AI-based automatic estimation of single-kidney glomerular filtration rate and split renal function using non-contrast CT
Abstract Objectives To address SPECT’s radioactivity, complexity, and costliness in measuring renal function, this study employs artificial intelligence (AI) with non-contrast CT to estimate single-kidney glomerular filtration rate (GFR) and split renal function (SRF). Methods 245 patients with atro...
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SpringerOpen
2025-04-01
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| Series: | Insights into Imaging |
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| Online Access: | https://doi.org/10.1186/s13244-025-01959-x |
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| author | Yiwei Wang Feng Xu Qiuyue Han Daoying Geng Xin Gao Bin Xu Wei Xia |
| author_facet | Yiwei Wang Feng Xu Qiuyue Han Daoying Geng Xin Gao Bin Xu Wei Xia |
| author_sort | Yiwei Wang |
| collection | DOAJ |
| description | Abstract Objectives To address SPECT’s radioactivity, complexity, and costliness in measuring renal function, this study employs artificial intelligence (AI) with non-contrast CT to estimate single-kidney glomerular filtration rate (GFR) and split renal function (SRF). Methods 245 patients with atrophic kidney or hydronephrosis were included from two centers (Training set: 128 patients from Center I; Test set: 117 patients from Center II). The renal parenchyma and hydronephrosis regions in non-contrast CT were automatically segmented by deep learning. Radiomic features were extracted and combined with clinical characteristics using multivariable linear regression (MLR) to obtain a radiomics-clinical-estimated GFR (rcGFR). The relative contribution of single-kidney rcGFR to overall rcGFR, the percent renal parenchymal volume, and the percent renal hydronephrosis volume were combined by MLR to generate the estimation of SRF (rcphSRF). The Pearson correlation coefficient (r), mean absolute error (MAE), and Lin’s concordance coefficient (CCC) were calculated to evaluate the correlations, differences, and agreements between estimations and SPECT-based measurements, respectively. Results Compared to manual segmentation, deep learning-based automatic segmentation could reduce the average segmentation time by 434.6 times to 3.4 s. Compared to single-kidney GFR measured by SPECT, the rcGFR had a significant correlation of r = 0.75 (p < 0.001), MAE of 10.66 mL/min/1.73 m2, and CCC of 0.70. Compared to SRF measured by SPECT, the rcphSRF had a significant correlation of r = 0.92 (p < 0.001), MAE of 7.87%, and CCC of 0.88. Conclusions The non-contrast CT and AI methods are feasible to estimate single-kidney GFR and SRF in patients with atrophic kidney or hydronephrosis. Critical relevance statement For patients with an atrophic kidney or hydronephrosis, non-contrast CT and artificial intelligence methods can be used to estimate single-kidney glomerular filtration rate and split renal function, which may minimize the radiation risk, enhance diagnostic efficiency, and reduce costs. Key Points Renal function can be assessed using non-contrast CT and AI. Estimated renal function significantly correlated with the SPECT-based measurements. The efficiency of renal function estimation can be refined by the proposed method. Graphical Abstract |
| format | Article |
| id | doaj-art-b9154b93c0cc4432b45c4b0c987ba290 |
| institution | DOAJ |
| issn | 1869-4101 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Insights into Imaging |
| spelling | doaj-art-b9154b93c0cc4432b45c4b0c987ba2902025-08-20T03:06:54ZengSpringerOpenInsights into Imaging1869-41012025-04-0116111110.1186/s13244-025-01959-xAI-based automatic estimation of single-kidney glomerular filtration rate and split renal function using non-contrast CTYiwei Wang0Feng Xu1Qiuyue Han2Daoying Geng3Xin Gao4Bin Xu5Wei Xia6Department of Urology, Ninth People’s Hospital, School of Medicine, Shanghai Jiao Tong UniversityDepartment of Nuclear Medicine, Ninth People’s Hospital, School of Medicine, Shanghai Jiao Tong UniversityDepartment of Radiology, Huashan Hospital, Fudan UniversityDepartment of Radiology, Huashan Hospital, Fudan UniversityMedical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of SciencesDepartment of Urology, Ninth People’s Hospital, School of Medicine, Shanghai Jiao Tong UniversityMedical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of SciencesAbstract Objectives To address SPECT’s radioactivity, complexity, and costliness in measuring renal function, this study employs artificial intelligence (AI) with non-contrast CT to estimate single-kidney glomerular filtration rate (GFR) and split renal function (SRF). Methods 245 patients with atrophic kidney or hydronephrosis were included from two centers (Training set: 128 patients from Center I; Test set: 117 patients from Center II). The renal parenchyma and hydronephrosis regions in non-contrast CT were automatically segmented by deep learning. Radiomic features were extracted and combined with clinical characteristics using multivariable linear regression (MLR) to obtain a radiomics-clinical-estimated GFR (rcGFR). The relative contribution of single-kidney rcGFR to overall rcGFR, the percent renal parenchymal volume, and the percent renal hydronephrosis volume were combined by MLR to generate the estimation of SRF (rcphSRF). The Pearson correlation coefficient (r), mean absolute error (MAE), and Lin’s concordance coefficient (CCC) were calculated to evaluate the correlations, differences, and agreements between estimations and SPECT-based measurements, respectively. Results Compared to manual segmentation, deep learning-based automatic segmentation could reduce the average segmentation time by 434.6 times to 3.4 s. Compared to single-kidney GFR measured by SPECT, the rcGFR had a significant correlation of r = 0.75 (p < 0.001), MAE of 10.66 mL/min/1.73 m2, and CCC of 0.70. Compared to SRF measured by SPECT, the rcphSRF had a significant correlation of r = 0.92 (p < 0.001), MAE of 7.87%, and CCC of 0.88. Conclusions The non-contrast CT and AI methods are feasible to estimate single-kidney GFR and SRF in patients with atrophic kidney or hydronephrosis. Critical relevance statement For patients with an atrophic kidney or hydronephrosis, non-contrast CT and artificial intelligence methods can be used to estimate single-kidney glomerular filtration rate and split renal function, which may minimize the radiation risk, enhance diagnostic efficiency, and reduce costs. Key Points Renal function can be assessed using non-contrast CT and AI. Estimated renal function significantly correlated with the SPECT-based measurements. The efficiency of renal function estimation can be refined by the proposed method. Graphical Abstracthttps://doi.org/10.1186/s13244-025-01959-xRenal functionNon-contrast CTDeep learningRadiomics |
| spellingShingle | Yiwei Wang Feng Xu Qiuyue Han Daoying Geng Xin Gao Bin Xu Wei Xia AI-based automatic estimation of single-kidney glomerular filtration rate and split renal function using non-contrast CT Insights into Imaging Renal function Non-contrast CT Deep learning Radiomics |
| title | AI-based automatic estimation of single-kidney glomerular filtration rate and split renal function using non-contrast CT |
| title_full | AI-based automatic estimation of single-kidney glomerular filtration rate and split renal function using non-contrast CT |
| title_fullStr | AI-based automatic estimation of single-kidney glomerular filtration rate and split renal function using non-contrast CT |
| title_full_unstemmed | AI-based automatic estimation of single-kidney glomerular filtration rate and split renal function using non-contrast CT |
| title_short | AI-based automatic estimation of single-kidney glomerular filtration rate and split renal function using non-contrast CT |
| title_sort | ai based automatic estimation of single kidney glomerular filtration rate and split renal function using non contrast ct |
| topic | Renal function Non-contrast CT Deep learning Radiomics |
| url | https://doi.org/10.1186/s13244-025-01959-x |
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