Early ultrasound markers for predicting the transition from acute kidney injury to chronic kidney disease and treatment response in a murine model

Min Hoan Moon; Jeonghwan Lee; Chang Kyu Sung; Jung Pyo Lee; Myoung Seok Lee

doi:10.14366/usg.26139

Moon, Lee, Sung, Lee, and Lee: Early ultrasound markers for predicting the transition from acute kidney injury to chronic kidney disease and treatment response in a murine model

Original Article

Ultrasonography 2026; 45(4): 359-368. https://doi.org/10.14366/usg.26139

Early ultrasound markers for predicting the transition from acute kidney injury to chronic kidney disease and treatment response in a murine model

Min Hoan Moon^1,²^*

, Jeonghwan Lee^3,⁴^*

, Chang Kyu Sung^1,²

, Jung Pyo Lee^3,⁴

, Myoung Seok Lee¹

¹Department of Radiology, Seoul Metropolitan Government Seoul National University Boramae Medical Center, Seoul, Korea

²Department of Radiology, Seoul National University College of Medicine, Seoul, Korea

³Department of Internal Medicine, Seoul Metropolitan Government Seoul National University Boramae Medical Center, Seoul, Korea

⁴Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea

Correspondence to: Myoung Seok Lee, MD, Department of Radiology, Seoul National University Boramae Medical Center, 20 Boramae-ro-5-gil, Dongjak-gu, Seoul 07061, Korea Tel. +82-2-870-2542 Fax. +82-2-870-3539 E-mail: achieva1004@gmail.com

^* These authors contributed equally to this work.

Received April 11, 2026 Revised April 30, 2026 Accepted May 3, 2026 Published online May 3, 2026

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Purpose

This study evaluated the feasibility of ultrasound (US) parameters for predicting the transition from acute kidney injury (AKI) to chronic kidney disease (CKD) and assessing the therapeutic response to 17-DMAG, a fibrosis-mitigating agent, in a murine unilateral ischemia–reperfusion injury (UIRI) model.

Methods

Male C57BL/6 mice were assigned to sham (n=16) or UIRI (n=24) groups, with half of the UIRI mice receiving 17-DMAG (20 mg/kg intraperitoneally, three times weekly). Serial US examinations were performed on postoperative days (PODs) 3 and 8 to evaluate morphological parameters (kidney size and parenchymal thickness [PT]), vascular parameters (resistive index [RI] and vascular index [VI] derived from microvascular imaging [MVI]), and tissue stiffness assessed by shear-wave speed (SWS). Pathologic fibrosis was defined as a Sirius red–positive area >4%. Diagnostic performance was evaluated using receiver operating characteristic (ROC) curve analysis.

Results

At the early stage (POD 3), vascular parameters (VI and RI) demonstrated high diagnostic performance for predicting fibrosis progression (area under the receiver operating characteristic curve, 0.948 and 0.890, respectively), with VI serving as the only significant predictor of early treatment response to 17-DMAG. At POD 8, all parameters showed significant diagnostic performance for predicting fibrosis progression. However, for treatment response, only kidney size, PT, and RI demonstrated significant ROC performance, whereas VI and SWS did not reach statistical significance. These findings reflect the temporal transition from early functional microvascular compromise to established structural remodeling and parenchymal atrophy.

Conclusion

RI and VI are promising noninvasive surrogate markers for predicting the AKI-to-CKD transition, and VI may be useful for assessing early responses to 17-DMAG treatment. Whereas conventional US parameters identify late-stage structural remodeling, MVI provides a critical diagnostic window during the acute phase by detecting early microvascular compromise. These findings highlight the potential utility of MVI for real-time monitoring of AKI progression and anti-fibrotic treatment responses in clinical practice.

Keywords: Acute kidney injury; Reperfusion injury; AKI-to-CKD transition; Renal fibrosis; Microvascular imaging; Treatment response

Key points

Ultrasound parameters are effective tools for predicting the acute kidney injury–to–chronic kidney disease transition and for assessing therapeutic response to anti-fibrotic treatment with 17-DMAG in a murine model. At the early stage (postoperative day [POD] 3), microvascular imaging–derived vascular parameters (vascular index and resistive index) serve as sensitive indicators of microvascular compromise before overt structural alterations become apparent. At the later stage (POD 8), although all parameters predict disease progression, structural parameters such as kidney size and parenchymal thickness become more reliable for evaluating treatment response as pathology progresses toward irreversible remodeling.

Graphical abstract

Introduction

Acute kidney injury (AKI), defined by increased serum creatinine levels and reduced urine output lasting no longer than 7 days [1], has emerged as an independent risk factor for chronic kidney disease (CKD), end-stage kidney disease, and mortality [2]. AKI and CKD are now recognized as interconnected conditions with overlapping pathophysiological mechanisms, a process commonly referred to as the AKI-to-CKD transition. Previous studies have reported a pooled CKD progression rate of 25.8 per 100 person-years among patients with AKI [2], and 24.6% of patients with AKI developed CKD during a 3-year follow-up period [3].

