Pancreatic extracellular volume fraction based on dual-energy computed tomography iodine maps: association with diabetes

Introduction

Diabetes is a metabolic disease caused by multiple etiologies and characterized by chronic hyperglycemia. Epidemiological surveys predict that its worldwide prevalence will increase from 10.5% in 2021 to 12.2% in 2045 (1). Increased risk of developing cardiovascular disease, metabolic disease, neuropathy, nephropathy, peripheral vascular disease, and other diseases, is closely related to the continuous rise or high level of blood glucose in diabetes (2). However, it is noteworthy that patients with diabetes pay more attention to blood glucose levels or tissues associated with complications and they are not equipped with other potential imaging/clinical biomarkers to understand the nuances of what happens to the pancreas on a pathophysiological level. The difficulty in controlling blood glucose levels in some diabetic patients may be attributed to histological changes in the pancreas, such as pancreatic fibrosis.

Pancreatic biopsy is an invasive examination that is currently the gold standard to evaluate pancreatic fibrosis. Fukui et al. (3) found that computed tomography (CT)-derived extracellular volume fraction (fECV) exhibited a strong correlation with the histological fibrosis fraction (r=0.64), demonstrating its potential for non-invasive assessment of pancreatic fibrosis. Further study by Sun et al. (4) indicated a strong correlation between magnetic resonance imaging (MRI)- and CT-derived fECV (r=0.948). However, MRI examination is not appropriate for patients with claustrophobia or post-pacemaker implantation, and images are easily affected by respiratory movement and the cardiovascular pulsation. Single-energy CT (SECT) is constrained by its reliance on a single energy spectrum, which can cause different materials to exhibit similar CT attenuation values, limiting its ability to differentiate tissues. Dual-energy CT (DECT) acquires two datasets simultaneously using high- and low-energy spectra, leveraging the distinct attenuation properties of materials at different energy levels to achieve more precise tissue characterization. A key advantage of DECT is its ability to generate pixel-level iodine concentration maps, where non-iodine components are replaced with black pixels, enabling direct quantification of iodine-based contrast agent concentrations. It is an essential parameter for calculating fECV. Additionally, the material decomposition capability of DECT allows for both qualitative and quantitative assessment of tissue composition and contrast agent distribution. With its superior performance to SECT, DECT enhances imaging accuracy and provides more reliable diagnostic information for clinical applications (5,6). The extracellular volume represents the sum of the extravascular-extracellular and intravascular spaces. Iodinated contrast material can freely traverse surrounding spaces and fECV can be calculated by absolute contrast enhancement between pre-contrast and equilibrium phases or iodine density in equilibrium phase, after iodine contrast agent administration (6,7). CT-derived fECV can be obtained by two methods: absolute contrast enhancement method (CT-∆HU) and dual-energy iodine density method (CT-ID) (8); the SECT absolute enhancement method requires both non-contrast and equilibrium-phase images to calculate HU differences. The DECT iodine density method derives iodine density values solely from iodine maps acquired during the equilibrium phase. The latter method not only minimizes the risk of misregistration between pre- and post-contrast kVp and spatial images but also reduces radiation exposure (9). Furthermore, it enhances diagnostic performance in assessing fibrosis (8,10).

Pancreatic fibrosis is closely associated with glucose metabolism and is typically defined as the excessive accumulation of extracellular matrix (ECM) proteins in the extravascular-extracellular space. Diabetes islet β cells consume more oxygen while producing insulin. Various factors, such as hyperglycemia, hypoxia, and oxidative stress, can induce the activation of pancreatic stellate cells (PSCs), leading to ECM hyperplasia and promoting pancreatic fibrosis. Fukui et al. (CT-∆HU) (11) and Noda et al. (MRI) (12) demonstrated that pancreatic fECV was significantly higher in patients with diabetes than in those without diabetes or with pre-diabetes. However, there is a lack of research on its potential for noninvasive evaluation of the relationship between glycemic control and pancreatic histology in diabetic patients. Furthermore, a study by Gao et al. (13) indicated that myocardial fECV is higher in diabetic patients with poor blood glucose control than in those with well-controlled diabetes. Given that the pancreas is a primary organ affected by diabetes, it remains to be investigated whether pancreatic histopathological changes are also associated with glycemic control levels.

