Top

World Journal of Urology

Published in:

01-12-2024 | Urography | Original Article

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

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

Published in: World Journal of Urology | Issue 1/2024

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.

Available only for authorised users

Truong LD, Gaber L, Eknoyan G (2011) Obstructive uropathy. Contrib Nephrol 169:311–326. https://doi.org/10.1159/000314578CrossRefPubMed

Stevens S (2018) Obstructive kidney disease. Nurs Clin N Am 53(4):569–578. https://doi.org/10.1016/j.cnur.2018.07.007CrossRef

Renard-Penna R, Rocher L, Roy C, André M, Bellin MF, Boulay I, Eiss D, Girouin N, Grenier N, Hélénon O, Lapray JF, Lefèvre A, Matillon X, Ménager JM, Millet I, Ronze S, Sanzalone T, Tourniaire J, Brunelle S, Rouvière O (2020) Imaging protocols for CT urography: results of a consensus conference from the French Society of Genitourinary Imaging. Eur Radiol 30(3):1387–1396. https://doi.org/10.1007/s00330-019-06529-6CrossRefPubMed

Keramida G, James JM, Prescott MC, Peters AM (2015) Pitfalls and limitations of radionuclide renal imaging in adults. Semin Nucl Med 45(5):428–439. https://doi.org/10.1053/j.semnuclmed.2015.02.008CrossRefPubMed

Iyer RS, Schopp JG, Swanson JO, Thapa MM, Phillips GS (2013) Safety essentials: acute reactions to iodinated contrast media. Can Assoc Radiol J 64(3):193–199. https://doi.org/10.1016/j.carj.2011.12.014CrossRefPubMed

Musso CG, Álvarez-Gregori J, Jauregui J, Macías-Núñez JF (2016) Glomerular filtration rate equations: a comprehensive review. Int Urol Nephrol 48(7):1105–1110. https://doi.org/10.1007/s11255-016-1276-1CrossRefPubMed

Levey AS, Coresh J, Greene T, Stevens LA, Zhang YL, Hendriksen S, Kusek JW, Van Lente F (2006) Using standardized serum creatinine values in the modification of diet in renal disease study equation for estimating glomerular filtration rate. Ann Intern Med 145(4):247–254. https://doi.org/10.7326/0003-4819-145-4-200608150-00004CrossRefPubMed

Ma YC, Zuo L, Chen JH, Luo Q, Yu XQ, Li Y, Xu JS, Huang SM, Wang LN, Huang W, Wang M, Xu GB, Wang HY (2006) Modified glomerular filtration rate estimating equation for Chinese patients with chronic kidney disease. J Am Soc Nephrol 17(10):2937–2944. https://doi.org/10.1681/ASN.2006040368CrossRefPubMed

Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AR, Feldman HI, Kusek JW, Eggers P, Van Lente F, Greene T, Coresh J (2009) A new equation to estimate glomerular filtration rate. Ann Intern Med 150(9):604–612. https://doi.org/10.7326/0003-4819-150-9-200905050-00006CrossRefPubMedPubMedCentral

10.

Berns JS (2015) Clinical decision making in a patient with stage 5 CKD–Is eGFR good enough? Clin J Am Soc Nephrol 10(11):2065–2072. https://doi.org/10.2215/CJN.00340115CrossRefPubMedPubMedCentral

11.

Michels WM, Grootendorst DC, Verduijn M, Elliott EG, Dekker FW, Krediet RT (2010) Performance of the Cockcroft-Gault, MDRD, and new CKD-EPI formulas in relation to GFR, age, and body size. Clin J Am Soc Nephrol 5(6):1003–1009. https://doi.org/10.2215/CJN.06870909CrossRefPubMedPubMedCentral

12.

Li Y, Zhang Y, Zhang E, Chen Y, Wang Q, Liu K, Yu HJ, Yuan H, Lang N, Su MY (2021) Differential diagnosis of benign and malignant vertebral fracture on CT using deep learning. Eur Radiol 31(12):9612–9619. https://doi.org/10.1007/s00330-021-08014-5CrossRefPubMedPubMedCentral

13.

Cha KH, Hadjiiski L, Samala RK, Chan HP, Caoili EM, Cohan RH (2016) Urinary bladder segmentation in CT urography using deep-learning convolutional neural network and level sets. Med Phys 43(4):1882–1896CrossRefPubMedPubMedCentral

14.

