Kupffer phase echo level ratio in contrast-enhanced ultrasound for differentiating malignant liver tumors

Introduction

Hepatic malignant tumors primarily include hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (IHC), and liver metastases. Among them, HCC is a unique type of malignancy that can be directly diagnosed through contrast-enhanced imaging modalities, such as computed tomography (CT), magnetic resonance imaging (MRI), and contrast-enhanced ultrasound (CEUS), allowing patients to proceed with treatment without requiring a biopsy (1,2). Among these imaging techniques, CEUS offers several advantages over CT and MRI, including real-time dynamic observation, cost-effectiveness, and a favorable safety profile (3). Contrast agents used in CEUS can be categorized into two types: pure blood-pool agents, such as SonoVue (Bracco S.P.A., Milan, Italy), and combined blood-pool and Kupffer cell agents, such as Sonazoid (GE Healthcare, Oslo, Norway). The primary component of Sonazoid is perfluorobutane, which has the unique ability to be phagocytosed by hepatic macrophages (Kupffer cells), leading to sustained liver enhancement for over an hour (4,5). This enhancement phase, which extends beyond the conventional blood-pool phase, is referred to as the Kupffer phase (6,7). Since malignant tumors disrupt Kupffer cells, they typically appear as hypoenhancing defects during the Kupffer phase (8).

Recent studies have shown that non-HCC malignant liver tumors tend to exhibit more pronounced hypoenhancement during the Kupffer phase compared to HCC (9,10). Based on this characteristic, some researchers have proposed to categorize the degree of defects—by comparing lesions to the surrounding normal liver parenchyma—into mild and marked defects. This classification was then used to modify the CEUS Liver Imaging Reporting and Data System (LI-RADS) published by the American College of Radiology (ACR). The modified LR-5 category demonstrated significantly improved sensitivity and accuracy in diagnosing HCC, with a non-significant reduction in specificity (11,12). However, qualitative assessment of defect severity may be influenced by variations in ultrasound equipment, settings, and examiner subjectivity, potentially impacting diagnostic consistency and reliability.

The time-intensity curve (TIC) is a commonly used method for quantitative analysis in CEUS, providing a range of parameters that effectively reflect the perfusion characteristics of lesions (13,14). However, current applications of TIC are predominantly focused on the vascular phases. If the degree of defect during the Kupffer phase could also be quantitatively assessed, it may enhance the objectivity in evaluating tumor characteristics. Some researchers have explored this by calculating the echo level ratio (ELR) between the lesion and the surrounding normal liver parenchyma during the Kupffer phase, thereby assessing the relative extent of defects. Furthermore, a correlation has been demonstrated between ELR values and the histological differentiation of HCC (15,16).

To date, no study has systematically evaluated the differences in quantitative parameters of lesions during the Kupffer phase among various types of malignant liver tumors. This study aimed to assess the differences in ELR values between HCC and non-HCC malignant tumors, and to compare this parameter with vascular phase parameters obtained from TIC analysis. We present this article in accordance with the STROBE reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1229/rc).

Methods

Patients

This retrospective study included patients with malignant liver tumors who underwent CEUS at Beijing Tsinghua Changgung Hospital between September 2023 and January 2025. The inclusion criteria were as follows: (I) pathological confirmation obtained via biopsy or surgery within one week after CEUS examination; (II) target lesion with a minimum diameter of ≥1.0 cm to ensure adequate image quality for quantitative CEUS analysis; and (III) no prior chemotherapy, radiotherapy, surgery, or interventional treatment before the CEUS examination. The exclusion criteria were: (I) poor image quality or excessive respiratory motion preventing quantitative image analysis; (II) lack of continuous dynamic imaging within the first 3 minutes after contrast administration and absence of Kupffer phase images; and (III) pathological diagnosis of combined hepatocellular carcinoma-cholangiocarcinoma (cHCC-CCA) or other rare pathological types. Although cHCC-CCA is clinically important due to its dual hepatocellular and cholangiocarcinoma characteristics, only 15 cases were identified during the study period. The small sample size was insufficient for reliable statistical analysis; therefore, these patients were excluded to maintain the validity of the comparative results. A flowchart of patient selection is shown in Figure 1. A total of 221 patients (221 lesions) were included in the final analysis. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study protocol was approved by the Ethics Committee of Beijing Tsinghua Changgung Hospital (No. 25280-4-01). Given the retrospective nature of the study, informed consent was waived.

Figure 1 Flowchart of patient inclusion. cHCC-CCA, combined hepatocellular carcinoma-cholangiocarcinoma; HCC, hepatocellular carcinoma; IHC, intrahepatic cholangiocarcinoma.