This retrospective study aimed to validate whether the pancreatic fECV derived from DECT iodine maps (CT-ID) in equilibrium phase was associated with hemoglobin A1c (HbA1c) and to further analyze the relationship between pancreatic fECV and diabetes progression. We present this article in accordance with the STROBE reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-24-2419/rc).

Methods

Patient data

This was a retrospective study that enrolled a total of 292 patients who underwent triple-phase contrast-enhanced DECT scans at Qingdao Municipal Hospital from October 2021 to June 2022. The inclusion criteria were as follows: adult patients; patients accepted equilibrium phase contrast-enhanced DECT scan with the gemstone spectral imaging (GSI) mode of the upper and middle abdomen; and HbA1c and hematocrit (Hct) data were obtained. The exclusion criteria were as follows: patients with liver cirrhosis; patients with severe pancreatic atrophy, diffuse pancreatic calcification, pancreatic fatty infiltration, acute pancreatitis (14,15), and other pancreatic lesions; post-pancreaticoduodenectomy patients; patients with a recent history of blood transfusion and bleeding or moderate to severe anemia (16-19); HbA1c values were not obtained within 30 days before and after CT examination; and the interval between abdominal CT and blood routine Hct measurements was more than 2 weeks. In the end, 213 patients met the criteria. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Institutional Review Board of Qingdao Municipal Hospital (No. 2023-006) and the requirement for individual consent for this retrospective analysis was waived.

Clinical data including age, sex, body mass index (BMI), Hct, and HbA1c were recorded from the hospital electronic medical records system. The Diabetes Control and Complications Trialand UK Prospective Diabetes Study studies have shown that maintaining HbA1c below 7% significantly reduces the risk of diabetic microvascular complications, such as retinopathy, nephropathy, and neuropathy (20). Additionally, long-term control of HbA1c below 7% also reduces the incidence of macrovascular complications, such as cardiovascular disease and stroke (20). A stricter HbA1c target (e.g., 6.5%) is suitable for younger patients or those with a shorter duration of diabetes, as it may further reduce the risk of complications. However, it may also increase the risk of hypoglycemia and treatment burden. A more relaxed HbA1c target (e.g., 8%) is more appropriate for patients at high risk of hypoglycemia, elderly patients, or those with a shorter life expectancy, but it may increase the risk of complications. According to the 2024 criteria of the American Diabetes Association (ADA) (20,21), patients were divided into two groups: a non-diabetes (Group ND) and a diabetes (Group D). Then, Group ND was separated into a normal blood glucose group (Group ND_N, HbA1c <5.7%) and a pre-diabetes group (Group ND_PD, 5.7%≤ HbA1c <6.5%, without hypoglycemic drug treatment). Similarly, according to the control of blood glucose after treatment, Group D was partitioned into a good blood glucose control (Group D_C, HbA1c <7.0%) and a poor blood glucose control (Group D_NC, HbA1c ≥7.0%). The patient flow diagram of this study is presented in Figure 1.

Figure 1 Patient flow diagram. Group D: diabetes. Group D_C: good blood glucose control. Group D_NC: poor blood glucose control. Group ND: non-diabetes. Group ND_N: normal blood glucose. Group ND_PD: pre-diabetes. CT, computed tomography; DECT, dual-energy CT; GSI, gemstone spectral imaging; HbA1c, hemoglobin A1c; Hct, hematocrit.

Data acquisition

Triple-phase contrast-enhanced CT of the abdomen in GSI mode was performed. The following GSI parameters were applied: tube voltage 80/140 kV, tube current 405 mA, pitch 0.992:1, rotational time 0.50 s, and reconstruction slice thickness 1.25 mm. Scanning range was from 1 cm above the diaphragm to the lower edge of liver, pancreas, and spleen. For contrast-enhanced scanning, iodine contrast agent (ioversol; Jiangsu Hengrui Pharmaceuticals Co., Ltd., Lianyungang, China) (320 mgI/mL) was injected through the elbow vein using a high-pressure syringe at a dose of 1.2 mL/kg body weight, the total iodine dose was 384 mgI/kg body weight, and a flow rate of 2.0 mL/s. The delay time for arterial phase, portal venous phase, and equilibrium phase were set at 30, 60, and 180 s after the unenhanced scan.