The key feature of the AKI-to-CKD transition is the development of fibrosis, which represents the hallmark pathological manifestation of CKD [4]. However, noninvasive assessment of kidney interstitial fibrosis (IF) remains challenging, and invasive needle biopsy is still considered the reference standard for evaluating renal fibrosis.

Ultrasonography (US) is a noninvasive, real-time imaging modality widely used as a first-line tool for evaluating kidney disease without radiation exposure. In addition to morphological parameters such as kidney echogenicity, length, and parenchymal thickness, vascular parameters including resistive index (RI) and microvascular imaging (MVI), as well as stiffness measurements obtained using shear-wave elastography (SWE), have demonstrated good diagnostic performance for CKD and renal fibrosis in both preclinical and clinical settings [5–9]. In AKI, US can identify structural abnormalities, aid in determining the etiology of AKI, evaluate disease severity, and assess intrarenal and extrarenal vascularity [10–12].

To the authors’ knowledge, few studies have specifically evaluated the utility of US parameters for predicting the AKI-to-CKD transition. Yoon et al. [13] reported that contrast-enhanced ultrasound (CEUS) may serve as a supplementary tool for predicting CKD progression in patients with AKI. Similarly, Cao et al. [14] demonstrated that kidney perfusion mapping using CEUS predicted progressive renal fibrosis in mice with ischemia–reperfusion injury. That study further showed that severe reductions in cortical perfusion at the time of AKI diagnosis may predict subsequent AKI-to-CKD transition. However, these studies evaluated only CEUS-derived parameters without incorporating widely used B-mode findings. Moreover, CEUS has inherent practical limitations, including the need for intravenous access, dedicated post-processing software, and additional personnel and examination time. Furthermore, the role of MVI without contrast agents in evaluating the AKI-to-CKD transition remains largely unexplored.

Therefore, the purpose of this study was to evaluate whether US parameters, including MVI, obtained after AKI could predict progression to renal fibrosis. To ensure consistent parameter control, the study was conducted using a murine AKI-to-CKD transition model. In addition, 17-DMAG (dimethylaminoethylamino-17-demethoxygeldanamycin) was used as a fibrosis-mitigating agent to evaluate the utility of US parameters for predicting treatment response [15].

Materials and Methods

Compliance with Ethical Standards

The animal experiments were approved by the Institutional Animal Care and Use Committee of the authors’ affiliated hospital (IACUC No. 2022-0004).

AKI-to-CKD Transition Animal Model

The unilateral ischemia–reperfusion injury (UIRI) method was used to establish an AKI-to-CKD transition animal model [16]. Wild-type male C57BL/6 (B6) mice aged 7–8 weeks and weighing approximately 20–22 g were obtained and housed for a 7-day acclimation period before the experiments. Anesthesia was induced using xylazine (Rompun; 10 mg/kg; Bayer, Leverkusen, Germany) and Zoletil (30 mg/kg; Virbac, Nice, France). The left renal hilum was accessed through a left flank incision, and the vascular pedicle was occluded for 27 minutes using microaneurysm clamps (Roboz Surgical Instrument Co., Gaithersburg, MD, USA). Mice were humanely sacrificed by exsanguination under anesthesia 6 weeks after the procedure. Mice were assigned to either the sham operation group (n=16) or the UIRI group (n=24). Twelve of the 24 UIRI mice received 17-DMAG intraperitoneally three times weekly at a dose of 20 mg/kg. A schematic flowchart illustrating the overall experimental design is provided in Supplementary Fig. 1.

Ultrasound Examination

Of the 40 mice included in the study, 18 mice (6 sham and 12 UIRI) underwent US on both postoperative day (POD) 3 and POD 8, whereas the remaining 22 mice were imaged only on POD 8 owing to clinical and experimental scheduling constraints (Supplementary Fig. 1). All US examinations were performed by a board-certified radiologist. Detailed US methods are described below.

US was performed using a clinical system equipped with an ultrahigh-frequency linear probe (i33LX9; center frequency, 33 MHz) and a high-resolution linear probe (i18LX5; 18 MHz). The i18LX5 probe was first used to localize the target kidney and delineate its outline and layered anatomy. Detailed grayscale and color Doppler assessments, including RI measurements, were then performed using the i33LX9 probe. Kidney length was defined as the maximal pole-to-pole distance on the long-axis view. Parenchymal thickness (PT) was measured at the midportion of the longest-axis view and defined as the distance from the cortical capsule at the cortex-perirenal fat interface to the interface between the sinus and the apical tip of the pyramid [17]. RI was calculated as the mean of two measurements obtained from different interlobar arteries in the target kidney.

