In clinical practice, Scr is a convenient indicator for assessing renal function. The simplest method to assess the glomerular filtration rate is by using formulas based on Scr12. Formulas widely used to estimate eGFR include Cockcroft-Gault13, MDRD14, and CKD-EPI10. Currently, the most commonly used formula, CKD-EPI, demonstrates superior accuracy compared to the MDRD formula in evaluating GFR, especially when the GFR is exceeds 60 ml/min/(1.73 m2).10 However, it should be noted that kidneys possess strong reserve function. In patients with asymmetric renal disease such as renal tumors, obstruction, and infections, eGFR tends to remain normal even in the presence of severe unilateral damage. Timely identification of renal impairment can guide patient management by regulating elevated blood pressure and preventing potential consequences like cardiovascular disease, skeletal disease, and hypertension15.
In routine review of renal images, the radiologist focuses on renal space-occupying lesions in the kidneys, such as renal tumors and cysts. However, there is insufficient attention given to changes in the thickness of the renal cortex and enlargement of the renal parenchyma. When these changes are a concern for the radiologist, it usually indicates severe damage to kidney function. Additionally, distinguishing between mild and moderate renal impairment with just visual observation is difficult. Integrating radiomics with AI in clinical practice has significant potential for enhancing assisted medical diagnosis. Renal fibrosis plays a crucial role in the progression of CKD. Therefore, analyzing the texture of the renal cortex can be used to predict renal function accurately. The critical issue that needs to be addressed for kidney texture analysis is kidney segmentation. Manual delineation of renal tissue is time-consuming and subjective, leading to individual differences in results. Segmentation is a critical step in analyzing abdominal images16. The radiologist qualitatively examines and obtains information by segmenting the kidney using fully automated software tools.
Several studies have been conducted on AI-assisted medical image segmentation for kidney disease. Bevilacqua et al.17 showed an 86% accuracy in segmenting MR images of kidneys with autosomal dominant polycystic kidney disease (ADPKD). Yin et al.18 developed a novel deep neural network for boundary distance regression to segment the kidneys. The abnormal kidney images are from children with congenital abnormalities of the kidney and urinary tract. The DSC and accuracy achieved were 94% and 98.9%, respectively. Sharma et al.19 utilized an a deep learning-based automated segmentation method on a CT dataset of ADPKD patients exhibiting RI, resulting in an overall mean DSC of 0.86. Cruz et al.20 employed deep neural networks on the KITS public dataset to undertake kidney and tumor segmentation, utilizing a three-step approach. Initially, they utilized AlexNet to narrow down the image scope, followed by performing coarse segmentation of kidneys and subsequently refining the segmentation using U-Net. Korfiatis et al.21 proposed a fully automated framework for kidney segmentation based on CNNs, achieving a DSC greater than 0.9 for both left and right kidneys. Turco et al.22 employed a multiphase level set framework in conjunction with an automated detection mechanism to enable the fully automated computation of total kidney volume on CT images. The nnU-Net framework offers a significant advantage by enabling effective learning from a limited number of annotated images, owing to its innovative pre-processing pipeline. The nnU-Net segmentation framework, TotalSegmentator, used by Wasserthal et al.23, has been widely employed for accurate and reliable whole-body segmentation (including the kidney), demonstrating excellent performance. The TotalSegmentator demonstrated a high DSC of 0.943, surpassing the performance of other freely available segmentation tools. Our study’s 3D nnU-Net segmentation model achieved a mean DSC of 93.53% and a mean accuracy of 99.95% for renal parenchyma. The mean DSC value for the renal cortex was 81.48%, with an accuracy of 99.92%. The model demonstrated excellent discrimination for pure cysts, high-density cysts, fat in the renal sinus, renal stones, and calcifications in the renal vasculature, as depicted in Fig. 3. In conclusion, the AI based on the framework of the 3D nnU-Net model can effectively segment kidney enhanced CT images and exhibit good capabilities.
