Dual-energy computed tomography-derived electron density in the differential diagnosis of focal hepatic lesions: initial experience

Introduction

There are various types of focal hepatic lesions (FHLs), including benign and malignant tumors, and accurate imaging diagnosis is important for patient management and predicting patient prognosis. FHLs are differentially diagnosed by dynamic contrast-enhanced computed tomography (CT) and magnetic resonance imaging (MRI), which can provide information on the hemodynamics of FHLs. The pathological diagnosis of a biopsy or excised specimen is not always necessary once the diagnosis is confirmed by typical imaging findings on CT or MRI. Therefore, imaging technology is crucial in the differential diagnosis of FHLs (1,2).

Magnetic resonance (MR) images provide better contrast resolution than CT images, even without a dynamic contrast study. Recent studies have reported that non-contrast MRI can accurately differentiate between malignant and benign tumors with high precision (3,4). However, MRI has some disadvantages, including the requirement for special hardware, high cost, and susceptibility to the effects of heartbeats and peristaltic movements of the digestive tract. In contrast, CT examinations are widely used for the imaging diagnosis of FHLs because of their simplicity, shorter scan time, and fewer artifacts. However, differentially diagnosing FHLs based on non-contrast CT images alone is challenging, and using contrast agents is essential (5).

Dual-energy CT (DECT), an advanced imaging technique, has been increasingly used in clinical practice, and the usefulness of its material discrimination ability has been reported for the analysis of urinary calculus composition (6), adrenal nodule properties (7), and discrimination between hemorrhage and contrast media (8). Furthermore, the clinical utility of electron density (ED), which can be calculated using DECT, has been reported for brain tumors (9) and bone lesions (10). The greatest advantage of ED is the lack of need for contrast agents, which may also be applied to the differential diagnosis of FHLs. However, the clinical utility of ED in the liver has not been adequately reported.

The purpose of this study was to evaluate the ED of FHLs by focusing on malignant tumors [hepatocellular carcinoma (HCCs) and liver metastases] and a limited subset of benign tumors (hepatic hemangiomas and cysts), and to assess the diagnostic performance of ED. We present this article in accordance with the STARD reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2024-2592/rc).

Methods

Study population

In this single-center retrospective study, we reviewed consecutive outpatients and inpatients who underwent upper abdominal enhanced DECT at University of Fukui Hospital between October 2021 and March 2023. The indications for CT examination included further detailed examination of FHLs detected using ultrasonography or periodic follow-up of patients with chronic liver disease. Patients were eligible for the study if they fulfilled the following criteria: (I) older than 30 years old; (II) diagnosed with FHLs >5 mm in diameter based on imaging findings; and (III) no history of prior treatment for FHLs, including hepatectomy, percutaneous local therapy, and hepatic artery catheterization. Of the 107 patients who met the above criteria, patients with the following conditions were excluded: motion artifacts (n=5), weight >90 kg (n=4), HCC without typical CT findings (n=3), and diagnosed with other FHLs such as focal nodular hyperplasia (n=6). In typical hypervascular HCC, the contrast enhancement pattern is relatively uniform. However, atypical HCCs—such as well-differentiated tumors or those with fatty change or fibrosis—are presumed to exhibit substantial heterogeneity in both histological background and ED. For this reason, we determined that they were not suitable for initial-stage analysis. Including such variations in an early study with a small cohort could confound the results and hinder the ability to draw clear preliminary conclusions. Therefore, we excluded atypical HCCs, considering it essential to first establish reference values specifically for typical HCC as a foundational step.

Eighty-nine patients [43 with malignant tumors and 46 with benign lesions, with an average diameter of 22.4 mm (range, 5.8–91.2 mm)] were finally included (Figure 1). Malignant tumors comprised 24 HCCs and 19 liver metastases. The benign lesions comprised 28 hepatic hemangiomas and 18 hepatic cysts. The 19 patients with liver metastases had primary malignancies, including colorectal cancer (n=9), pancreatic cancer (n=4), lung adenocarcinoma (n=2), uterine cancer (n=2), renal cell carcinoma (n=1), and gallbladder carcinoma (n=1). The albumin-bilirubin (ALBI) score was calculated as follows to compare the background liver status in each group: [log₁₀ bilirubin (µmol/L) × 0.66] + [albumin (g/L) × (−0.0852)]. The ALBI score as a predictor of hepatic dysfunction was classified into three grades: ≤−2.60, grade 1; <−2.60 to ≤−1.39, grade 2; and >1.39, grade 3 (11). The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the research ethics committee of University of Fukui (No. 20190164) and individual consent for this retrospective analysis was waived.

Figure 1 Flowchart of the study population. CT, computed tomography; FHL, focal hepatic lesion; HCC, hepatocellular carcinoma.

