Glomerulonephritis through the lens of ultrasound and radiology

Table 3 Ultrasound techniques in glomerulonephritis¹

Ultrasound technique	Technical principles	Primary applications in GN	Key findings/parameters	Ref.
B-mode (grayscale) ultrasound	Reflection of ultrasound waves from tissue interfaces generates 2D anatomical images	Morphological assessment: Kidney size, cortical thickness, echogenicity, corticomedullary differentiation	Normal kidney: 10-12 cm length, cortical thickness 7-10 mm; increased echogenicity correlates with fibrosis, glomerulosclerosis; cortical thickness < 4.0 mm/cm predicts eGFR decline	O’Neill et al[22], 2000; Moghazi et al[14], 2005; Petrucci et al[6], 2018; Andrulli et al[7], 2024
Color/power doppler ultrasound	Detection of blood flow via Doppler shift; power Doppler measures flow magnitude independent of direction	Vascular resistance assessment, large vessel abnormalities detection	RRI = (PSV - EDV)/PSV; RRI ≥ 0.70 indicates tubulointerstitial damage; isolated TIN: RRI 0.73 vs TIN + GN: 0.64; 65% of isolated TIN shows pathological RRI	Yura et al[24], 1993; Gigante et al[8], 2016; Gigante et al[25], 2022; Galesić et al[54], 2004
Contrast-enhanced ultrasound (CEUS)	Microbubble contrast agents (sulfur hexafluoride) enhance vascular visualization; time-intensity curves quantify perfusion	Perfusion assessment, disease activity monitoring, microvascular alterations detection	Prolonged wash-out correlates with histological activity (P = 0.016); mesangial hyperplasia correlation (P = 0.008); TIC-AUC cutoff 8049.0 - arbitrary units for PLN vs nPLN (AUC 0.810)	Nestola et al[53], 2018; Wei et al[36], 2025; Yang et al[42], 2020; Qasim et al[56], 2025
Shear wave elastography (SWE)	Acoustic radiation force generates shear waves; tissue stiffness measured as Young’s modulus (YM) or shear wave velocity (SWV)	Fibrosis detection and quantification, chronic changes assessment	YM values in CKD significantly higher than controls; YM cutoffs: 0-15 kPa (absent/mild IFTA), 16-27 kPa (moderate), > 28 kPa (severe); cutoff < 20.77 kPa for fibrosis detection; negative correlation with eGFR (r= -0.576, P < 0.0001)	Turgutalp et al[40], 2020; Huynh et al[57], 2022; Choi et al[58], 2023; Sofia et al[59], 2017; Grenier et al[60], 2011
Viscoelastic ultrasound imaging	Measures both elastic (storage) and viscous (loss) properties of tissue using plane-wave ultrasound	Differentiation of proliferative vs non-proliferative lupus nephritis, tissue characterization	Vmean and Dmean elevated in proliferative LN; cut-off vmean 2.16 Pa·s: AUC 0,77, sensibility 56.7%, specificity 86.8% per PLN; combined model: (Vmean + Scr + anti-dsDNA) AUC 0.83	Yuan et al[35], 2025
Microvascular flow imaging (MVFI)	Advanced Doppler techniques (SMI, MFI, MicroFlow) using clutter suppression and adaptive filtering to detect slow-flow microvessels	Microvascular perfusion quantification, early vascular damage detection	Vascular index (VI) = Doppler pixels/total pixels; VI in MN: 0.35 ± 0.18 vs controls: 0.65 ± 0.09 (P < 0.001); 46% reduction in functional capillary density in MN; higher diagnostic performance than eGFR in early disease, AUC VI (0.79), AUC eGFR (0.63)	Lu et al[51], 2024; Qin et al[61], 2014
Ultrasound radiomics	Extraction of quantitative texture features from ultrasound images using computational algorithms	GN subtype classification, fibrosis prediction, pathological correlation	180 features extracted from renal parenchyma; LASSO regression selects discriminative features; gray-level variance, run-length non-uniformity, wavelet-derived textures most informative	Zhang et al[41], 2021; Qin et al[10], 2023; Floreani et al[62], 2021
Machine learning/deep learning ultrasomics	Neural networks analyze ultrasound images combined with radiomics features and clinical data	Integrated diagnostic models, fibrosis staging, outcome prediction	U-net for automatic segmentation; random forest (RF) for classification (37 features); combined nomogram models with clinical factors; SHAP analysis for feature interpretation	Vernuccio et al[63], 2020; Huang et al[44], 2025; Kawashima et al[64], 1997; Stunell et al[65], 2007

¹The table summarizes key ultrasound modalities used in glomerulonephritis, including B-mode, Doppler, contrast-enhanced ultrasound, elastography, viscoelastic imaging, MicroFlow imaging, and radiomics, highlighting their main quantitative parameters, diagnostic thresholds, and correlations with renal pathology and function.

2D: Two-dimensional; eGFR: Estimated glomerular filtration rate; RRI: Renal resistive index; PSV: Peak systolic velocity; EDV: End diastolic velocity; TIN: Tubulointerstitial nephritis; GN: Glomerulonephritis; CEUS: Contrast-enhanced ultrasound; SWE: Shear wave elastography; YM: Young’s modulus; SWV: Shear wave velocity; CKD: Chronic kidney disease; LN: Lupus nephritis; AUC: Area under the curve; PLN: Proliferative lupus nephritis; MVFI: Microvascular flow imaging; SMI: Superb microvascular imaging; MFI: MicroFlow imaging; VI: Vascular index; LASSO: Least absolute shrinkage and selection operator; SHAP: SHapley Additive exPlanations; RF: Random forest.