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. 2025 Jun 4;13(6):1381.
doi: 10.3390/biomedicines13061381.

Texture Analysis of T2-Weighted Images as Reliable Biomarker of Chronic Kidney Disease Microstructural State

Marcin Majos  1   2 , Artur Klepaczko  3 , Katarzyna Szychowska  4 , Ludomir Stefanczyk  2 , Ilona Kurnatowska  4
Affiliations

Texture Analysis of T2-Weighted Images as Reliable Biomarker of Chronic Kidney Disease Microstructural State

Marcin Majos et al. Biomedicines. .

Abstract

Objectives: The diagnostics of chronic kidney disease (CKD) consist of three basic groups of examinations: laboratory tests, radiological imaging and histopathological examinations. However, in the most severe clinical cases, where a fast, undisputed decision is required, histopathological tests are the only suitable option. Unfortunately, such tests require an invasive kidney biopsy, which is not possible in many patients. The aim of this study is to create an algorithm that can categorize CKD patients into active and non-active phases on the basis of MRI texture analysis and compare the results with histopathological examinations. Methods: MRI examinations were performed on healthy volunteers (group 1, N = 14) and CKD patients who also received kidney biopsy. The histopathological examination was used to divide the patients into active phase CKD (group 2, N = 58) and non-active phase CKD (group 3, N = 22). The T2-weighted MRI images were analyzed using a Support Vector Machine (SVM) model created with qMazDa software, which was trained to classify images into the appropriate group of CKD activity. Results: The following evaluation metrics were calculated for the final SVM models corresponding to confusion matrices: for texture analysis-balanced accuracy 81.6%, sensitivity 68.2-92.0%, specificity 82.5-97.5% and precision 62.5-95.8%; for texture and shape analysis-balanced accuracy 87.3%, sensitivity 77.3-100.0%, specificity 87.5-100.0% and precision 65.4-100.0%. Conclusions: Texture analysis of T2-weighted images associated with kidney shape features seems to be reliable method of assessing the state of ongoing CKD.

Keywords: CKD; MRI; T2-weighted images; chronic kidney disease; magnetic resonance; texture analysis.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Example regions of interest for texture and shape analysis.
Figure 2
Figure 2
Confusion matrices obtained for final SVM models (Group 1—normal, Group 2—active, Group 3—chronic)—5-fold cross-validation test.
Figure 3
Figure 3
ROC curve analysis between healthy volunteers (Group 1) and patients with CKD (Group 2 combined with Group 3).
Figure 4
Figure 4
ROC curve analysis between patients with active phase of CKD (Group 2) and patients with non-active phase of CKD (Group 3).
See this image and copyright information in PMC

References

    1. Sato Y., Takahashi M., Yanagita M. Pathophysiology of AKI to CKD progression. Semin. Nephrol. 2020;40:206–215. doi: 10.1016/j.semnephrol.2020.01.011. - DOI - PubMed
    1. Hill N.R., Fatoba S.T., Oke J.L., Hirst J.A., O’Callaghan C.A., Lasserson D.S., Hobbs F.D.R. Global Prevalence of Chronic Kidney Disease—A Systematic Review and Meta-Analysis. PLoS ONE. 2016;11:e0158765. doi: 10.1371/journal.pone.0158765. - DOI - PMC - PubMed
    1. Navaneethan S.D., Zoungas S., Caramori M.L., Chan J.C., Heerspink H.J., Hurst C., Liew A., Michos E.D., Olowu W.A., Sadusky T., et al. Diabetes Management in Chronic Kidney Disease: Synopsis of the KDIGO 2022 Clinical Practice Guideline Update. Ann. Intern. Med. 2023;176:381–387. doi: 10.7326/M22-2904. - DOI - PubMed
    1. Palm F., Nordquist L. Renal oxidative stress, oxygenation, and hypertension. Am. J. Physiol. Regul. Integr. Comp. Physiol. 2011;301:R1229–R1241. doi: 10.1152/ajpregu.00720.2010. - DOI - PMC - PubMed
    1. Ow C.P.C., Ngo J., Ullah M.M., Hilliard L.M., Evans R.G. Renal hypoxia in kidney disease: Cause or consequence? Acta Physiol. 2018;222:e12999. doi: 10.1111/apha.12999. - DOI - PubMed

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