Data measurement

The spectral data in equilibrium phase were transferred to AW 4.7 workstation (GE Healthcare, Chicago, IL, USA) and iodine maps were obtained using GSI Viewer software. Regions of interest (ROIs; ranging 50–55 mm²) (6,12) were delineated manually in the head, body, and tail of the pancreas by two radiologists (with 2 and 3 years of clinical experience, respectively) in a blind way to measure the iodine densities, and the average iodine density was used for calculating the fECV. Afterwards, all ROIs were visually confirmed (by radiologists with over 15 years of clinical experience) and manually adjusted as needed to ensure accuracy. The ROIs were placed through the center of the pancreatic parenchyma to avoid the influence of the main pancreatic duct, peripancreatic fat, and blood vessels. At the same level of pancreatic ROI, one identical ROI was delineated on the abdominal aorta, as shown in Figure 2A-2C. The following Eq. [1] was used for calculating pancreatic fECV:

fECV%=IDpancreas-equilibriumphaseIDabdominalaorta-equilibriumphase×(100−Hct)[%][1]

Figure 2 The measurement of pancreatic fECV in a 61-year-old man with diabetes. In iodine images of equilibrium phase, (A) the ROIs are placed on the head of the pancreas (red circle 1) and the abdominal aorta (red circle 2) at the same level; (B) the ROIs are placed on the body of the pancreas (red circle 3) and the abdominal aorta (red circle 4) at the same level; (C) the ROIs are placed on the tail of the pancreas (red circle 5) and the abdominal aorta (red circle 6) at the same level. fECV, extracellular volume fraction; ROIs, regions of interest.

The fECV reflects the extracellular volume, which represents the sum of the extravascular-extracellular and intravascular spaces. During the equilibrium phase, the distribution of iodine contrast agent reached a balance between the extravascular-extracellular and intravascular spaces, where ID_{pancreas-equilibrium phase} and ID_{abdominal aorta-equilibrium phase} were mean iodine density of the pancreas and abdominal aorta, respectively, in equilibrium phase iodine maps. ID_{pancreas-equilibrium phase}/ID_{abdominal aorta-equilibrium phase} represents the proportion of interstitial components. Hct refers to the red blood cell (RBC) volume, and 100 − Hct represents the amount of iodine contrast agent in the interstitium at equilibrium (6).

Statistical analysis

Excel (version 2010, Microsoft, Redmond, WA, USA) spreadsheet was utilized to record clinical and imaging data. All statistical analyses were performed using SPSS software (version 25.0, IBM Corp., Armonk, NY, USA) and GraphPad Prism (version 9, GraphPad Software Inc., San Diego, CA, USA). Continuous variables were illustrated by mean ± standard deviation (x¯±SD). Shapiro-Wilk test was performed to test for normal distribution. Normally distributed data were compared between groups using independent sample t-test or one-way analysis of variance (ANOVA), whereas non-normally distributed data were compared between groups using Mann-Whitney U test or Kruskal-Wallis test. Categorical variables were explained as numbers and percentages and comparisons were conducted by chi-square test (χ²). The correlation between pancreatic fECV and HbA1c, which follows a non-normal distribution, was evaluated by Spearman rank correlation analysis, with multiple corrections by the Bonferroni method. Univariate and multivariate analyses were performed using a logistic regression model to evaluate the associations of fECV, age, sex, Hct, and BMI with diabetes, aiming to determine whether fECV serves as an independent predictor of diabetes progression. The diagnostic value of pancreatic fECV between different groups was evaluated by receiver operating characteristic (ROC) curve analysis. Box diagram, scatter diagram, and ROC curve were used to visualize the differences and correlation between groups. A P value <0.05 indicated statistical significance.

Results

Finally, a total of 213 patients with an average age of 62.77±8.55 years were enrolled. Group ND included 120 patients (Group ND_N, n=56; Group ND_PD, n=64) and Group D contained 93 patients (Group D_C, n=37; Group D_NC, n=56). Age, sex, Hct, and BMI between Group D and Group ND did not show significant differences (P>0.05), as presented in Table 1. Similarly, the four indexes between Group ND_N, Group ND_PD, Group D_C, and Group D_NC (all P>0.05) did not indicate significant differences, as presented in Table 2.

The fECV in Group D was significantly higher than that in Group ND (P<0.001), as presented in Table 1 and Figure 3A. Comparisons among all subgroups were performed by ANOVA. Group D_NC generated the highest fECV followed by Group D_C, Group ND_PD, and Group ND_N. There were significant differences in pairwise comparisons, except for Group ND_N and Group ND_PD (all P<0.05), as presented in Table 2 and Figure 3B.