Ultrasound and CEUS scans

All examinations were performed using a LOGIQ E10s ultrasound system (GE Healthcare, Oslo, Norway) equipped with a convex array transducer operating at 1–5 MHz, with the frequency adjusted according to the patient’s acoustic window and lesion depth to achieve optimal imaging. Patients were examined in the supine or left lateral decubitus position. After abdominal exposure, liver lesions were assessed. For patients with multiple intrahepatic lesions, the one with the best imaging quality was selected as the target. In the B-mode ultrasound, lesion characteristics such as size, echogenicity, margin, and depth were recorded. Subsequently, CEUS was performed with a mechanical index (MI) set between 0.18 and 0.22. The contrast agent (Sonazoid; GE Healthcare, Oslo, Norway; 0.01 mL/kg body weight) was administered via bolus injection through the median cubital vein, followed by a 5-mL saline flush. Starting from contrast injection, a continuous 180-second video was acquired for vascular phase TIC analysis. At 10 minutes post-injection, patients were instructed to hold their breath, and a 10-second stable video clip was acquired for quantitative analysis during the Kupffer phase, effectively balancing image stability with patient comfort and compliance.

Quantitative parameter analysis

TIC analysis was first performed on the vascular phase. A region of interest (ROI) was placed within the lesion using the 180-second video. For TIC analysis, circular ROIs were drawn in the solid portion of the lesion. The diameter of the ROI was adjusted between 1.0 and 1.5 cm based on the size of the analyzable solid component. Areas of necrosis, calcification, major vessels, and bile ducts were carefully avoided. If the diameter of the analyzable solid component exceeded 3 cm, multiple ROIs were selected at the same depth, and the mean value was used for analysis to better represent the entire lesion’s perfusion characteristics. Based on previous quantitative CEUS studies on malignant liver tumors (17,18), the following parameters were extracted for analysis: peak intensity (PI), time to peak (TTP), wash-in area under the curve (WiAUC), wash-out area under the curve (WoAUC), and the combined wash-in and wash-out area under the curve (WiWoAUC).

For Kupffer phase analysis, a circular ROI was placed within the lesion and an equal-sized ROI in adjacent liver parenchyma at the same depth. Because the raw data extracted from the ultrasound system are expressed as echo level (EL, dB), we calculated the ELR by dividing the mean EL of the lesion by that of the liver parenchyma. According to the conversion formula described by von Volkmann HL et al. (19), grayscale intensity (Y) can be derived from EL using the following equation: Y = 255 + (255/99) × EL (dB), where 0 dB corresponds to maximum brightness (white, gray level =255) and −99 dB corresponds to minimum brightness (black, gray level =0).

All image analyses were independently performed by two radiologists with over 5 years of experience in liver CEUS.

Statistical analysis

Continuous variables were expressed as mean ± standard deviation (SD) if normally distributed, or as median with the 25th–75th percentiles if not. Comparisons between two groups were performed using the Student’s t-test or the Mann-Whitney U test, depending on data distribution. Categorical variables were presented as counts and percentages (n, %) and compared using the Pearson Chi-squared test or Fisher’s exact test, as appropriate. Comparisons of general clinical characteristics and conventional ultrasound parameters among different pathological types were conducted using one-way analysis of variance (ANOVA). If the ANOVA showed statistically significant differences, post-hoc comparisons were performed using the Bonferroni correction. For quantitative parameters showing significant differences between groups, receiver operating characteristic (ROC) curves were constructed to evaluate the diagnostic performance of individual parameters and their combinations. The areas under the curve (AUCs) were compared using the DeLong test. Internal validation was performed using bootstrap resampling to assess the robustness of the ELR cutoff and the AUC estimates. One thousand bootstrap samples were generated by sampling with replacement from the original cohort. For each bootstrap sample we recalculated the ROC curve, AUC, and the optimal cutoff defined by the maximum Youden index; 95% confidence intervals (CIs) for the AUC were derived from the empirical distribution of bootstrap estimates. To assess intra- and interobserver reproducibility of the quantitative measurements, 20 patients were randomly selected, and intraclass correlation coefficients (ICCs) were calculated. An ICC ≥0.75 was considered excellent; 0.60–0.74, good; 0.40–0.59, moderate; and <0.40, poor agreement.

All statistical analyses were performed using SPSS version 23.0 (IBM Corp., Armonk, NY, USA). A two-sided P value of <0.05 was considered statistically significant.