Many studies have investigated the relationship between enhanced CT kidney and renal function, all of which have shown a significant correlation between GFR and renal cortical CT enhancement values as well as renal cortical thickness24,25,26. Most of these studies measure the thickness and CT value of renal parenchyma and cortex by selecting an area in the cross-section of renal CT images. However, this method has some limitations due to potential manual measurement errors. The renal cortical and parenchymal thicknesses sometimes reflect renal morphology but ultimately do not represent renal volume. This is because the larger the kidney volume, the greater the difference between thickness and volume. In patients with ESRD, where the renal cortex becomes very thin, measurement errors are further increased. To address these issues, this study utilized AI to develop a 3D nnU-Net model for segmentation of the renal parenchyma and cortex. This approach effectively reduces labor expenses while enabling comprehensive evaluation of the kidney. The segmenting process for a patient’s kidney image now only takes 2–3 min. Table 3 revealed that in the early stage of RI, there was an increase in the volume of renal parenchyma, cortex, and medulla. However, subgroup analysis (Fig. 4) showed no statistically significant difference in renal parenchymal volume, cortex volume, or medullary volume between the normal control group and the mild RI group. Renal volumes, including parenchymal, cortical, and medullary volumes, showed statistically significant differences between the severe RI group and the other groups. Regarding renal cortical volume, there were statistically significant differences in pairwise comparisons among the four groups, except for the normal control group and mild RI group. This suggested that relying solely on renal parenchymal volume size for assessing renal function is unreliable, while renal cortical volume serves as a relatively favorable indicator for moderate to severe RI. Kuo et al.27 constructed a model for automatically estimating eGFR using renal ultrasound images. The accuracy of the CKD status classification was 85.6%, higher than that of experienced nephrologists (60.3–80.1%). The specificity was high (92.1%), and the sensitivity was moderate (60.7%). The study included 4505 CKD patients with eGFR < 60 ml/min/1.73 m2, but the validity of this model for early-stage CKD patients is unknown. In our study, HuRP and HuRC showed statistically significant differences between the normal group and other RI groups, suggesting that these indicators could detect renal impairment as early as possible (Fig. 4). There was no difference in HuRm between the normal control group and the mild RI group. As the kidney is mildly damaged, it alters glomerular basement membrane permeability, causing proteins to leak out of the damaged filtration barrier into the renal interstitium. This leads to interstitial edema and thickening of the renal cortex. Therefore, in the early stages of RI, there is a decrease in CT values of both the renal cortex and renal parenchyma. The renal cortex exhibits greater sensitivity than the medulla in CKD. Renal function is closely related to the mass and number of glomeruli within the renal cortex, which directly reflected its thickness or volume28. CKD is usually characterized by interstitial fibrosis and tubular atrophy with or without elevated Scr9. Early injury is located in the basement membrane of the glomerulus; this would result in an inhomogeneous enhancement of the renal cortex, which is not recognizable to the naked eye. These pathological changes can be reflected in the heterogeneity of renal tissue’s texture, volume, and shape in digital medical images29. Zhang et al.30 investigated the feasibility of using texture analysis based on the apparent diffusion coefficient and T1 and T2 maps to evaluate renal function. This approach is feasible and relatively accurate for assessing renal function. AI-assisted CKD diagnosis based on ultrasound imaging integrated with computer-extracted measurable features31. Chantaduly et al.32 developed two different CNNs models for renal CT images that can differentiate between mild and severe fibrosis, achieving an accuracy of over 85% for both classifications. Importantly, there were no statistically significant difference in HuRP, HuRC, and HuRM values between the mild RI group and the moderate RI group. Glomerular fibrosis, renal atrophy, and thinning of the renal cortex and medulla occur in ESRD. Consequently, the severe RI group exhibited significant differences in volume and CT values compared to the other group. It is worth emphasizing that only VRC showed a statistically significant difference among all indicators between mild RI and moderate RI.
The correlation analysis of VRP, VRC, VRM, HuRP, HuRC, and HuRM with renal function was performed. The subgroup analysis was performed based on gender, and no statistically significant difference was observed between males and females. The results suggested that VRC, HuRP, and HuRC could effectively reflect renal function. ROC analysis is a widely used statistical method in clinical diagnostic studies. The results of this study showed that HuRC and HuRP had the highest accuracy in predicting CKD. We recommended using HuRC and HuRP as valid indicators for evaluating CKD.
AI is used to construct the model, automatically segmenting renal parenchymal and cortical volumes, which saves labor costs and allows for a comprehensive assessment of the kidney. AI-assisted medical imaging techniques significantly reduce physician workload and improve efficiency. However, there are some deficiencies in this study: 1) This study is a single-center study with insufficient sample size, which may lead to bias in the results. Influenced by the sample size, the numbers of CKD stage 1 and 4 patients are relatively small, which may impact the results. 2) The accuracy of automatic segmentation of AI image may be affected because the CT images use a layer thickness of 5 mm. 3) This study did not fully consider the influence of patients’ heart function on the augmentation of the renal cortical phase. 4) CKD patients are routinely not recommended for iodine contrast enhancement due to impaired renal function. This study is retrospective, the study’s conclusions still need to be validated by further large samples. 5) The subjects of this study exclude those with unilateral hydronephrosis, patients with severe hydronephrosis, polycystic kidneys, renal tumors, and other diseases for which AI segmentation still needs to be further improved. Preliminary findings from this study suggest that the renal cortex images are of significant clinical importance in patients with mild RI, helping to diagnose renal disease at an early stage and thereby delaying the progression of CKD as long as possible.
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- Source: https://www.nature.com/articles/s41598-024-67658-7