MVI was assessed using a clutter-suppression technique optimized for low-velocity vessels (superb microvascular imaging, Canon Medical Systems, Otawara, Japan), and the vascular index (VI) was calculated. Acquisition parameters were standardized using a low wall filter, high frame rates (44–55 frames/s), and color gain adjusted to suppress noise while preserving visualization of subcapsular vessels, typically at 38%–40%. VI was calculated as the proportion of color-encoded pixels within a manually traced renal region of interest (ROI) relative to the total pixel count for that ROI (Figs. 1, 2).

SWE was performed using the i18LX5 probe, with the display scale set to 90 kPa. Coupling gel was applied liberally, and the transducer was gently placed and then lifted vertically to maintain a consistent gel standoff of approximately 0.5 cm and minimize probe pressure. SWE was acquired in single-shot mode, with one push pulse per frame, to estimate shear-wave speed (SWS) and tissue elasticity. Two or three circular ROIs, each 1 mm in diameter, were positioned with reference to the propagation map and variation/confidence map. According to vendor guidance, ROIs were placed where propagation contours were most parallel and closely spaced and where variation was minimal. To reduce anisotropy, ROIs were positioned as close as feasible to the interpolar region [18]. After four push pulses, a total of 10–12 ROIs were obtained within a single session. For each ROI, mean SWS (m/s) was recorded, and the corresponding elasticity (kPa) was derived using E=3ρVs², where E is elasticity, ρ is tissue density, and Vs is estimated SWS [19]. The ρ value was assumed to be 1,000 kg/m³, reflecting the predominant water content of animal tissue. Because SWS is the directly measured quantity in SWE, whereas stiffness in kPa is derived under specific assumptions, only SWS is reported to ensure unit consistency [19].

Fibrosis Quantification

Paraffin-embedded kidney tissue sections 4 μm thick were stained with Sirius red (ScyTek, Logan, UT, USA) to assess tissue fibrosis. For each kidney section, at least eight fields at ×200 magnification were randomly selected and photographed using a light microscope (BX53F2, Olympus, Tokyo, Japan). The fibrotic area and total tissue area were quantified using ImageJ software version 1.53e (Wayne Rasband, National Institutes of Health, Bethesda, MD, USA). Pathologic fibrosis was defined as a Sirius red–positive area >4% of the total tissue area. Treatment response to 17-DMAG was defined as the absence of histologically confirmed pathologic fibrosis.

Statistical Analysis

Between-group comparisons of US parameters were performed using the Mann-Whitney U test for comparisons between the sham and UIRI groups and between 17-DMAG–treated and untreated mice within the UIRI group. Spearman rank correlation coefficients were calculated between each US parameter and the percentage of Sirius red–positive area relative to the total tissue area (%Area). The predictive performance of each US parameter was evaluated as the area under the receiver operating characteristic curve (AUC), with sensitivity and specificity calculated at the optimal cutoff value. Statistical analyses were performed using MedCalc Statistical Software version 23.3.7 (MedCalc Software Ltd., Ostend, Belgium). A two-sided P-value <0.05 was considered statistically significant.

Results

Five US parameters—affected kidney size, PT, RI, VI, and SWS—were evaluated according to the US imaging time point (POD 3 or POD 8) in each group: sham, 17-DMAG–treated UIRI, and untreated UIRI. These parameters are presented as mean±standard deviation in Table 1. Between-group comparisons of US parameters, including comparisons between the sham and UIRI groups and between the 17-DMAG–treated and untreated UIRI groups, are also presented in Table 1 as P-values.

Value of US Parameters for Predicting AKI-to-CKD Transition

At POD 3, all US parameters in the UIRI group, except kidney size, showed significant changes compared with those in the sham group. At POD 8, all parameters differed significantly between the sham and UIRI groups (Table 1).

Table 2 summarizes the predictive performance of US parameters for predicting the AKI-to-CKD transition, defined as the development of pathologic fibrosis, using AUC values, sensitivity, specificity, and cutoff values. At POD 3, the vascular parameters RI and VI showed higher AUC values (AUC, 0.890 and 0.948; P=0.007 and P=0.002, respectively), indicating good performance for predicting the AKI-to-CKD transition. SWS also showed potential utility for predicting the AKI-to-CKD transition (AUC, 0.871; P=0.016). At POD 8, all parameters showed high AUC values for predicting the AKI-to-CKD transition.

Value of US Parameters for Predicting Treatment Response

At POD 3, US parameters in the 17-DMAG–treated group did not differ significantly from those in the untreated group, although RI and VI showed relatively low P-values compared with the other parameters (P=0.052 and P=0.093, respectively). At POD 8, all parameters except SWS differed significantly between the 17-DMAG–treated and untreated groups (Table 1).

