ROC curve results for different diffusion models.

Purpose
To compare the ability of diffusion parameters obtained by stretched-exponential and kurtosis models of diffusion-weighted imaging (DWI) to distinguish between patients with primary aldosteronism (PA) and healthy controls (HCs) in renal assessment.
Materials and methods
A total of 44 participants (22 patients and 22 HCs) underwent renal MRI with an 11 b-value DWI sequence and a 3 b-value diffusion kurtosis imaging (DKI) sequence from June 2021 to April 2022. Binary logistic regression was used to construct regression models combining different diffusion parameters. Receiver-operating characteristic (ROC) curve analysis and comparisons were used to evaluate the ability of single diffusion parameters and combined diffusion models to distinguish between the two groups.
Results
A total of six diffusion parameters (including the cortical anomalous exponent term [α_Cortex], medullary fractional anisotropy [FA_Medulla], cortical FA [FA_Cortex], cortical axial diffusivity [Da_Cortex], medullary mean diffusivity [MD_Medulla] and medullary radial diffusivity [Dr_Medulla]) were included, and 10 regression models were studied. The area under the curve (AUC) of Dr_Medulla was 0.855, comparable to that of FA_Cortex and FA_Medulla and significantly higher than that of α_Cortex, Da_Cortex and MD_Medulla. The AUC of the Model_all parameters was 0.967, comparable to that of Model_FA (0.946) and Model_DKI (0.966) and significantly higher than that of the other models. The sensitivity and specificity of Model_all parameters were 87.2% and 95%, respectively.
Conclusion
The Model_all parameters, Model_FA and Model_DKI were valid for differentiating between PA patients and HCs with similar differentiation efficacy and were superior to single diffusion parameters and other models.

**研究目的** 本研究旨在比较扩散加权成像（DWI）的拉伸指数模型与峰度模型所获扩散参数在肾脏评估中区分原发性醛固酮增多症（PA）患者与健康对照者（HCs）的能力。

**材料与方法** 本研究于2021年6月至2022年4月期间纳入44名受试者（22名PA患者与22名健康对照者），所有受试者均接受肾脏MRI检查，扫描序列包含11个b值的DWI序列与3个b值的扩散峰度成像（DKI）序列。采用二元logistic回归构建融合不同扩散参数的回归模型；通过受试者工作特征（ROC）曲线分析及组间比较，评估单一扩散参数与联合扩散模型区分两组受试者的能力。

**研究结果** 本研究共纳入6项扩散参数，包括皮层异常指数项（α_Cortex）、髓质各向异性分数（FA_Medulla）、皮层各向异性分数（FA_Cortex）、皮层轴向扩散系数（Da_Cortex）、髓质平均扩散系数（MD_Medulla）及髓质径向扩散系数（Dr_Medulla），并构建了10种回归模型进行分析。其中髓质径向扩散系数（Dr_Medulla）的曲线下面积（AUC）为0.855，其区分效能与FA_Cortex、FA_Medulla相当，且显著高于α_Cortex、Da_Cortex及MD_Medulla。全参数联合模型（Model_all）的AUC为0.967，与FA联合模型（Model_FA，0.946）及DKI联合模型（Model_DKI，0.966）的AUC无显著差异，但显著高于其余所有模型。全参数联合模型的灵敏度与特异度分别为87.2%与95%。

**研究结论** 全参数联合模型、FA联合模型及DKI联合模型均可有效区分PA患者与健康对照者，三者的区分效能相近，且均优于单一扩散参数及其他联合模型。