Teramoto A, Fujita H, Yamamuro O, Tamaki T (2016) Automated detection of pulmonary nodules in PET/CT images: ensemble false-positive reduction using a convolutional neural network technique. Med Phys 43(6Part1):2821–2827CrossRefPubMed

15.

Kim T, Lee KH, Ham S, Park B, Lee S, Hong D, Kim GB, Kyung YS, Kim C, Kim N (2020) Active learning for accuracy enhancement of semantic segmentation with CNN-corrected label curations: evaluation on kidney segmentation in abdominal CT. Sci Rep UK 10(1):1–7

16.

Çiçek Ö, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O (2016) 3D U-Net: learning dense volumetric segmentation from sparse annotation. Springer, Berlin, pp 424–432

17.

Levin A, Stevens PE, Bilous RW, Coresh J, De Francisco ALM, De Jong PE, Griffith KE, Hemmelgarn BR, Iseki K, Lamb EJ, Levey AS, Riella MC, Shlipak MG, Wang H, White CT, Winearls CG (2013) Kidney disease: Improving global outcomes (KDIGO) CKD work group. KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int Suppl 3(1):1–150. https://doi.org/10.1038/kisup.2012.73CrossRef

18.

Park J, Bae S, Seo S, Park S, Bang JI, Han JH, Lee WW, Lee JS (2019) Measurement of glomerular filtration rate using quantitative SPECT/CT and deep-learning-based kidney segmentation. Sci Rep 9(1):4223. https://doi.org/10.1038/s41598-019-40710-7CrossRefPubMedPubMedCentral

19.

Wang H, Bowe B, Cui Z, Yang H, Joshua SS, Xie Y, Al-Aly Z (2022) A deep learning approach for the estimation of glomerular filtration rate. IEEE Trans Nanobiosci. https://doi.org/10.1109/TNB.2022.3147957CrossRef

20.

Hu J, Xu X, Zhang K, Li Y, Zheng J, Chen W, Wang X (2020) Comparison of estimated glomerular filtration rates in Chinese patients with chronic kidney disease among serum creatinine-, cystatin-C- and creatinine-cystatin-C-based equations: a retrospective cross-sectional study. Clin Chim Acta 505:34–42. https://doi.org/10.1016/j.cca.2020.01.033CrossRefPubMed

21.

Wang X, Luo Y, Wang Y, Wang C, Zhao X, Wang D, Liu L, Liu G, Wang Y (2014) Comparison of associations of outcomes after stroke with estimated GFR using Chinese modifications of the MDRD study and CKD-EPI creatinine equations: results from the China National Stroke Registry. Am J Kidney Dis 63(1):59–67. https://doi.org/10.1053/j.ajkd.2013.08.008CrossRefPubMed

22.

Stevens LA, Coresh J, Greene T, Levey AS (2006) Assessing kidney function–measured and estimated glomerular filtration rate. N Engl J Med 354(23):2473–2483. https://doi.org/10.1056/NEJMra054415CrossRefPubMed

Title: UroAngel: a single-kidney function prediction system based on computed tomography urography using deep learning
Authors: Qingyuan Zheng
Xinmiao Ni
Rui Yang
Panpan Jiao
Jiejun Wu
Song Yang
Zhiyuan Chen
Xiuheng Liu
Lei Wang
Publication date: 01-12-2024
Publisher: Springer Berlin Heidelberg
Keywords: Urography
Computed Tomography
Computed Tomography
Published in: World Journal of Urology / Issue 1/2024
Print ISSN: 0724-4983
Electronic ISSN: 1433-8726
DOI: https://doi.org/10.1007/s00345-024-04921-6

Keynote webinar | Spotlight on medication adherence

Springer Medicine

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

Abstract

Background

Methods

Results

Conclusions

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Background

Methods

Results

Conclusions

Please log in to get access to this content

Other articles of this Issue 1/2024

One-year clinical outcomes of MR-guided stereotactic body radiation therapy with rectal spacer for patients with localized prostate cancer

Prevalence and predictors of stone passage after double J stenting for symptomatic ureteral stones: a cross-sectional, real-life study

Single-port robot-assisted radical prostatectomy

The ERUS course on robot-assisted kidney transplantation

Comment on ‘A new position utilizing the effect of gravity in proximal ureteral stones, ureteroscopic lithotripsy in the reverse Trendelenburg position: a prospective, randomized, comparative study’

Current practice and unmet training needs in robotic-assisted radical prostatectomy: investigation from the Junior ERUS/YAU working group