The ability of US parameters to predict attenuation of kidney fibrosis after 17-DMAG treatment is shown in Table 3. In the untreated UIRI group (n=12), pathologic fibrosis was observed in all mice at the time of sacrifice. In the 17-DMAG–treated group (n=12), pathologic fibrosis developed in five mice. At POD 3, only VI showed a statistically significant AUC value (AUC, 0.886; P=0.030), suggesting that VI may be the most reliable US parameter for predicting early treatment response. At POD 8, all parameters except VI and SWS showed significant AUC values. Morphological parameters, including kidney size and PT, showed higher AUC values with lower P-values (AUC, 0.828 and 0.926; P=0.011 and P=0.001, respectively), suggesting that POD 8 was a sufficient time point for detecting AKI-to-CKD transition in the UIRI mouse model. The vascular parameters RI and VI also showed high AUC values (AUC, 0.848 and 0.750; P=0.008 and P=0.051, respectively), although the P-value for VI approached but did not reach statistical significance.

Taken together, these results suggest that the therapeutic effect of 17-DMAG may be associated with attenuation of microvascular rarefaction. Accordingly, vascular parameters, particularly VI at the early stage and RI at POD 8, may reflect treatment response to 17-DMAG. However, the borderline AUC of VI at POD 8 indicates that its late-stage predictive role remains inconclusive.

Correlation of US Parameters with Histological Fibrosis Area

At POD 3, RI and VI were strongly correlated with %Area (ρ=0.616, P=0.007 for RI; ρ=−0.698, P=0.001 for VI). SWS on POD 3 showed a modest correlation with %Area (ρ=0.498, P=0.045). At POD 8, all parameters showed robust correlations with %Area (Figs. 3, 4).

Discussion

This experimental study evaluated the feasibility of US parameters for predicting progression from AKI to CKD and for assessing the effects of fibrosis-mitigating treatment after AKI. At the early stage of AKI, represented by POD 3 in this study, US vascular parameters (RI and VI) and SWS showed good performance for predicting the AKI-to-CKD transition. Furthermore, VI showed good performance for predicting treatment response to the fibrosis-mitigating agent 17-DMAG during early-stage AKI imaging at POD 3.

As noted above, the AKI-to-CKD transition was defined in this study as the onset of pathologic fibrosis, represented by a Sirius red–positive area >4% of the total tissue area, because kidney fibrosis is the hallmark pathological manifestation of CKD. To define a threshold for pathologic fibrosis, a cutoff value was established based on both the experimental data and established clinical reference ranges. In this study, receiver operating characteristic (ROC) curve analysis yielded a %Area cutoff value of 4.02% for differentiating between 17-DMAG–treated and untreated UIRI groups. This threshold aligns closely with the histological characteristics of healthy human kidneys. Specifically, Rule et al. [20] reported that, in a large cohort of healthy living donors, only 4.9% of individuals had IF exceeding 5%. Wang et al. [21] reported that any IF was present in 22% of kidney donors and that fibrosis >5% was present in 4%. Given that IF greater than 5% is uncommon in healthy populations, the 4% threshold used herein provides a conservative criterion for distinguishing pathologic fibrosis from baseline histological variation.

An important finding of this study is that US vascular parameters are key markers for evaluating the AKI-to-CKD transition and that MVI may serve as an indicator for predicting therapeutic response to a fibrosis-mitigating agent in a UIRI mouse model, particularly during the early stage of AKI.

Although AKI has traditionally been classified as prerenal, intrinsic renal, or postrenal—with prerenal AKI typically attributed to ischemic insult from hypoperfusion—growing evidence suggests that AKI is better understood as a pathophysiological continuum. This process involves a complex interplay of mechanisms, including renal hypoperfusion, direct tubular injury, interstitial hypertension, and renal vasoconstriction mediated by tubuloglomerular feedback. In this context, decreased renal parenchymal perfusion is closely associated with the development and progression of AKI [22,23] and precedes renal functional decline [24,25], as supported by evidence of impaired renal microcirculation in animal models [14]. These findings highlight the importance of evaluating renal perfusion and vascularity.

RI has been extensively studied for the diagnosis and follow-up of AKI. Previous studies have shown that RI may be useful for predicting AKI, distinguishing acute tubular necrosis or other parenchymal diseases from prerenal azotemia, and predicting AKI severity [11]. Consistent with these findings, RI showed good performance for predicting AKI-to-CKD transition in the present study. CEUS has also been studied for evaluating renal microcirculation in AKI. A previous meta-analysis reported that CEUS can demonstrate reduced microcirculatory perfusion, prolonged perfusion time, and a reduced rising slope in the renal cortex of patients with AKI, indicating that CEUS may aid in AKI diagnosis [8]. The value of CEUS for predicting AKI severity and prognosis has also been suggested [13]. Currently, MVI using advanced clutter filters is attracting attention as a complement or alternative to CEUS because it is noninvasive and avoids the additional time and effort required for contrast-agent administration. To the best of the authors’ knowledge, this is the first study to investigate the diagnostic feasibility and clinical utility of advanced, commercially available MVI technology in an experimental longitudinal model of AKI-to-CKD transition. Consistent with previous reports showing that MVI can delineate fine renal vascular architecture, from interlobar to interlobular arteries, and is closely associated with tubular atrophy [9,26], these results further support MVI as a reliable imaging biomarker for monitoring AKI-to-CKD transition.

