AI-based segmentation of renal enhanced CT images for quantitative evaluate of chronic kidney disease.

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.

The Prognosis and Immune Microenvironment Related Factor Interleukin-23 in Clear Cell Renal Cell Carcinoma: A Radiological Investigation.

PURPOSE: To explore the ccRCC clinical and immune characteristics correlated with IL-23 expression level and build pre-operative prediction models based on contrast CT scans. MATERIALS AND METHODS: The study included the cancer genome atlas kidney renal clear cell carcinoma cases to build a bioinformatics cohort. The cases with qualified contrast CT images were selected as radiographic and radiomics cohort. The IL-23 expression level groups were defined by median-based thresholding. The clinical characteristics were compared between groups. The impacts of IL-23 on immune microenvironment composition were measured via the CIBERSORT. Two radiologists evaluated the pre-operative contrast CT images. The radiomics features were automatically extracted. IL-23 group-specific radiographic and radiomics features were collected and used for prediction model establishment via Orange Data Mining Toolbox. P < 0.05 was set as statistically significant. RESULTS: For total, 530 ccRCC cases were included. The IL-23 group was significantly associated with survival, histologic grade, AJCC tumor stage, AJCC cancer stage, and plasma calcium level. Except for Treg and other T cells, IL-23 showed correlation with NK cell, mast cell, monocyte infiltration. Axial length was the only significant radiographic measurement between IL-23 groups. The radiomics features established an IL-23 group prediction model with the highest 10-fold cross-verification AUC of 0.842. CONCLUSION: The clear cell renal cell carcinoma IL-23 expression level had prognosis and immune microenvironment correlation and could be predicted by pre-operative radiomics features.