为了定量评估慢性肾脏病(CKD),将基于深度卷积神经网络的分割模型应用于肾脏增强计算机断层扫描(CT)图像.对100名诊断为CKD的个体和90名健康肾脏的个体进行了回顾性分析。接受肾脏或腹部对比增强CT扫描的患者。收集所有参与者的人口统计学和临床数据。这项研究包括两个不同的阶段:首先,建立并验证了用于分割肾脏增强CT扫描动脉期的三维(3D)nnU-Net模型;其次,利用3DnnU-Net模型对CKD进行定量评估。3DnnU-Net模型的肾实质平均Dice相似系数(DSC)为93.53%,肾皮质为81.48%。肾实质体积(VRP)在肾功能的不同阶段差异有统计学意义。肾皮质体积(VRC),肾髓质体积(VRM),肾实质(HuRP)的CT值,肾皮质(HuRC)的CT值,和肾髓质(HuRM)的CT值(F=93.476,144.918,9.637,170.533,216.616和94.283;p<0.001)。Pearson相关分析显示肾小球滤过率(eGFR)与VRP呈显著正相关,VRC,VRM,HuRP,HuRC,和HuRM(r分别为0.749、0.818、0.321、0.819、0.820和0.747,所有p<0.001)。同样,血清肌酐(Scr)水平与VRP呈负相关,VRC,VRM,HuRP,HuRC,和HuRM(r=-0.759、-0.777、-0.420、-0.762、-0.771和-0.726,所有p<0.001)。为了预测男性的CKD,VRP的曲线下面积(AUC)为0.726,p<0.001;VRC,AUC0.765,p<0.001;VRM,AUC0.578,p=0.018;HuRP,AUC0.912,p<0.001;HuRC,AUC0.952,p<0.001;和HuRM,AUC0.772,男性p<0.001。在女性中,VRP的AUC为0.813,p<0.001;VRC,AUC0.851,p<0.001;VRM,AUC0.623,p=0.060;HuRP,AUC0.904,p<0.001;HuRC,AUC0.934,p<0.001;和HuRM,AUC为0.840,p<0.001。预测HuRPCKD的最佳截止值男性为99.9Hu,女性为98.4Hu,在HuRC,男性为120.1胡,女性为111.8胡。我们基于AI的3DnnU-Net模型对肾脏进行了有效分割,以增强肾脏CT图像。就轻度肾损伤而言,与肾脏体积相比,CT值表现出更高的敏感性.相关分析显示,VRC之间的关联更强,HuRP,和具有肾功能的HuRC,虽然VRP和HuRM之间的联系较弱,VRM之间的关联最弱。特别是,HuRP和HuRC在预测肾功能方面表现出显著的潜力。为了诊断CKD,建议设置阈值如下:男性HuRP<99.9Hu和HuRC<120.1Hu,女性HuRP<98.4Hu,HuRC<111.8Hu。
To quantitatively evaluate chronic kidney disease (CKD), a deep convolutional neural network-based segmentation model was applied to renal enhanced computed tomography (CT) images. A retrospective analysis was conducted on a cohort of 100 individuals diagnosed with CKD and 90 individuals with healthy kidneys, who underwent contrast-enhanced CT scans of the kidneys or abdomen. Demographic and clinical data were collected from all participants. The study consisted of two distinct stages: firstly, the development and validation of a three-dimensional (3D) nnU-Net model for segmenting the arterial phase of renal enhanced CT scans; secondly, the utilization of the 3D nnU-Net model for quantitative evaluation of CKD. The 3D nnU-Net model achieved a mean Dice Similarity Coefficient (DSC) of 93.53% for renal parenchyma and 81.48% for renal cortex. Statistically significant differences were observed among different stages of renal function for renal parenchyma volume (VRP), renal cortex volume (VRC), renal medulla volume (VRM), the CT values of renal parenchyma (HuRP), the CT values of renal cortex (HuRC), and the CT values of renal medulla (HuRM) (F = 93.476, 144.918, 9.637, 170.533, 216.616, and 94.283; p < 0.001). Pearson correlation analysis revealed significant positive associations between glomerular filtration rate (eGFR) and VRP, VRC, VRM, HuRP, HuRC, and HuRM (r = 0.749, 0.818, 0.321, 0.819, 0.820, and 0.747, respectively, all p < 0.001). Similarly, a negative correlation was observed between serum creatinine (Scr) levels and VRP, VRC, VRM, HuRP, HuRC, and HuRM (r = - 0.759, - 0.777, - 0.420, - 0.762, - 0.771, and - 0.726, respectively, all p < 0.001). For predicting CKD in males, VRP had an area under the curve (AUC) of 0.726, p < 0.001; VRC, AUC 0.765, p < 0.001; VRM, AUC 0.578, p = 0.018; HuRP, AUC 0.912, p < 0.001; HuRC, AUC 0.952, p < 0.001; and HuRM, AUC 0.772, p < 0.001 in males. In females, VRP had an AUC of 0.813, p < 0.001; VRC, AUC 0.851, p < 0.001; VRM, AUC 0.623, p = 0.060; HuRP, AUC 0.904, p < 0.001; HuRC, AUC 0.934, p < 0.001; and HuRM, AUC 0.840, p < 0.001. The optimal cutoff values for predicting CKD in HuRP are 99.9 Hu for males and 98.4 Hu for females, while in HuRC are 120.1 Hu for males and 111.8 Hu for females. The kidney was effectively segmented by our AI-based 3D nnU-Net model for enhanced renal CT images. In terms of mild kidney injury, the CT values exhibited higher sensitivity compared to kidney volume. The correlation analysis revealed a stronger association between VRC, HuRP, and HuRC with renal function, while the association between VRP and HuRM was weaker, and the association between VRM was the weakest. Particularly, HuRP and HuRC demonstrated significant potential in predicting renal function. For diagnosing CKD, it is recommended to set the threshold values as follows: HuRP < 99.9 Hu and HuRC < 120.1 Hu in males, and HuRP < 98.4 Hu and HuRC < 111.8 Hu in females.