Figure 3 Box plots of pancreatic fECV in different groups. (A) Pancreatic fECV in Group D was significantly higher than that in Group ND (P<0.001). (B) The pancreatic fECV in Group D_NC was significantly higher those in Group D_C, Group ND_N and Group ND_PD (all P<0.05). *, P<0.05; ***, P<0.001. Group D: diabetes. Group D_C: good blood glucose control. Group D_NC: poor blood glucose control. Group ND: non-diabetes. Group ND_N: normal blood glucose. Group ND_PD: pre-diabetes. fECV, extracellular volume fraction.

For all patients, HbA1c was positively correlated with fECV (r_s=0.457, P<0.001). Additionally, further analysis revealed that there was a significant positive correlation between HbA1c and fECV in Group D (r_s=0.449, P<0.001). However, in the other subgroups (Group ND, Group ND_N, Group ND_PD, Group D_C, and Group D_NC), there was no significant correlation between HbA1c and fECV, as presented in Table 3 and Figure 4.

Figure 4 Scatter diagram of the correlation between HbA1c and fECV (r_s=0.457, P<0.001). fECV, extracellular volume fraction; HbA1c, hemoglobin A1c.

Univariate regression analysis revealed a significant association between pancreatic fECV and diabetes [odds ratio (OR) =1.413; 95% confidence interval (CI): 1.246–1.603; P<0.001]. After adjusting for potential confounders, including age, sex, Hct, and BMI, multivariate regression analysis confirmed that pancreatic fECV was an independent predictor of diabetes (OR =1.449; 95% CI: 1.265–1.659; P<0.001).

Setting the cutoff value as 34.00%, the area under the curve (AUC) of fECV to distinguish Group ND and Group D was 0.732 (95% CI: 0.664–0.799, sensitivity 60.20%, specificity 79.20%, P<0.001). Among all subgroups, corresponding AUCs to differentiate Group ND_N and Group D_NC, Group ND_PD and Group D_NC were higher, achieving 0.831 (95% CI: 0.754–0.907, sensitivity 75.00%, specificity 83.90%, P<0.001) and 0.782 (95% CI: 0.700–0.864, sensitivity 71.40%, specificity 79.70%, P<0.001), respectively; the AUCs to determine Group ND_N and Group D_C, Group D_C and Group D_NC were up to 0.651 (95% CI: 0.538–0.764, sensitivity 67.60%, specificity 60.70%, P=0.014) and 0.687 (95% CI: 0.571–0.803, sensitivity 71.40%, specificity 70.30%, P=0.002), respectively; the AUC for Group ND_PD and Group D_C was moderate, at 0.594 (95% CI: 0.479–0.709, sensitivity 62.20%, specificity 54.70%, P=0.116), as presented in Table 4 and Figure 5.

Figure 5 Diagnostic performance of fECV for identifying different groups by ROC curves. Group D: diabetes. Group D_C: good blood glucose control. Group D_NC: poor blood glucose control. Group ND: non-diabetes. Group ND_N: normal blood glucose. Group ND_PD: pre-diabetes. AUC, area under the curve; fECV, extracellular volume fraction; ROC, receiver operating characteristic.

Discussion

This study demonstrated a significant positive correlation between fECV and HbA1c. In addition, fECV in Group D was significantly higher than that in Group ND and unrelated to good glycemic control; the AUC of fECV was 0.732 between Group ND and Group D. Moreover, fECV was notably different between Group D_C and Group D_NC; the AUC of fECV was 0.687 between Group D_C and Group D_NC. Pancreatic fECV was an independent predictor of diabetes progression.

Diabetes islet β cells consume more oxygen while producing insulin. Hyperglycemia, hypoxia, or oxidative stress induced activation of PSCs through the renin-angiotensin-aldosterone system (RAAS). As a result, ECM hyperplasia, islet cell fibrosis, and islet cell hypoxia damage islet β cells, insulin secretion diminisheed, and blood glucose further increased (22). Therefore, we predicted that the growth of pancreatic fECV in diabetic patients might be related to the expansion of extracellular space caused by ECM hyperplasia after PSCs activation. The irreversible process denoted that hypoglycemic treatment could delay the progression of fibrosis, but could not reverse the fibrosis. Accordingly, it also indirectly showed that pancreatic fECV was associated with hyperglycemic metabolism. Wong et al. (23) and Laohabut et al. (24) demonstrated similar results in corresponding cardiac research. As illustrated above, fECV could reveal the degree of pancreatic fibrosis (7). The higher fECV level represented a more serious degree of pancreatic fibrosis, more severe damage of islet β cells, less insulin, which was the only hypoglycemic hormone in the body under physiological conditions, impaired blood glucose regulation mechanism, and increased blood glucose level. HbA1c is the product of the combination of hemoglobin in RBC and sugars in serum through glycosylation reaction. The amount of HbA1c depended on the blood glucose concentration and the contact time between blood glucose and hemoglobin. A higher blood glucose level resulted in a higher HbA1c. In general, fECV and HbA1c present a positive correlation. The results of Fukui et al. (pancreatic CT-∆HU-fECV) (11) and Noda et al. (pancreatic MRI-fECV) (12) were consistent with those of this study. These findings demonstrated that fECV could estimate diabetes progression by reflecting pathophysiological changes in the pancreas.

