ReviewWorld journal of nephrology2026
Renal elastography in kidney disease: Current evidence, limitations, and future directions.
Review in World journal of nephrology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Renal elastography has gained attention as a noninvasive way to estimate tissue stiffness and, indirectly, structural kidney damage. In general, elastography is considered a noninvasive test (NIT) for assessing liver fibrosis, and it has been widely adopted and even proposed as a potential alternative to liver biopsy in selected settings. However, its use as an NIT has not been completely translated to the kidney, largely due to fundamental differences in tissue structure and hemodynamics. Unlike the relatively homogeneous liver, the kidney is anisotropic and highly dependent on perfusion. Therefore, stiffness measurements may be affected by blood flow, vascular resistance, and interstitial pressure at the time of measurement, making them more difficult to interpret. There is growing evidence that shear-wave elastography can detect structural changes in chronic kidney disease, hypertension-related damage, acute kidney injury, and some glomerular disorders. That said, findings remain inconsistent. The reported cut-off values vary widely between studies, and reproducibility remains limited. Technical factors, such as inter-device and acquisition-protocol variability, further complicate the comparison of imaging results. In practice, elastography provides information about tissue mechanics and may be complementary to conventional imaging and laboratory markers. However, it might not be specific if used alone. Combined approaches of specifically shear-wave elastography with functional and biochemical NIT could provide better diagnostic performance and reflect a more clinically useful approach. Overall, renal elastography is better viewed as a complementary tool rather than a replacement for histology. Future research should focus on standardization, improving cross-platform validation, and generating longitudinal data to clarify its role in clinical decision-making and risk assessment.
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