Kidney fibrosis represents the hallmark histological transformation in the transition from AKI to CKD. Although pharmacological interventions such as sodium-glucose cotransporter 2 inhibitors, renin–angiotensin system blockers, and finerenone are currently used in clinical practice to mitigate fibrotic progression, the development of novel anti-fibrotic agents remains an important unmet clinical need and a major focus of ongoing research. The authors’ previous study suggested that 17-DMAG, identified through drug repositioning, effectively attenuates kidney fibrosis [15]. In this context, early treatment-response assessment is essential. These findings demonstrate that MVI can serve as an imaging biomarker for early response assessment. Given the inherent advantages of US as a noninvasive and accessible modality, integration of MVI into the drug-development pipeline could provide a valuable tool for evaluating new therapeutic candidates and monitoring treatment efficacy in real time.

The predictive pattern of US parameters shifted over time in the assessment of 17-DMAG treatment response. VI was the only significant predictor at POD 3 but showed only a borderline, nonsignificant trend at POD 8 (P=0.051). In contrast, kidney size, PT, and RI became significant predictors at POD 8, whereas SWS did not significantly predict treatment response at either time point. This temporal shift likely reflects the progression of kidney injury, in which hemodynamic parameters such as VI serve as early indicators of microvascular compromise. As injury evolves and morphological distortion becomes more prominent, structural changes such as reduced kidney size and PT emerge as more definitive indicators of established disease. Although microvascular damage continues to intensify at later stages, the relative difference between the treated and untreated groups may diminish. This pattern suggests a potential floor effect in vascular parameters, whereby extensive rarefaction in both groups leads to convergence of values, even if some therapeutic benefit from 17-DMAG remains. Therefore, the emergence of RI, kidney size, and PT as significant predictors at later time points indicates a transition from functional microvascular fluctuation to irreversible structural remodeling. Clinically, kidney functional or structural impairment lasting less than 3 months is defined as acute kidney disease (AKD) [4]. Cortical thinning and decreased kidney size are known US findings in late AKD or CKD. Previous studies have shown that, in a UIRI mouse model, kidneys initially exhibit edematous swelling after ischemia, gradually atrophy, recover to basal volume after 1 week, and decrease in weight to two-thirds of baseline after 3 weeks [27]. This trajectory is consistent with the changes in US parameters observed in the present study. Given the substantially higher metabolic rate of mice compared with humans, POD 8 in a murine UIRI model may already represent late-stage AKD or early CKD. Thus, MVI may be an effective tool during the AKI stage, whereas conventional US parameters, including kidney size, PT, and RI, may be more informative in late AKD and CKD, when microvascular rarefaction has progressed.

Several limitations of the present study should be acknowledged. First, the sample size was relatively small. Although significant differences and high diagnostic performance were observed for parameters such as VI and RI, the small cohort may limit the generalizability of the findings and result in wide confidence intervals for AUC values. Consequently, these results should be interpreted as preliminary, and further validation in a larger independent cohort is required to confirm the robustness of these US parameters. Second, because of the limited number of events, defined as cases with pathologic fibrosis, multivariable logistic regression analysis could not be performed. Including multiple independent variables in a small dataset carries a high risk of overfitting, which could artificially inflate diagnostic accuracy. Therefore, this study focused on univariable ROC analysis for each parameter to preserve statistical integrity. Third, baseline US data were not available. Because of clinical workload and scheduling constraints, initial US examinations could not be performed, preventing evaluation of parameter changes relative to baseline. Although longitudinal changes often provide more sensitive insights than absolute values, the lack of baseline data precluded this comparative analysis. Future studies with larger sample sizes and dedicated imaging protocols would allow development of a combined diagnostic model and could potentially enhance overall predictive performance. Finally, because this was a controlled experimental study using an animal model, the cutoff values of US parameters cannot be directly applied to humans. Clinical application in human subjects will require further investigation to account for physiological complexity and the diverse etiologies of kidney fibrosis in clinical settings.