Imaging protocol of CT

All CT examinations were performed using a CT system (SOMATOM Force; Siemens Healthcare, Forchheim, Germany). Following unenhanced DECT, a dynamic contrast material-enhanced study was conducted for 40 s (arterial phase), 70 s (portal phase), and 180 s (equilibrium phase) after the completion of an intravenous injection of nonionic contrast material (Iopamiron 370; Bayer Health Care, Osaka, Japan) (600 mg of iodine per kilogram of body weight). The DECT imaging parameters were tube voltage: A-tube 90 kV/B-tube 150 kV (Sn filter added), tube rotation time: 0.5 s/rotation, acquisition detector: 0.6 mm × 192 rows, pitch factor: 0.6, and tube current: reference mAs at A-tube 370 mAs/B-tube 230 mAs. The reconstruction parameters were kernel: Qr36 (standard kernel for soft tissue), iterative reconstruction technique [Advanced Modeled Iterative Reconstruction (ADMIRE)] strength level: 5, slice thickness: 3 mm, and slice spacing: 3 mm.

Standard of reference

The imaging diagnosis of FHLs was confirmed by two radiologists (20 and 12 years of experience, respectively) who did not participate in the ED analyses by consensus. FHLs were definitively diagnosed based on pathological results or typical CT characteristics. HCCs were diagnosed based on the classic CT features of non-rim arterial-phase hyperenhancement and washout in the equilibrium phase (12). Liver metastases included those with a history of malignant tumors and nodules that appeared in the liver during follow-up. Hepatic hemangiomas and cysts were diagnosed based on classic CT characteristics (13,14).

ED analysis

All data from the unenhanced DECT images were sent to a workstation (Syngo.via version VB30A; Siemens Medical Solutions, Erlangen, Germany), and ED images were created using dual-energy software (Rho/Z; Siemens Medical Solutions). On the resulting ED image, a region of interest (ROI) was drawn as large as possible in the transverse section of the lesion, and the ED of the lesion was measured. In addition, three circular ROIs of 10 mm² were set in background liver parenchyma areas without FHLs, avoiding major blood vessels. The average of these three EDs was used as the ED of the background liver parenchyma (bED). Therefore, the relative ED (rED) was calculated using the following formula: rED = ED/bED × 100. An example of an ROI setting is shown in Figure 2. ED measurements were independently obtained by two radiological technologists (35 and 8 years of experience in CT imaging, respectively) blinded to the patients’ clinical data.

Figure 2 Representative case of HCC. Arterial phase, equilibrium phase, and ED images of HCC (arrows) and an example of the ROI on the ED image (A-D) are shown. One ROI was placed on the lesion, and the ED was measured. Three ROIs were placed on the background liver parenchyma, and the bED was measured. bED, electron density of background liver parenchyma; ED, electron density; HCC, hepatocellular carcinoma; rED, relative electron density; ROI, region of interest.

Statistical analysis

Sex and ALBI grade distributions between patient groups were compared using Fisher’s exact test. Age, body weight, aspartate aminotransferase (AST) and alanine aminotransferase (ALT) levels, ALBI score, and tumor diameter were compared using the Kruskal-Wallis test. The ED, rED, and bED were compared using a one-way analysis of variance (ANOVA). The Bonferroni post-hoc test was used for multiple comparisons among the groups. Spearman’s rank correlation coefficient was used to evaluate the association between bED and ALBI score across all 89 cases.

The inter-observer agreement between the two readers for the measured ED was evaluated using intra-class correlation coefficients (ICCs; two-way ANOVA) and 95% confidence intervals (CIs). The agreement based on ICCs was classified according to the following definitions: <0.50, poor; 0.50–0.74, moderate; 0.75–0.89, good; and 0.90–1.00, excellent (15).

The diagnostic performance of ED and rED for differentiating malignant tumors from benign lesions was evaluated using receiver operating characteristic (ROC) curve analysis, and the area under the receiver operating characteristic curve (AUC) and 95% CIs were calculated. DeLong’s test was used to compare the ROC curves of ED and rED. The optimized cutoff values were obtained using the maximized Youden index method based on ROC analysis. The sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) were calculated. In addition, we conducted ROC curve analysis excluding hepatic cysts, comparing only malignant tumors and hepatic hemangiomas. For all statistical analyses, the reported P values were two-sided, with P values <0.05 considered statistically significant. All statistical analyses were conducted using the SPSS statistical software (version 22.0; IBM, Chicago, IL, USA).

