肝脏超声模型在磁共振质子密度脂肪分数评估中的应用

肝脏 ›› 2025, Vol. 30 ›› Issue (10): 1393-1397.

肝脏超声模型在磁共振质子密度脂肪分数评估中的应用

葛海龙, 巢晨, 侍兴松, 白崔巍, 王玉, 尹琪

213200 常州江苏大学附属金坛医院肝胆外科(葛海龙,巢晨, 侍兴松,王玉),放射科(白崔巍),麻醉科(尹琪)

收稿日期:2024-09-25 出版日期:2025-10-31 发布日期:2025-12-16
通讯作者: 尹琪,Email:525257061@qq.com;王玉,Email:ywang030608@stu.suda.edu.cn
基金资助:
2022、2023年度江苏大学医教协同创新基金(JDY2022018、JDYY2023042);2021年度常州市卫健委科技项目(QN202139);2023年常州市卫健委科技项目前沿计划(QY202309)

Deep learning-based ultrasound models in the evaluation of magnetic resonance imaging-proton density fat fraction

GE Hai-long¹, CHAO Chen¹, SHI Xing-song¹, BAI Cui-wei², WANG Yu¹, YIN Qi³

1. Department of Hepatobiliary Surgery Jintan Affiliated Hospital of Jiangsu University, Changzhou 213200, China;
2. Department of Radiology Jintan Affiliated Hospital of Jiangsu University, Changzhou 213200, China;
3. Department of Anesthesiology Jintan Affiliated Hospital of Jiangsu University, Changzhou 213200, China

Received:2024-09-25 Online:2025-10-31 Published:2025-12-16
Contact: YIN Qi,Email:525257061@qq.com;WANG Yu,Email:ywang030608@stu.suda.edu.cn

摘要/Abstract

摘要： 目的了解肝脏超声模型在质子密度脂肪分数(PDFF)评估中的作用。方法纳入2020年2月至2024年1月江苏大学附属金坛医院就诊的患者360例。在1周内完成空腹状态下肝脏超声和MRI-PDFF检测。分别建立①回归模型:量化肝脏脂肪变性程度,以连续变量PDFF值作为预测数据标签;②分类模型:基于PDFF值三个截取值(5%、10%及25%)建立分类器,诊断并评估严重程度。结果剑突下纵切、第二肝门和肝肾切面三个切面的深度学习模型预测值分别与PDFF实际值进行Spearman相关性分析,结果显示三者预测值与实际值密切相关(r值分别为0.860,0.922和0.903)。最终,三切面平均值与PDFF实际值相关性r值达到0.961。剑突下纵切、第二肝门和肝肾切面三个切面的深度学习模型对以PDFF 5%作为截取值的二分类,结果显示三者分类效果显著(AUC分别为0.938,0.946和0.959)。而三切面平均值与PDFF 5%(是否脂肪肝)二分类的AUC达到0.970。以PDFF 10%(是否需要干预)作为截取值的二分类,三个切面分类效果同样显著(AUC分别为0.974,0.991和0.997),而三者均值的AUC达到了0.998。以PDFF 25%(是否为重度)作为截取值的二分类,三个切面分类效果依然显著(AUC分别为0.904,0.958和0.936)。结论本研究基于深度学习算法建立的超声图像脂肪肝分类和量化模型,在脂肪肝的诊疗过程中可提高效率、降低成本,具有临床应用前景。

关键词: 非酒精性脂肪性肝病, 化学位移编码磁共振成像, 质子密度脂肪分数, 超声, 深度学习, 迁移学习

Abstract: Objective The use of Proton Density Fat Fraction (PDFF) in the diagnosis of fatty liver has been limited by its high cost and limited availability. This study aimed to develop a liver ultrasound model using deep learning for the evaluation of fatty liver severity and steatosis assessment based on PDFF. Methods Patients whose liver ultrasound and MRI-PDFF examinations were completed within one week in our hospital were included in the study. Two models were developed: 1) a regression model: quantifying the degree of liver steatosis, with continuous variable PDFF values as predictive data labels; 2) a classification model: classifiers were developed based on three threshold values of PDFF (5%, 10%, and 25%) to qualitatively diagnose and assess the severity of fatty liver. Results Predictions from the regression model based on three views (subcostal longitudinal, second liver portal, and hepatorenal interface) were correlated with actual PDFF values using Spearman correlation analysis. The results showed a close correlation between the predicted values and actual values (r values of 0.860, 0.922, and 0.903, respectively). Moreover, the average value of the three views correlated with the actual PDFF value with an r value of 0.961. The classification model based on the three views for binary classification using a PDFF 5% as the threshold showed significant performance (AUCs of 0.938, 0.946, and 0.959, respectively). The average value of the three views achieved an AUC of 0.970. As PDFF 10% as the threshold, the performance was also significant (AUCs of 0.974, 0.991, and 0.997), with the average achieving 0.998. As PDFF 25% as the threshold, the three AUCs remained significant (0.904, 0.958, and 0.936), with the average AUC being 0.992. Finally, to visualize the deep learning model in liver ultrasound prediction, we used Gradient-weighted Class Activation Mapping for visual interpretation of the convolutional neural network. Conclusion This study proposes deep learning-based ultrasound image classification and quantification models for fatty liver. These models have the potential to improve efficiency and reduce costs in the diagnosis and treatment of fatty liver, and shows significant clinical application prospects.

Key words: Nonalcoholic fatty liver disease, Chemical-shift-encoded magnetic resonance imaging (CSE-MRI), Proton density fat fraction (PDFF), Ultrasound, Deep learning, Transfer learning

葛海龙, 巢晨, 侍兴松, 白崔巍, 王玉, 尹琪. 肝脏超声模型在磁共振质子密度脂肪分数评估中的应用[J]. 肝脏, 2025, 30(10): 1393-1397.

GE Hai-long, CHAO Chen, SHI Xing-song, BAI Cui-wei, WANG Yu, YIN Qi. Deep learning-based ultrasound models in the evaluation of magnetic resonance imaging-proton density fat fraction[J]. Chinese Hepatolgy, 2025, 30(10): 1393-1397.

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