In conclusion, this study demonstrates that US vascular parameters (RI and VI) may serve as noninvasive surrogate markers for predicting the AKI-to-CKD transition and that VI may be useful for assessing early therapeutic responses to fibrosis-mitigating agents such as 17-DMAG. Although conventional US parameters, including kidney size and PT, are effective for identifying established structural remodeling in late-stage AKD, MVI offers a critical diagnostic window during the acute phase by detecting subtle microvascular compromise. Future clinical studies are warranted to validate these experimental findings and to determine whether early-stage US after AKI can be established as a tool for predicting disease progression and monitoring anti-fibrotic therapies in humans.

Author Contributions

Conceptualization: Moon MH, Lee MS. Data acquisition: Lee J, Sung CK, Lee JP, Lee MS. Data analysis or interpretation: Moon MH, Lee J, Sung CK, Lee JP, Lee MS. Drafting of the manuscript: Lee MS. Critical revision of the manuscript: Moon MH, Lee J, Sung CK, Lee JP, Lee MS. Approval of the final version of the manuscript: all authors.

Conflict of Interest

No potential conflict of interest relevant to this article was reported.

Acknowledgments

This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government Ministry of Science and ICT (MSIT) (No. RS-2024-00349731).

Supplementary Material

Supplementary Fig. 1.

Schematic flowchart of the study population and imaging schedule (https://doi.org/10.14366/usg.26139).

usg-26139-Supplementary-Fig-1.pdf

References

1. Khwaja A. KDIGO clinical practice guidelines for acute kidney injury. Nephron Clin Pract 2012;120:c179-c184.

2. Coca SG, Singanamala S, Parikh CR. Chronic kidney disease after acute kidney injury: a systematic review and meta-analysis. Kidney Int 2012;81:442-448.

3. Horne KL, Packington R, Monaghan J, Reilly T, Selby NM. Three-year outcomes after acute kidney injury: results of a prospective parallel group cohort study. BMJ Open 2017;7:e015316.

4. Koh ES, Chung S. Recent update on acute kidney injury-to-chronic kidney disease transition. Yonsei Med J 2024;65:247-256.

5. Moghazi S, Jones E, Schroepple J, Arya K, McClellan W, Hennigar RA, et al. Correlation of renal histopathology with sonographic findings. Kidney Int 2005;67:1515-1520.

6. Bige N, Levy PP, Callard P, Faintuch JM, Chigot V, Jousselin V, et al. Renal arterial resistive index is associated with severe histological changes and poor renal outcome during chronic kidney disease. BMC Nephrol 2012;13:139.

7. Darabont R, Mihalcea D, Vinereanu D. Current insights into the significance of the renal resistive index in kidney and cardiovascular disease. Diagnostics (Basel) 2023;13:1687.

8. Li Y, Chen L, Feng L, Li M. Contrast-enhanced ultrasonography for acute kidney injury: a systematic review and meta-analysis. Ultrasound Med Biol 2023;49:1930-1939.

9. Lee MS, Cho JY, Moon MH, Lee J, Lee JP, Shin N, et al. Comprehensive ultrasonographic evaluation of normal and fibrotic kidneys in a mouse model with an ultra-high-frequency transducer. Ultrasonography 2024;43:314-326.

10. Faubel S, Patel NU, Lockhart ME, Cadnapaphornchai MA. Renal relevant radiology: use of ultrasonography in patients with AKI. Clin J Am Soc Nephrol 2014;9:382-394.

11. Varanasi P, Bhasin-Chhabra B, Koratala A. Point-of-care ultrasonography in acute kidney injury. J Transl Crit Care Med 2024;6:e24-00005.

12. Jiang J, Zhou J, Bao J, Gao H. Current applications and research trends of ultrasound examination in acute kidney injury assessment: a bibliometric analysis. Int Urol Nephrol 2025;57:1933-1944.

13. Yoon HE, Kim DW, Kim D, Kim Y, Shin SJ, Shin YR. A pilot trial to evaluate the clinical usefulness of contrast-enhanced ultrasound in predicting renal outcomes in patients with acute kidney injury. PLoS One 2020;15:e0235130.

14. Cao W, Cui S, Yang L, Wu C, Liu J, Yang F, et al. Contrast-enhanced ultrasound for assessing renal perfusion impairment and predicting acute kidney injury to chronic kidney disease progression. Antioxid Redox Signal 2017;27:1397-1411.

15. Lee SM, Lee MS, Jung HR, Lee J, Cho B, Jin W, et al. Therapeutic potential of heat shock protein 90 inhibitor 17-DMAG in regulating METTL3 for kidney fibrosis treatment. J Am Soc Nephrol 2026;37:1404-1422.

16. Fu Y, Tang C, Cai J, Chen G, Zhang D, Dong Z. Rodent models of AKI-CKD transition. Am J Physiol Renal Physiol 2018;315:F1098-F1106.

17. Kadioglu A. Renal measurements, including length, parenchymal thickness, and medullary pyramid thickness, in healthy children: what are the normative ultrasound values? AJR Am J Roentgenol 2010;194:509-515.

