Dai Ruihong, Sun Miaomiao, Lu Mei, Deng Lanhua
Department of Ultrasound, Meng Cheng County Hospital of Chinese Medicine, Bozhou City, Anhui Province, China.
Heliyon. 2024 Jul 5;10(13):e34150. doi: 10.1016/j.heliyon.2024.e34150. eCollection 2024 Jul 15.
Metabolic dysfunction-associated steatotic liver disease (MASLD) significantly impacts patients with type 2 diabetes mellitus (T2DM), where current non-invasive assessment methods show limited predictive power for future fibrotic progression. This study aims to develop an enhanced deep learning (DL) model that integrates ultrasound elastography images with clinical data, refining the prediction of fibrotic progression in T2DM patients with MASLD who initially exhibit no signs of hepatic fibrosis.
We enrolled 946 diabetic MASLD patients without advanced fibrosis, confirmed by initial liver stiffness measurements (LSM) below 6.5 kPa. Patients were divided into a training dataset of 671 and a testing dataset of 275. Hepatic shear wave elastography (SWE) images measured liver stiffness, classifying participants based on progression. A DL integrated model (DI-model) combining SWE images and clinical data was trained and its predictive performance compared with individual Image and Tabular models, as well as a logistic regression model on the testing dataset.
Fibrotic progression was observed in 18.1 % of patients over three years. During the training phase, the DI-model outperformed other models, achieving the lowest validation loss of 0.161 and highest accuracy of 0.933 through cross-validation. In the testing phase, it demonstrated robust discrimination with AUCs of 0.884 and 0.903 for the receiver operating characteristic and precision-recall curves, respectively, clearly outperforming other models. Shapley analysis identified BMI, LSM, and glycated hemoglobin as critical predictors.
The DI-model significantly enhances the prediction of future fibrotic progression in diabetic MASLD patients, demonstrating the benefit of combining clinical and imaging data for early diagnosis and intervention.
代谢功能障碍相关脂肪性肝病（MASLD）对2型糖尿病（T2DM）患者有显著影响，目前的非侵入性评估方法对未来纤维化进展的预测能力有限。本研究旨在开发一种增强的深度学习（DL）模型，该模型将超声弹性成像图像与临床数据相结合，以优化对最初无肝纤维化迹象的T2DM合并MASLD患者纤维化进展的预测。
我们纳入了946例无晚期纤维化的糖尿病MASLD患者，通过初始肝脏硬度测量（LSM）低于6.5 kPa确诊。患者被分为671例的训练数据集和275例的测试数据集。肝脏剪切波弹性成像（SWE）图像测量肝脏硬度，并根据进展情况对参与者进行分类。训练了一个结合SWE图像和临床数据的DL集成模型（DI模型），并在测试数据集上将其预测性能与单独的图像和表格模型以及逻辑回归模型进行比较。
在三年期间，18.1%的患者出现纤维化进展。在训练阶段，DI模型优于其他模型，通过交叉验证实现了最低验证损失0.161和最高准确率0.933。在测试阶段，它在受试者工作特征曲线和精确召回率曲线的AUC分别为0.884和0.903，显示出强大的区分能力，明显优于其他模型。Shapley分析确定体重指数、LSM和糖化血红蛋白为关键预测因素。
DI模型显著增强了对糖尿病MASLD患者未来纤维化进展的预测，证明了结合临床和影像数据进行早期诊断和干预的益处。