Results

Clinical features

Table 1 summarizes the clinical features of the four FHL groups. There were no significant differences in sex (P=0.82), body weight (P=0.69), or ALT levels (P=0.07) between the groups. Patients with HCCs and hepatic cysts were significantly older than those with liver metastases and hepatic hemangiomas (P<0.005). Patients with malignant tumors had significantly higher ALBI scores (P<0.005), ALBI grades (P<0.001), and AST levels (P<0.05) than those with benign lesions. The ρ values for the correlation between ALBI score and bED was −0.409 (P<0.001, Spearman’s rank correlation).

ED analysis

Table 2 and Figure 3 present the ED, bED, and rED of the four FHL groups. Malignant tumors tended to have higher ED and rED values and lower bED values than benign lesions. Good agreement in measured ED, bED, and rED was found between the two readers (ICC =0.86–0.97). Significant differences in ED were observed among all FHLs (P<0.05), except for liver metastases and hepatic hemangiomas (Figure 3A). In contrast, a significant difference in bED was observed only between liver metastases and hepatic hemangiomas (P<0.05) (Figure 3B). Significant differences in rED were observed among all FHLs (P<0.05), except for HCCs and liver metastases (Figure 3C). The ROC curves of ED and rED for distinguishing malignant tumors from benign lesions are shown in Figure 4; the AUC, optimal cutoff value, sensitivity, specificity, accuracy, PPV, and NPV are presented in Table 3. The AUC of the rED was significantly larger than that of the ED (P<0.05). The ROC curves of ED and rED for distinguishing malignant tumors from hemangiomas are shown in Figure 5; the AUC, optimal cutoff value, sensitivity, specificity, accuracy, PPV, and NPV are presented in Table 4. The AUC of rED was found to be significantly higher than that of ED (P<0.05), indicating superior diagnostic performance.

Figure 3 Box plots of ED, bED, and rED of the four FHLs groups. Significant differences in ED were observed among all FHLs (P<0.05), excluding the liver metastases and hepatic hemangiomas (A). In contrast, a significant difference in bED was observed only between liver metastases and hepatic hemangiomas (P<0.05) (B). Significant differences in rED were observed among all FHLs (P<0.05), excluding hepatocellular carcinomas and liver metastases (C). bED, electron density of background liver parenchyma; ED, electron density; FHL, focal hepatic lesion; HCC, hepatocellular carcinoma; NS, not significant; rED, relative electron density.

Figure 4 Receiver operating characteristic curves of ED and rED for distinguishing malignant tumors from benign lesions. The AUC of rED (0.88) was significantly higher than that of ED (0.80) (P<0.05). AUC, area under the receiver operating characteristic curve; ED, electron density; rED, relative electron density.

Figure 5 Receiver operating characteristic curves of ED and rED for distinguishing malignant tumors from hemangiomas. The AUC of rED (0.80) was significantly higher than that of ED (0.67) (P<0.05). AUC, area under the receiver operating characteristic curve; ED, electron density; rED, relative electron density.

Discussion

To our knowledge, this is the first report to demonstrate the usefulness of ED derived from DECT in distinguishing FHLs. HCCs and liver metastases tended to have higher ED and rED and lower bED than hepatic hemangiomas and cysts. Group comparisons and ROC analysis indicated that the rED, calculated using the simple formula rED = ED/bED × 100, can differentiate malignant liver tumors from benign lesions. In addition, the high ICCs of ED, bED, and rED indicated that the measurements obtained by different measurers were consistent and reliable.

DECT can calculate ED in Hounsfield units (HUs) using two X-ray energies to produce an ED image. ED is the probability of the presence of electrons at a specific location and depends on the type of molecule and the molecular structure (16). The ED calculated using DECT has an average error of 1.3% from the true ED (17), and DECT can determine the ED with high accuracy. Although what the ED reflects in lesions is unclear, several results have been reported for systemic lesions. In higher-grade cerebral gliomas, cell density increases, and intercellular spaces become narrower, limiting the diffusion of water molecules and reducing the apparent diffusion coefficients (ADCs). Therefore, diffusion-weighted imaging is useful for assessing tumor cell density and grade (18-20). ED is also reportedly higher in high-grade cerebral gliomas with high cell density (9). These results suggest that ED reflects the cell density in brain tumors. Additionally, ED has been reported to reflect cell density in the bone marrow (10). Although several studies have shown that malignant liver tumors have lower ADCs than benign lesions, reflecting a higher cell density (21-26), no study has evaluated FHLs using ED images. In addition, recent reports have suggested that tissue T2 signal—commonly referred to as the “T2 shine-through” effect—also plays a major role in determining ADCs (27). Similarly, the biological background of ED in hepatic lesions may reflect not only cellular density but also other factors such as water content, molecular structure, and tissue architecture. Therefore, further investigation is warranted.