18. Nightingale K. Acoustic radiation force impulse (ARFI) imaging: a review. Curr Med Imaging Rev 2011;7:328-339.

19. Saftoiu A, Gilja OH, Sidhu PS, Dietrich CF, Cantisani V, Amy D, et al. The EFSUMB guidelines and recommendations for the clinical practice of elastography in non-hepatic applications: update 2018. Ultraschall Med 2019;40:425-453.

20. Rule AD, Amer H, Cornell LD, Taler SJ, Cosio FG, Kremers WK, et al. The association between age and nephrosclerosis on renal biopsy among healthy adults. Ann Intern Med 2010;152:561-567.

21. Wang X, Lieske JC, Alexander MP, Jayachandran M, Denic A, Mathew J, et al. Tubulointerstitial fibrosis of living donor kidneys associates with urinary monocyte chemoattractant protein 1. Am J Nephrol 2016;43:454-459.

22. Oh SW. The cause and treatment of acute kidney injury. Korean J Med 2019;94:315-321.

23. Ninet S, Schnell D, Dewitte A, Zeni F, Meziani F, Darmon M. Doppler-based renal resistive index for prediction of renal dysfunction reversibility: a systematic review and meta-analysis. J Crit Care 2015;30:629-635.

24. Post EH, Kellum JA, Bellomo R, Vincent JL. Renal perfusion in sepsis: from macro- to microcirculation. Kidney Int 2017;91:45-60.

25. Uchino S, Kellum JA, Bellomo R, Doig GS, Morimatsu H, Morgera S, et al. Acute renal failure in critically ill patients: a multinational, multicenter study. JAMA 2005;294:813-818.

26. Yoo J, Je BK, Choo JY. Ultrasonographic demonstration of the tissue microvasculature in children: microvascular ultrasonography versus conventional color Doppler ultrasonography. Korean J Radiol 2020;21:146-158.

27. Zager RA, Johnson AC, Becker K. Acute unilateral ischemic renal injury induces progressive renal inflammation, lipid accumulation, histone modification, and “end-stage” kidney disease. Am J Physiol Renal Physiol 2011;301:F1334-F1345.

Fig. 1.

Ultrasound images of a mouse kidney after unilateral ischemia–reperfusion injury.

A–D. B-mode and microvascular images of a mouse kidney were obtained on postoperative day (POD) 3 and POD 8 after unilateral ischemia–reperfusion injury. On the POD 3 B-mode image, the layered structure of the injured kidney is relatively preserved (A), with diminished flow signals on microvascular imaging (B). On POD 8, the renal parenchymal echotexture becomes heterogeneous, with loss of the layered structure (C). On microvascular imaging, flow signals are further decreased (D). E. Sirius red staining shows multifocal structural distortion with red-stained fibrosis. F. Representative fibrosis quantification image from the same Sirius red–stained specimen shown in (E) (×200), analyzed using ImageJ software. The fibrosis quantification result shows a %Area value of 10.28%, as displayed in the Results windows. Scale bars=200 μm (E), 50 μm (F). ROI, region of interest; SMI, superb microvascular imaging; %Area, Sirius red–positive area relative to the total tissue specimen area.

Fig. 2.

Ultrasound images of a mouse kidney after unilateral ischemia–reperfusion injury with 17-DMAG treatment.

A–D. B-mode and microvascular images of a mouse kidney were obtained on postoperative day (POD) 3 and POD 8 after unilateral ischemia–reperfusion injury with 17-DMAG treatment. The layered structure of the renal parenchyma is relatively preserved on the POD 3 B-mode image (A). Flow signals are more evident than in the untreated kidney on microvascular imaging (B). On POD 8, kidney size is preserved on B-mode imaging during 17-DMAG treatment (C), without a marked decrease in flow signals on microvascular imaging (D). E. Sirius red staining shows less structural distortion and fibrosis than in the untreated kidney. F. Representative fibrosis quantification image from the same Sirius red–stained specimen shown in (E) (×200), analyzed using ImageJ software. The fibrosis quantification result shows a %Area value of 3.51%, as displayed in the Results windows. Scale bars=200 μm (E), 50 μm (F). ROI, region of interest; SMI, superb microvascular imaging; %Area, Sirius red–positive area relative to the total tissue specimen area.

Fig. 3.

Correlogram of ultrasound parameters measured 3 days after unilateral ischemia–reperfusion injury and fibrotic area on histological specimens.

PT, parenchymal thickness; VI, vascular index; SWS, shear-wave speed; %Area, Sirius red–positive area relative to the total tissue specimen area; RI, resistive index.

Fig. 4.

Correlogram of ultrasound parameters measured 8 days after unilateral ischemia–reperfusion injury and fibrotic area on histological specimens.