Results

Clinical and ultrasound characteristics

A total of 221 patients were ultimately included in this study, with one lesion evaluated per patient, resulting in 221 lesions in total. The patients were categorized into the HCC group (n=97) and the non-HCC group (n=124) based on pathological diagnosis. Among the HCC cases, 29 were well differentiated, 51 moderately differentiated, and 17 poorly to moderately/poorly differentiated. The non-HCC group included 49 cases of IHC and 75 cases of liver metastases. The primary sites of metastatic tumors were as follows: pancreas (n=27), gallbladder or bile ducts (n=21), gastrointestinal tract (n=19), breast (n=4), kidney (n=1), lung (n=1), and other origins (n=2).

The mean ages of patients with HCC, IHC, and metastatic tumors were 58.1±12.8, 63.7±10.5, and 61.7±11.8 years, respectively, with a statistically significant difference among groups (P=0.020). No significant differences were observed in sex distribution (P=0.130) or body mass index (BMI) (P=0.759) among the three groups.

In terms of grayscale ultrasound features, the average lesion diameters in the HCC, IHC, and metastasis groups were 4.47±1.13, 4.22±1.30, and 4.00±1.36 cm, respectively. Hypoechoic appearance was observed in 81.4% (79/97) of HCCs, 89.8% (44/49) of IHCs, and 88.0% (66/75) of metastases. Well-defined margins were seen in 62.9% (61/97), 67.3% (33/49), and 78.7% (59/75) of the lesions in the HCC, IHC, and metastasis groups, respectively. Homogeneous echotexture was observed in 61.9% (60/97) of HCCs, 79.6% (39/49) of IHCs, and 69.3% (52/75) of metastases. No statistically significant differences were found among the three groups regarding lesion location or intralesional vascularity (details in Table 1).

In CEUS characteristics, arterial phase hyperenhancement (APHE) was observed in 92.8% (90/97) of HCCs, 87.8% (43/49) of IHCs, and 80.0% (60/75) of metastases. In the Kupffer phase, hypoenhancement was observed in 86.6% (84/97), 93.9% (46/49), and 90.7% (68/75) of lesions in the respective groups.

CEUS quantitative parameters

Among vascular phase parameters, the HCC group demonstrated significantly higher PI [−42.02 (−39.42, −46.22) vs. −45.55 (−41.60, −51.60) dB, P<0.001], WiWoAUC [1,748.52 (1,563.75, 1,895.42) vs. 1,539.78 (1,353.90, 1,720.16), P<0.001], and WoAUC [1,545.47 (1,373.81, 1,688.25) vs. 1,319.34 (1,150.68, 1,551.29), P<0.001] than the non-HCC group. No significant differences were found for other TIC-derived parameters.

For the Kupffer phase, the ELR was significantly lower in the HCC group compared to the non-HCC group [1.20 (1.12, 1.26) vs. 1.31 (1.21, 1.41), P<0.001]. Figure 2 illustrates representative Kupffer phase ELR of HCC, IHC and metastasis.

Figure 2 ELR of the Kupffer phase in different types of malignant liver tumors. (A) A 54-year-old male patient with moderately differentiated hepatocellular carcinoma. The mean echo level of the adjacent liver parenchyma was −49.313 dB, and that of the lesion was −57.080 dB (red box). The ELR was 1.16. (B) A 58-year-old female patient with intrahepatic cholangiocarcinoma (small duct type). The mean echo level of the adjacent liver parenchyma was −50.358 dB, and that of the lesion was −66.558 dB (red box). The ELR was 1.32. (C) A 57-year-old male patient with liver metastasis from gallbladder adenocarcinoma. The mean echo level of the adjacent liver parenchyma was −51.528 dB, and that of the lesion was −69.255 dB (red box). The ELR was 1.34. A, difference between baseline and peak; ArT, arrival time; AUC, area under the curve; B, baseline; C, indicator for the wash-in gradient of curve; ELR, echo level ratio; Grad., gradient; k, indicator for the wash-out gradient of curve; MGrad., max gradient; MGT, max gradient time; MSE, mean square error; PI, peak intensity; TtoP, time to peak; TWH, time width at half maximum; TWR, time width ratio; WiAUC, wash-in area under the curve; WoAUC, wash-out area under the curve.

ROC curve analysis of quantitative parameters

The diagnostic performance of each quantitative parameter in distinguishing HCC from non-HCC lesions is summarized in Table 2. The AUC for PI, WoAUC, WiWoAUC, and ELR were 0.671, 0.713, 0.712, and 0.741, respectively, with no statistically significant differences between them. The combination of parameters yielded an AUC of 0.849, which was superior to any single parameter (Figure 3).