The results of the ROC curve analysis in this study revealed that rED had a significantly larger AUC and higher diagnostic performance than ED for discriminating between benign and malignant FHLs. Although there was no significant difference in ED between liver metastases and hemangiomas, there was a significant difference in the rED. Furthermore, even when hepatic cysts—which exhibit markedly different ED values from malignant tumors—were excluded and the analysis focused solely on malignant tumors and hepatic hemangiomas, ROC curve analysis demonstrated that rED showed a significantly greater AUC. This may be because patients with liver metastases have significantly lower bED than those with hepatic hemangiomas. Comparisons of the ALBI score/grade and AST level revealed that patients with malignant liver tumors had significantly lower liver function than those with benign lesions. Liver HUs decrease as the hepatic function declines (28). As ED is calculated using HUs (29,30), it also decreases as hepatic function declines. In the present study, patients with malignant liver tumors tended to have a lower bED than those with benign lesions. A moderate negative correlation between the ALBI score and bED further supports the association between bED and liver function. This finding suggests that normalizing lesion ED by the background liver parenchyma may enable more accurate differentiation between benign and malignant FHLs. In this study, no significant difference in ED was observed between hepatic metastases and hepatic hemangiomas. Therefore, the utility of ED for differentiating between these two lesions appears to be limited. However, rED showed a significant difference between hepatic hemangiomas and malignant tumors, and its AUC was greater than that of ED. These findings suggest that rED, which accounts for the influence of the background liver parenchyma, may contribute to improved diagnostic accuracy. The present study’s findings and those of previous studies suggest that elevated ED in FHLs and liver parenchyma may reflect higher cellular density. This indicates the potential for ED to have broader utility in hepatic imaging and diagnostics.

ED can be calculated using non-contrast CT, which is less restrictive and may help in the differential diagnosis of FHLs in patients who cannot undergo contrast-enhanced CT or MRI. Contrast-enhanced CT has high sensitivity and specificity for detecting HCC (31). However, contrast-enhanced CT is sometimes inadequate because of iodine allergy or renal dysfunction. MRI provides various image contrasts and high soft-tissue contrast; liver-specific contrast agents can provide information on blood flow and function (32). Moreover, since non-contrast MRI enables high-precision differentiation between malignant and benign tumors, it serves as an effective diagnostic tool for FHLs in cases where the use of contrast agents is contraindicated. However, MRI has the disadvantage of a long examination time, and patients with implanted pacemakers or claustrophobia cannot be examined. In contrast, non-contrast CT involves a short examination time and few restrictions, making it simple and accessible for many patients. Therefore, it may represent a viable alternative in situations where MRI is contraindicated or not feasible to perform. Furthermore, given the practical advantages of CT, it may serve a complementary role to MRI examinations. Based on the results of this study, the diagnostic performance of ED alone for discriminating between benign and malignant FHLs is not perfect, but the use of rED, which reflects background liver function, may be beneficial for non-invasive imaging diagnosis.

Our study has some limitations. First, this was a single-center study, and the number of patients was small. Second, although EDs were compared among HCCs, liver metastases, hepatic hemangiomas, and hepatic cysts, this study did not evaluate EDs of other FHLs, such as focal nodular hyperplasia. Further studies are required to evaluate the utility of ED in differentiating malignant tumors from FNH and hepatic adenoma. In addition, the inclusion of hepatic cysts—which are typically easy to differentiate even on non-contrast CT—may have led to an overall overestimation of the diagnostic performance in this study. Third, the liver metastases evaluated in this study comprised different histological types and primary sites with varying CT contrast patterns. Fourth, the study included patients with typical CT findings. Although HCCs vary in their degree of differentiation and composition, only typical hypervascular HCCs were examined in this study. HCCs may display atypical imaging types if the tumor is well- or poorly differentiated (33), has fatty acidosis (34), or has abundant interstitial fibrosis (33). The findings related to HCC in this study are limited to cases with typical imaging features and should not be generalized to all HCC subtypes. Further studies are required to provide further insights into the benefits of rED with respect to other species of FHLs. Finally, hepatic steatosis is a common condition known to significantly reduce the HUs of the liver parenchyma. However, the degree of steatosis was not systematically assessed in this study. This may have influenced the values of bED and rED, suggesting the need for future dedicated investigations to clarify its impact.

Conclusions

In conclusion, malignant liver tumors tended to have a higher ED than benign lesions, indicating that ED may reflect cell density in FHLs. ED helps in the differential diagnosis of FHLs, and the use of rED normalized by the bED further improves the diagnostic performance. This study focused on a limited subset of FHLs, aiming to evaluate the preliminary utility of rED. Future investigations involving a broader spectrum of hepatic lesions are warranted to further validate and expand upon these findings.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STARD reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2024-2592/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-2024-2592/dss

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2024-2592/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. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the research ethics committee of University of Fukui (No. 20190164) and individual consent for this retrospective analysis 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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