Pan Zhongxian, Chen Qiuyi, Lin Haiwei, Huang Wensheng, Li Junfeng, Meng Fanqi, Zhong Zhangnan, Liu Wenxi, Li Zhujing, Qin Haodong, Huang Bingsheng, Chen Yueyao
Department of Radiology, Shenzhen Traditional Chinese Medicine Hospital (The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine), Shenzhen, China.
Medical AI Lab, School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen, China.
Abdom Radiol (NY). 2025 Jan 22. doi: 10.1007/s00261-025-04804-3.
Intra-pancreatic fat deposition (IPFD) is closely associated with the onset and progression of type 2 diabetes mellitus (T2DM). We aimed to develop an accurate and automated method for assessing IPFD on multi-echo Dixon MRI.
In this retrospective study, 534 patients from two centers who underwent upper abdomen MRI and completed multi-echo and double-echo Dixon MRI were included. A pancreatic segmentation model was trained on double-echo Dixon water images using nnU-Net. Predicted masks were registered to the proton density fat fraction (PDFF) maps of the multi-echo Dixon sequence. Deep semantic segmentation feature-based radiomics (DSFR) and radiomics features were separately extracted on the PDFF maps and modeled using the support vector machine method with 5-fold cross-validation. The first deep learning radiomics (DLR) model was constructed to distinguish T2DM from non-diabetes and pre-diabetes by averaging the output scores of the DSFR and radiomics models. The second DLR model was then developed to distinguish pre-diabetes from non-diabetes. Two radiologist models were constructed based on the mean PDFF of three pancreatic regions of interest.
The mean Dice similarity coefficient for pancreas segmentation was 0.958 in the total test cohort. The AUCs of the DLR and two radiologist models in distinguishing T2DM from non-diabetes and pre-diabetes were 0.868, 0.760, and 0.782 in the training cohort, and 0.741, 0.724, and 0.653 in the external test cohort, respectively. For distinguishing pre-diabetes from non-diabetes, the AUCs were 0.881, 0.688, and 0.688 in the training cohort, which included data combined from both centers. Testing was not conducted due to limited pre-diabetic patients. Intraclass correlation coefficients between radiologists' pancreatic PDFF measurements were 0.800 and 0.699 at two centers, suggesting good and moderate reproducibility, respectively.
The DLR model demonstrated superior performance over radiologists, providing a more efficient, accurate and stable method for monitoring IPFD and predicting the risk of T2DM and pre-diabetes. This enables IPFD assessment to potentially serve as an early biomarker for T2DM, providing richer clinical information for disease progression and management.
胰腺内脂肪沉积（IPFD）与2型糖尿病（T2DM）的发生和发展密切相关。我们旨在开发一种准确且自动化的方法，用于在多回波狄克逊磁共振成像（MRI）上评估IPFD。
在这项回顾性研究中，纳入了来自两个中心的534例接受上腹部MRI检查并完成多回波和双回波狄克逊MRI检查的患者。使用nnU-Net在双回波狄克逊水图像上训练胰腺分割模型。将预测的掩码配准到多回波狄克逊序列的质子密度脂肪分数（PDFF）图上。分别在PDFF图上提取基于深度语义分割特征的放射组学（DSFR）和放射组学特征，并使用支持向量机方法和五折交叉验证进行建模。通过平均DSFR和放射组学模型的输出分数，构建第一个深度学习放射组学（DLR）模型，以区分T2DM与非糖尿病和糖尿病前期。然后开发第二个DLR模型，以区分糖尿病前期与非糖尿病。基于三个胰腺感兴趣区域的平均PDFF构建两个放射科医生模型。
在整个测试队列中，胰腺分割的平均骰子相似系数为0.958。在训练队列中，DLR模型和两个放射科医生模型区分T2DM与非糖尿病和糖尿病前期的曲线下面积（AUC）分别为0.868、0.760和0.782，在外部测试队列中分别为0.741、0.724和0.653。对于区分糖尿病前期与非糖尿病，在包括两个中心合并数据的训练队列中，AUC分别为0.881、0.688和0.688。由于糖尿病前期患者数量有限，未进行测试。两个中心放射科医生对胰腺PDFF测量的组内相关系数分别为0.800和0.699，分别表明良好和中等的可重复性。
DLR模型表现出优于放射科医生的性能，为监测IPFD以及预测T2DM和糖尿病前期风险提供了一种更高效、准确和稳定的方法。这使得IPFD评估有可能成为T2DM的早期生物标志物，为疾病进展和管理提供更丰富的临床信息。
