Evidence map›Paper›PMID 40192862›Full record

ArticleInsights into imaging2025

AI-based automatic estimation of single-kidney glomerular filtration rate and split renal function using non-contrast CT.

Yiwei Wang, Feng Xu, Qiuyue Han, Daoying Geng, Xin Gao, Bin Xu, Wei Xia

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PubMed Full text at PMC Publisher
In one paragraph

Article in Insights into imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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0cells of the map it votes in
3citing papers in PubMed
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1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Yiwei Wang *Department of Urology, Ninth People's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Feng Xu *Department of Nuclear Medicine, Ninth People's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Qiuyue HanDepartment of Radiology, Huashan Hospital, Fudan University, Shanghai, China.
Daoying GengDepartment of Radiology, Huashan Hospital, Fudan University, Shanghai, China.
Xin GaoMedical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China.
Bin XuDepartment of Urology, Ninth People's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China. 116201@sh9hospital.org.cn.
Wei XiaMedical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China. xiav1990@163.com.ORCID http://orcid.org/0000-0003-3368-4050

Funding

he Key Research and Development Program of Shandong Province 2021SFGC0104Shanghai Jiaotong University School of Medicine Shanghai high-level local university construction project 2022zxy005the 2023 Whole Laboratory Automation System and Intelligent Analysis System Project ZTZB-23-990-008the Key Research and Development Program of Jiangsu Province BE2021663, BE2023714the Suzhou Science and Technology Plan Project SJC2021014, SZS2022008the Taishan Industrial Experts Program tscx202312131
6 · The paper itself

Abstract

objectivesTo 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).

methods245 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.

resultsCompared 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 m

conclusionsThe 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.

Indexed as

Deep learningNon-contrast CTRadiomicsRenal function

Identifiers

PMID40192862
PMCPMC11977085

What Socratic holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.