2DM 的 AUROC 为 0.835，Accuracy 为 0.822，预测 MetS 的 AUROC 为 0.838，Accuracy 为 0.787，优于基于基准参数 PDFF 和 BMI 训练的 RF 模型。
肝脏放射组学特征可能是 T2DM 和 MetS 的潜在影像学生物标志物。
