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UroAngel: a single-kidney function prediction system based on computed tomography urography using deep learning

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Abstract

Background

Accurate estimation of the glomerular filtration rate (GFR) is clinically crucial for determining the status of obstruction, developing treatment strategies, and predicting prognosis in obstructive nephropathy (ON). We aimed to develop a deep learning-based system, named UroAngel, for non-invasive and convenient prediction of single-kidney function level.

Methods

We retrospectively collected computed tomography urography (CTU) images and emission computed tomography diagnostic reports of 520 ON patients. A 3D U-Net model was used to segment the renal parenchyma, and a logistic regression multi-classification model was used to predict renal function level. We compared the predictive performance of UroAngel with the Modification of Diet in Renal Disease (MDRD), Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equations, and two expert radiologists in an additional 40 ON patients to validate clinical effectiveness.

Results

UroAngel based on 3D U-Net convolutional neural network could segment the renal cortex accurately, with a Dice similarity coefficient of 0.861. Using the segmented renal cortex to predict renal function stage had high performance with an accuracy of 0.918, outperforming MDRD and CKD-EPI and two radiologists.

Conclusions

We proposed an automated 3D U-Net-based analysis system for direct prediction of single-kidney function stage from CTU images. UroAngel could accurately predict single-kidney function in ON patients, providing a novel, reliable, convenient, and non-invasive method.

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Data availability

The data of this study are protected by the Ethics Committee and cannot be published. The codes and supplementary materials involved can be obtained by contacting the corresponding authors upon reasonable request.

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Acknowledgements

The authors acknowledge the Grant from the Project of Hubei Province Key Research and Development Project of China (Grant no. 2020BCB051), Hubei Province Central Guiding Local Science and Technology Development Project (Grant no. 2022BGE232), and the National Medical Education Development Center Medical Simulation Education Research Project of China (Grant no. 2021MNYB11) for supporting the work.

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Author notes

  1. Qingyuan Zheng, Xinmiao Ni and Rui Yang contributed equally to this work.

Authors and Affiliations

  1. Department of Urology, Renmin Hospital of Wuhan University, 99 Zhang Zhi-Dong Road, Wuhan, 430060, Hubei, People’s Republic of China

    Qingyuan Zheng, Xinmiao Ni, Rui Yang, Panpan Jiao, Jiejun Wu, Song Yang, Zhiyuan Chen, Xiuheng Liu & Lei Wang

  2. Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan, 430060, Hubei, People’s Republic of China

    Qingyuan Zheng, Xinmiao Ni, Rui Yang, Panpan Jiao, Jiejun Wu, Song Yang, Zhiyuan Chen, Xiuheng Liu & Lei Wang

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  1. Qingyuan Zheng
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Contributions

Xiuheng Liu reports being director and the sponsor of this study. Qingyuan Zheng, Rui Yang, and Xinmiao Ni performed the research, analyzed the data, and wrote the paper. Jiejun Wu, Panpan Jiao, Song Yang, Lei Wang, and Zhiyuan Chen contributed essential tools.

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Correspondence to Xiuheng Liu or Lei Wang.

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Conflict of interest

The authors have no potential conflicts of interest to report.

Ethical approval

This retrospective study was approved by the Ethics Committee of Renmin Hospital of Wuhan University (No. WDRY2021-K065), and the requirement for written informed consent was waived. All patients had signed informed consent forms regarding the use of iodinated contrast agents before undergoing CTU examinations.

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Zheng, Q., Ni, X., Yang, R. et al. UroAngel: a single-kidney function prediction system based on computed tomography urography using deep learning. World J Urol 42, 238 (2024). https://doi.org/10.1007/s00345-024-04921-6

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