Evaluation of the stability of deep vein thrombosis based on computed tomography energy spectrum

Introduction

Venous thromboembolism (VTE) mainly manifests clinically as deep vein thrombosis (DVT) or pulmonary embolism (PE). In epidemiological studies, annual incidence of PE is about 39–115 per 100,000 population, and the annual incidence of DVT is about 53–162 per 100,000 population (1-3). Acute pulmonary embolism (APE) represents a significant complication of DVT, is characterized by high mortality and disability rates, and is second only to myocardial infarction and stroke (4). An estimated 80–90% of APE cases are attributable to DVT (3). Consequently, the investigation of DVT stability has emerged as a focal point and a challenging area of research domestically and internationally.

Recently, numerous scholars have conducted imaging-based studies on the stability of DVT. Moreover, vascular intervention is the gold standard for DVT diagnosis. However, its clinical application is limited by its invasive nature, potential for contrast allergies, and risk of nephrotoxicity (5). Currently, compression ultrasound is the gold standard for DVT diagnosis, although conventional ultrasound cannot assess thrombus stability (6). Ultrasound elastography offers the capability to determine thrombus age and assess the risk of dislodgement (7). Nevertheless, the procedure poses a risk of artificially dislodging unstable thrombi. Furthermore, ultrasound investigations into DVT stability have predominantly focused on a specific type of floating thrombus (8). Consequently, there is a pressing need for a safer and more precise method to evaluate thrombus stability. Computed tomography venography (CTV) of the lower extremities is becoming an important method for DVT in recent years. The thrombus density ratio of DVT detected by CTV could predict the occurrence of APE (9). Nonetheless, DVT recognition is often challenging owing to the minimal density difference between the thrombus and DVT lumen in conventional computed tomography (CT) images.

Thrombus is mainly composed of red blood cells (RBCs), fibrin and platelets. The ratio of different components will affect the physical properties of thrombus. Spectral CT can obtain a variety of parameters to reflect these properties, so as to realize the analysis of thrombus composition. CT energy spectroscopy, a novel functional imaging technique, has recently demonstrated enhanced sensitivity in detecting peripheral DVT compared to conventional CT, and it substantially improves image contrast, allowing for more detailed visualization of thrombi (10). Beyond conventional density features, this technique enables the simultaneous acquisition of multiple quantitative parameters of thrombi, including energy spectrum curves, effective atomic numbers, iodine maps, and single-energy imaging, all within a single scan (11). These tools enable deeper information mining from raw spectral CT data, providing multi-modal quantitative parameters that quantitatively reflect tissue compositional differences and blood supply characteristics, thus achieving a qualitative leap in disease diagnosis.

In this study, we compared the differences of thrombus in energy spectrum CT parameters between DVT patients with and without APE to quantify thrombus heterogeneity energy spectrum. The primary objective was to determine whether energy spectrum CT parameters can predict DVT stability. The secondary objective was to evaluate the predictive value of individual/combined parameters of energy spectrum CT for APE risk. This study offers novel insights and methodologies for the diagnosis and management of thrombophilia. We present this article in accordance with the STROBE reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-20/rc).

Methods

Study population

This study was a retrospective analysis of patients with APE who underwent energy spectrum CT from October 2023 to November 2024 in the First Affiliated Hospital of Hebei North University. All patients underwent lower limb CTV and CT pulmonary angiography on energy spectrum CT. 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 the First Affiliated Hospital of Hebei North University (No. k2024234) and individual consent for this retrospective analysis was waived.

Inclusion criteria include: (I) diagnosis of unilateral common iliac vein, external iliac vein, or common femoral vein thrombosis; (II) age >18 years.

Exclusion criteria include: (I) previous history of DVT or APE; (II) iliac vein compression syndrome; (III) patients with bilateral DVT; (IV) patients treated with thrombolysis, surgical thrombolysis, and inferior vena cava filter placement prior to energy spectrum CT examination; (V) patients with images that did not meet the requirements for diagnosis and measurement (include patients with image artifacts and thrombi too small to be delineated); (VI) patients with malignant tumors.

Following the application of inclusion and exclusion criteria, patients were categorized into DVT with APE group and DVT without APE group based on the presence or absence of APE as indicated by computed tomography pulmonary angiography (CTPA).

Energy spectrum CT examination

Examination method

All patients were scanned by GE Revolution 256-row CT (GE Healthcare, Chicago, USA) with the application of gemstone spectral imaging (GSI) scanning sequence. The scanning parameters of CTPA: 100 kVp; tube current regulated using an automatic exposure control system. To initiate the image acquisition process, a threshold of 180 Hounsfield unit (HU) was established with the region of interest (ROI) specifically located within the trunk of the pulmonary artery. The scanning parameters of CTV: spectral parameters included GSI mode and automatic mA selection; tube voltage: 80–140 kV switched instantaneously; the detector width of 80 mm, pitch of 0.992:1, rotational speed of 0.5 s per rotation, and a field of view (FOV) of
50 cm × 50 cm. Scanning slice spacing and thickness were set at 5 mm, whereas the reconstructed image layer spacing and thickness were both 1.25 mm. Soft tissue kernel algorithm, and adaptive statistical iterative reconstruction (ASiR-V) algorithm at 50%. Contrast injection: 80 mL of iodixanol (320 mgI/mL) was injected at a rate of 4.0 mL/s via a forearm vein, followed by 40 mL of saline at the same rate for CTPA. Immediately afterwards, 40 mL of iodixanol at a rate of 1 mL/s for CTV, followed by 40 mL of saline at a flow rate of 1 mL/s. CTV data acquisition was then performed after a delay of 210 seconds. Volume CT dose indexes (CTDIvol) and dose-length products (DLPs) were retrieved from the dose exposure reports integrated into the DICOM structured report.