Figure 3 ROC curve of quantitative parameters for differentiating HCC from non-HCC hepatic malignancies. The AUC values were 0.671 for PI, 0.713 for WoAUC, 0.712 for WiWoAUC, and 0.741 for ELR. The combined diagnostic model achieved an AUC of 0.849, which was significantly higher than any single parameter. Pairwise comparisons among individual parameters showed no statistically significant differences (all P>0.05). ELR, echo level ratio; HCC, hepatocellular carcinoma; PI, peak intensity; ROC, receiver operating characteristic; WiWoAUC, wash-in and wash-out area under the curve; WoAUC, wash-out area under the curve.

Internal validation using 1,000 bootstrap resamples was performed to evaluate the robustness of the ELR cutoff and the AUC. The original AUC for ELR was 0.741; the bootstrap-derived 95% CI for the AUC was 0.701–0.781. The optimal cutoff in the original cohort was 1.30; across bootstrap resamples the median optimal cutoff was 1.29 (IQR, 1.25–1.33), indicating good stability of the threshold.

Intra- and inter-observer variability of quantitative parameters

The intra- and inter-observer reproducibility for the quantitative parameters is shown in Table 3. All parameters demonstrated excellent intra- and inter-observer agreement, with ICCs ranging from 0.868 to 0.984.

Discussion

This study evaluated the diagnostic value of quantitative Sonazoid CEUS parameters in differentiating HCC from non-HCC malignant liver tumors during the Kupffer phase. The key findings demonstrated that the ELR, reflecting the relative contrast defect in the Kupffer phase, was significantly lower in the HCC group compared to the non-HCC group. In our cohort, the optimal ELR cut-off value derived from ROC analysis was 1.30, yielding a sensitivity of 52.4% and a specificity of 85.6% for distinguishing HCC from non-HCC malignancies. This finding indicates that an ELR below 1.30 is strongly suggestive of HCC, whereas higher ELR values are more likely associated with non-HCC tumors. The sensitivity of ELR at the selected cutoff was modest, but its high specificity reduces false-positive diagnoses of HCC. Clinically, this means ELR should not be used as a stand-alone screening tool, as some HCC cases could be missed. Instead, ELR is most valuable when combined with vascular phase CEUS, especially in cases with inconclusive vascular findings. In such settings, ELR can enhance diagnostic specificity, thereby reducing unnecessary biopsies or overtreatment, while maintaining high overall accuracy when combined with vascular phase analysis. Among the vascular phase parameters, PI, WoAUC, and WiWoAUC were also significantly higher in HCCs. However, ELR showed the highest AUC among individual parameters, and the combination of Kupffer and vascular phase parameters yielded superior diagnostic performance. While multiparametric MRI with hepatobiliary agents remains a powerful benchmark for liver lesion characterization (20,21), ELR and vascular phase parameters derived from Sonazoid CEUS provides a rapid, accessible, and repeatable alternative or adjunct. Additionally, all quantitative parameters exhibited excellent intra- and inter-observer reproducibility, supporting their reliability for clinical use.

The observed differences in ELR between HCC and non-HCC lesions can be attributed to the distinct biological behavior and cellular composition of these tumor types. Pathological studies have shown that HCCs, particularly well-differentiated ones, may retain a substantial number of Kupffer cells, with some highly differentiated HCCs demonstrating Kupffer cell densities comparable to non-tumorous liver parenchyma (22,23). In contrast, non-HCC malignancies such as IHC and metastatic tumors typically lack Kupffer cells. Moreover, IHC are known to disrupt Kupffer cell function, leading to more pronounced perfusion defects during the Kupffer phase (24,25). In this context, the ELR provides a simple yet objective quantitative measure of Kupffer cell function within the lesion and thus offers valuable diagnostic insight.

In comparison, vascular phase TIC parameters such as PI, WoAUC, and WiWoAUC primarily reflect the hemodynamic characteristics of the lesion. Although these parameters were higher in HCCs, they are affected by factors such as nodule size, lesion depth, and respiratory motion, which may reduce their reliability (26,27). The ELR, by quantifying the Kupffer phase hypoenhancement relative to normal liver tissue, provides complementary information that enhances diagnostic specificity. Therefore, incorporating ELR into the CEUS evaluation protocol may improve diagnostic accuracy.