However, Kameda et al. (6) demonstrated that there was no significant correlation between fECV and HbA1c in the pancreas. The main reason for the converse results of this study was the different patient inclusion criteria. Firstly, Kameda et al. (6) excluded patients receiving hypoglycemic treatment (insulin or other drugs), whereas this study included these patients. PSCs generated fibrosis in the advanced stages of diabetes (25). In the early stage of diabetes, the pancreas could still produce insulin in the case of insulin resistance, whereas in advanced diabetes, the production and secretion of insulin might be gradually impaired with the progression of pancreatic fibrosis. In the early stage, treatment might only involve dietary and movement therapy. However, in the advanced stage, insulin or other medications are required to decline blood glucose levels. Therefore, there were no advanced diabetes with potential pancreatic fibrosis in Kameda et al. (6). Similarly, there was also no significant correlation between fECV of Group ND and HbA1c in this study, because there were no patients with underlying pancreatic fibrosis in this group. Secondly, all experiments in Kameda et al. (6) were related to liver cirrhosis. HbA1c erroneously decreased for situation of RBC renewal rate increased or the half-life shortened (26,27). However, most patients with liver cirrhosis have clinical manifestations such as hemorrhage, anemia, and hypersplenism. Recent hemorrhage might influence the Hct values in patients with liver cirrhosis and disturb the accuracy of fECV measurement. Furthermore, the etiology of included patients with liver cirrhosis was mainly viral hepatitis (48.5%) and alcoholic hepatitis (33.3%) (6). Wang (28) illustrated that alcoholic liver disease and some antiviral drugs, such as ribavirin, could also affect HbA1c level (reduce it). Moreover, the AUC of fECV for differentiating Group ND and Group D was 0.732 in this study, whereas the AUC of Noda et al. (12) was 0.990. Potential causes are as follows: firstly, Noda et al. (12) included patients with diabetes due to pancreatic ductal adenocarcinoma and chronic pancreatitis. The evolution and development of the disease was accompanied with changes in the extracellular microenvironment. The fibrous matrix of the tumor was significantly responsive; the rate and potency of pancreatic pathological changes and the pathogenesis of glucose intolerance in patients with tumor or inflammation might be different from that in patients with diabetes without tumor or inflammation (29). Secondly, the sample size of this study was larger than that of Noda et al. (12), which might contain more residual confounding factors that have not been analyzed.

The limitations of this study are as follows: firstly, this was a retrospective study using a relatively small sample size in a single-center, which may have caused selection bias. Secondly, manual delineation of ROIs was subjective and unavoidable measurement errors existed. Thirdly, the ability of fECV to monitor and predict diabetes was still unknown since there was no comparative analysis on the same patients before and after treatment. Fourthly, clinical indicators including family history, high-sugar, high-fat diet, alcohol consumption, smoking, and other living habits, were not considered for identification. Finally, there was no analysis whether patients had used angiotensin-converting enzyme inhibitors which prevented or delayed the progression of pancreatic fibrosis by affecting RAAS.

Conclusions

Pancreatic fECV derived from DECT iodine maps at equilibrium phase is associated with HbA1c. As an innovative imaging biomarker, pancreatic fECV could noninvasively reflect the pathophysiological changes of pancreas in diabetic patients and provided a novel index for blood glucose control and disease progression in clinical diabetic patients.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-24-2419/rc

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-24-2419/coif). L.L. is a former employee of GE Healthcare China Co., Ltd. The other 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 Institutional Review Board of Qingdao Municipal Hospital (No. 2023-006) and the requirement for 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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