Glomerulonephritis through the lens of ultrasound and radiology

Table 1 Prognostic significance of key ultrasound morphologic parameters in glomerulonephritis

Parameter1	Typical US finding	Correlation with histology	Predictive value2
Renal echogenicity	Increased cortical echogenicity (grades II-IV)	Glomerulosclerosis, tubulointerstitial fibrosis, inflammation	Higher echogenicity correlates with lower eGFR
Cortical thickness	Decreased cortical thickness	Positive correlation with eGFR	< 4.0 mm/cm predicts > 30% eGFR decline or dialysis initiation (sensitivity 72.5%, specificity 80%)

¹Renal cortical thickness normalized for patient height: Cortical thickness (mm) divided by height (cm).

²Reported cut-off values are protocol dependent and may vary according to acquisition settings and patient population.

eGFR: Estimated glomerular filtration rate; US: Ultrasound.

Table 2 Practical ultrasound features in differentiating acute vs chronic glomerulonephritis¹

Parameter	Acute GN/AKI	Chronic GN/CKD	Key differentiating feature
Kidney size	Normal or enlarged	Decreased renal length (< 8 cm)	Enlarged = acute; small = chronic
Cortical thickness	May be normal or increased	Reduced	Thinning indicates chronic damage
Echogenicity	Increased (infiltrates/edema)	Increased (fibrosis/sclerosis)	Alone non-specific; paired with size aids differential diagnosis
Corticomedullary differentiation	May be increased, Preserved or increased	Often reduced or lost	Loss of differentiation implies chronicity

¹Reported thresholds are protocol dependent and may vary according to acquisition settings and patient population.

AKI: Acute kidney injury; CKD: Chronic kidney disease; GN: Glomerulonephritis.

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.

Table 4 Clinician-oriented overview linking common clinical questions in glomerulonephritis to imaging modalities and clinical setting¹

Clinical question	Main clinical aim	Most appropriate imaging modality	Primary clinical endpoint	Clinical setting	Rationale
Acute vs chronic disease?	Disease staging	B-mode ultrasound	Chronicity	Standard of care	First-line tool to assess kidney size, cortical thickness, and corticomedullary differentiation
Renal perfusion impairment?	Functional assessment	Doppler ultrasound; CEUS	Perfusion	Standard of care/adjunct	Evaluation of renal blood flow and microvascular perfusion
Active inflammation vs fibrosis?	Activity vs chronic damage	CEUS; elastography	Activity/fibrosis	Adjunct/emerging	CEUS reflects inflammatory hyperemia; elastography estimates tissue stiffness
Extent of interstitial fibrosis?	Prognostic stratification	Elastography; MRI	Fibrosis	Adjunct	Non-invasive estimation of tissue stiffness and parenchymal remodeling
Tubulointerstitial involvement?	Tissue characterization	Multiparametric MRI	Tissue characterization	Adjunct	Provides complementary functional and structural information
Metabolic or inflammatory activity?	Molecular assessment	PET imaging	Activity	Emerging/research	Detects metabolically active inflammatory tissue
Risk stratification and outcome prediction?	Prognosis	Emerging imaging tools (Radiomics; AI)	Monitoring/prognosis	Research	Quantitative analysis of imaging features for predictive modeling

¹Imaging techniques provide complementary non-invasive information on renal morphology and function but cannot replace histopathological evaluation. Renal biopsy remains the reference standard for definitive diagnosis and should be performed whenever clinically indicated.

AI: Artificial intelligence; CEUS: Contrast-enhanced ultrasound; MRI: Magnetic resonance imaging; PET: Positron emission tomography.