Previous studies have explored the diagnostic value of Kupffer phase imaging in CEUS through qualitative assessments. Researchers have proposed refinements to the CEUS LI-RADS algorithm based on the degree of Kupffer phase hypoenhancement. Specifically, lesions ≥10 mm with APHE, no washout, and Kupffer phase defects were reclassified from LR-4 to LR-5, while lesions ≥10 mm with APHE, early washout, and mild Kupffer-phase defects were reallocated from LR-M to LR-5 (11,12). This modified algorithm significantly improved diagnostic sensitivity for HCC, with no statistically significant decrease in specificity (11,12). Further, combining this revised CEUS LI-RADS with CT/MRI LI-RADS was shown to enhance overall diagnostic sensitivity for HCC without compromising specificity (28).

However, quantitative studies of the Kupffer phase remain limited. Two investigations have demonstrated a correlation between the intensity ratio (the lesion and the surrounding liver parenchyma) and the histological differentiation of HCC, supported respectively by CD68 immunohistochemistry (16) and superparamagnetic iron oxide-enhanced MRI (SPIO-MRI) (15). Huang et al. reported that the signal intensity difference between HCC lesions and surrounding liver parenchyma in the Kupffer phase could reflect Ki-67 expression levels, providing insight into tumor proliferative activity (29). Similarly, Wang et al. showed that Kupffer phase intensity in biliary atresia rats model corresponded well with the number of Kupffer cells quantified by immunohistochemistry (30). These findings indicate that Kupffer phase quantification may provide objective markers that enhance both diagnostic accuracy and biological characterization of liver lesions.

Compared to these studies, our research extends the application of Kupffer phase quantitative analysis by directly comparing ELR values between HCC and non-HCC malignancies in a large cohort. Our results show that ELR can differentiate HCC from non-HCC tumors and provides added value beyond vascular-phase TIC parameters. This highlights the role of Kupffer-phase quantification in the noninvasive assessment of liver malignancies. In practical terms, ELR could be integrated into CEUS workflows as an adjunctive ‘tie-breaker’ parameter. Within the CEUS LI-RADS framework, when vascular-phase features are equivocal, a low ELR value may support categorization toward HCC, whereas a higher ELR may favor non-HCC malignancies and help avoid false-positive LR-5 assignment. Such an approach would align with existing LI-RADS principles by providing an additional quantitative marker without altering the fundamental algorithm.

In addition to conventional quantitative analysis, radiomics analysis based on Kupffer phase CEUS has recently emerged as a promising research direction. Preliminary studies suggest that Kupffer phase radiomic features may help differentiate highly differentiated HCC from atypical benign liver lesions (31), predict tumor grade and Ki-67 expression (32,33), and assess microvascular invasion (34). These findings complement our study, as both radiomics and ELR analysis emphasize the diagnostic potential of Kupffer phase CEUS.

This study has several limitations. First, this was a single-center retrospective analysis, and therefore external multicenter validation is required to confirm the generalizability of our findings. Second, ROI placement for quantitative analysis was manually performed, which may introduce subjectivity despite good inter- and intra-observer agreement. Future work should investigate artificial intelligence (AI)-based automated segmentation methods to further enhance reproducibility. Third, quantitative parameters may vary across different ultrasound systems and vendors, limiting the generalizability and standardization of this method in broader clinical settings. Another limitation is that we did not perform multivariable logistic regression including clinical covariates such as age, lesion size, or histological differentiation. In the future, multicenter prospective studies with standardized protocols are needed to validate ELR. Automated or AI-assisted quantification may further enhance reproducibility, and combining ELR with clinical factors through multivariable or machine learning models could provide a more comprehensive diagnostic tool. Beyond diagnosis, ELR may also support translational applications in image-guided interventions. In particular, integrating ELR with emerging techniques such as focused ultrasound ablation could aid in treatment planning, lesion targeting, and therapy monitoring, thereby extending the role of CEUS from diagnosis to therapy (35).

Conclusions

This study demonstrated that quantitative analysis of Kupffer phase CEUS using the ELR can effectively distinguish HCC from non-HCC liver malignancies. Compared with vascular-phase parameters, ELR showed superior diagnostic performance and added value when used in combination. These findings highlight the potential of Kupffer phase quantification as a non-invasive, objective tool for characterizing liver tumors. Further validation in larger, multicenter studies is warranted to confirm its clinical utility and support its integration into diagnostic workflows.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1229/rc

Data Sharing Statement: Available https://qims.amegroups.com/article/view/10.21037/qims-2025-1229/dss

Funding: This work was supported by Beijing Municipal Administration of Hospitals Incubating Program (No. PX2025033).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1229/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study protocol was approved by the Ethics Committee of Beijing Tsinghua Changgung Hospital (No. 25280-4-01). Given the retrospective nature of the study, informed consent was waived.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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