PT, parenchymal thickness; VI, vascular index; SWS, shear-wave speed; %Area, Sirius red–positive area relative to the total tissue specimen area; RI, resistive index.

Table 1.

US parameters on PODs 3 and 8

	Sham (n=6)	UIRI			P1^a)	P2^b)
	Sham (n=6)	17-DMAG untreated (n=6)	17-DMAG treated (n=6)	All UIRI kidneys (n=12)	P1^a)	P2^b)
POD 3
No.	6	6	6	12
Size (mm)	10.69±0.59	10.11±0.50	10.13±0.31	10.12±0.40	0.054	0.810
PT (mm)	1.90±0.17	1.43±0.33	1.57±0.40	1.50±0.35	0.030	0.628
RI	0.59±0.04	0.75±0.06	0.67±0.05	0.71±0.06	0.002	0.052
VI (%)	22.25±3.18	8.43±5.95	15.43±4.35	11.92±6.17	<0.001	0.093
SWS (m/s)	1.62±0.10	1.85±0.12	1.74±0.11	1.79±0.12	0.024	0.200
POD 8
No.	16	12	12	24
Size (mm)	11.30±0.50	6.94±2.34	9.71±1.57	8.32±2.41	<0.001	0.002
PT (mm)	1.68±0.23	0.87±0.26	1.49±0.40	1.18±0.46	<0.001	0.001
RI	0.62±0.03	0.78±0.07	0.70±0.06	0.74±0.08	<0.001	0.013
VI (%)	21.69±2.07	5.73±5.84	13.85±5.75	9.79±7.02	<0.001	0.003
SWS (m/s)	1.62±0.04	1.82±0.14	1.75±0.06	1.78±0.11	<0.001	0.099

Values are presented as mean±SD.

US, ultrasonography; POD, postoperative day; UIRI, unilateral ischemia-reperfusion injury; PT, parenchymal thickness; RI, resistive index; VI, vascular index; SWS, shear-wave speed; SD, standard deviation.

^a) P-value between sham operated kidney and all UIRI kidneys.

^b) P-value between UIRI kidneys treated and untreated with 17-DMAG.

Table 2.

US parameters for predicting AKI-to-CKD transition

Parameter	AUC	P-value^a)	95% CI	Cutoff	Sensitivity	Specificity
POD 3
Size (mm)	0.682	0.221	0.385–0.961	9.79	0.429	1.000
PT (mm)	0.786	0.050	0.545–0.978	1.60	0.857	0.727
RI	0.890	0.007	0.706–1.000	0.65	1.000	0.727
VI (%)	0.948	0.002	0.792–1.000	10.35	0.857	1.000
SWS (m/s)	0.871	0.016	0.644–1.000	1.81	0.667	0.909
POD 8
Size (mm)	0.904	<0.001	0.768–0.992	9.84	0.875	0.875
PT (mm)	0.935	<0.001	0.797–1.000	1.4	0.938	0.917
RI	0.926	<0.001	0.824–0.994	0.69	0.857	0.875
VI (%)	0.870	<0.001	0.737–0.972	13.6	0.812	0.833
SWS (m/s)	0.854	<0.001	0.693–0.975	1.76	0.714	0.917

US, ultrasonography; AKI, acute kidney injury; CKD, chronic kidney disease; AUC, area under the receiver-operating characteristic curve; CI, confidence interval; POD, postoperative day; PT, parenchymal thickness; RI, resistive index; VI, vascular index; SWS, shear-wave speed;

^a) P-value indicates statistical significance of AUC versus random chance (AUC, 0.5).

Table 3.

US parameters for predicting 17-DMAG treatment response

Parameter	AUC	P-value^a)	95% CI	Cutoff	Sensitivity	Specificity
POD 3
Size (mm)	0.543	0.871	0.167–0.886	9.79	0.429	1.000
PT (mm)	0.643	0.461	0.271–0.971	1.4	0.714	0.600
RI	0.757	0.163	0.444–1.000	0.65	1.000	0.400
VI (%)	0.886	0.030	0.625–1.000	10.35	0.857	1.000
SWS (m/s)	0.783	0.143	0.433–1.000	1.89	0.500	1.000
POD 8
Size (mm)	0.828	0.011	0.650–0.975	8.48	0.688	1.000
PT (mm)	0.926	0.001	0.785–1.000	1.2	0.812	1.000
RI	0.848	0.008	0.647–0.982	0.75	0.714	1.000
VI (%)	0.750	0.051	0.526–0.938	9.5	0.688	0.875
SWS (m/s)	0.723	0.094	0.479–0.917	1.76	0.714	0.750

US, ultrasonography; AUC, area under the receiver-operating characteristic curve; CI, confidence interval; PT, parenchymal thickness; RI, resistive index; VI, vascular index; SWS, shear-wave speed.

^a) P-value indicates statistical significance of AUC versus random chance (AUC, 0.5).