Image analysis

The original data were reorganized into an image set with a slice thickness of 1.25 mm using a single-energy recombination algorithm and subsequently uploaded to the GE post-processing workstation ADW4.7 for thrombus analysis. Post-processing operations on the energy spectrum data were conducted using the GSI viewer software, which involved adjusting the appropriate FOV and delineating the ROI at the maximum level of thrombosis. The energy spectrum data were exported by navigating through the following sequence: Administration → Command Window → Prefs → GSI Viewer → roiSave. This process enabled the extraction of CT values at 10 keV intervals, effective atomic numbers, and the concentrations of each pair of base substances.

Previous studies have indicated minimal variation in CT values at higher keV levels (12,13). Therefore, this study focused on the CT value at the 40 keV energy level. An energy spectrum curve was plotted based on attenuation values across different energy levels, from which the slope of the energy spectrum curve (λ) was derived. The calculation formula of λ is as follows: λ = (CT value 40 keV − CT value 140 keV)/(140−40). The effective atomic number (Eff-Z), concentrations of calcium-water [Ca(W)], water-calcium [W(Ca)], iodine-water [I(W)], water-iodine [W(I)], calcium-iodine [Ca(I)], and iodine-calcium [I(Ca)] were measured (Figure 1).

Figure 1 Energy spectrum image analysis and processing. (A) 40 keV single-energy image with ROI (red) placed at the center of the thrombus. (B) Energy spectral curve of the ROI region. (C) Effective atomic number of the ROI region. (D) Scatter plot of the distribution of the substance concentration in the ROI region on an iodine-water basis plot. CT, computed tomography; GSI, gemstone spectral imaging; HU, Hounsfield unit; ROI, region of interest.

These quantitative data were averaged over three consecutive measurements, ensuring that normal lumen and wall tissue were avoided during the outlining process. The measurements were independently evaluated by two radiologists with 5 and 7 years of experience in vascular radiology, respectively. The reviewers were blinded to the APE findings, and any discrepancies were resolved through consensus following consultation with a third radiologist possessing a decade of experience in vascular radiology.

Statistical methods

Statistical analyses were conducted using SPSS version 23.0 (SPSS Inc., Chicago, IL, USA). The Shapiro-Wilk test method was employed to conduct the normality test. Quantitative data adhering to a normal distribution were presented as mean ± standard deviation (x ± s). If the data do not follow a normal distribution, they are represented by interquartile range (IQR). Comparisons of normally distributed data were performed using the two-independent sample t-test, whereas data not following a normal distribution were analyzed using the Mann-Whitney test. Categorical data were expressed as proportions (%), and analyzed using the chi-square test for group differences. The receiver operating characteristic (ROC) curves were generated using MedCalc software to evaluate the accuracy of the diagnostic models. The ROC curve can demonstrate the model’s performance in predicting APE risk, and its area under the curve (AUC) can quantify the model’s ability to differentiate; the larger the AUC value, the more accurate the model’s prediction of APE risk, and the more effective it can be in identifying high-risk patients. The Delong test is used to compare different ROC curves, determining whether the differences between models or indicators are statistically significant.

Logistic regression analysis was used to analyze the relationship between multiple parameters (independent variables) of energy spectrum CT and APE risk (dependent variable). The R software was used to construct univariate and multivariate logistic regression models. In the multivariate regression model, variable selection was performed using stepwise regression, which involves progressively adding or excluding variables based on the P values to establish the optimal regression model.

Additionally, the model’s calibration curve was plotted to adjust or calibrate it, and the Hosmer-Lemeshow (H-L) test was used to determine whether the difference between the model’s predicted probability and the observed results was statistically significant (P>0.05 indicates that the model is well calibrated). DCA helps to assess the value of the model’s clinical application and the potential clinical benefits. The DCA takes into account the effect of the model’s predicted results on clinical decision-making and the resulting potential benefits and risks. This helps clinicians determine whether using the model can lead to greater clinical benefits at a specific threshold probability. The R packages used included rms, rmda, replot, riskRegression, and Hmisc. P values <0.05 were considered statistically significant. The integration of these three components ensures the optimal functionality of their distinct modules, thereby enhancing the reliability of the results and ensuring the accuracy of the analysis at all stages.

Outcome measures

Primary outcome: differences in spectral CT parameters [40 keV CT value, λ, Eff-Z, Ca(W), W(Ca), I(W), W(I), Ca(I), and I(Ca)] between DVT with/without APE groups.

Secondary outcomes: predictive performance of individual parameters and the combined model (AUC, calibration, net benefit).

Results

General clinical information

A total of 84 patients were diagnosed with DVT. Exclusions: three cases with a history of DVT or APE; two cases of iliac vein compression syndrome; eight cases of bilateral DVT; five patients who underwent thrombolytic therapy, surgical thrombectomy, and inferior vena cava filter placement; one patient who did not meet the imaging diagnosis and measurement requirements; two patients with malignant tumors.

A total of 63 patients with DVT were enrolled in this study, of whom 37 (58.7%) were male and 26 (41.3%) were female, with the mean age was 63.9±8.49 years. Overall, 23 cases (36.5%) were in the DVT without APE group, of whom 14 (60.9%) were male and 9 (39.1%) were female, with a mean age of 62.2±6.17 years. Also, 40 cases (63.5%) were in the DVT with APE group, of whom 23 (57.5%) were male and 17 (42.5%) were female, with an average age of 64.2±8.67 years. In all patients, the thrombus was located in the segmental and proximal pulmonary arteries (pulmonary trunk, right and left pulmonary arteries, lobar pulmonary arteries, and segmental pulmonary arteries). Radiation dose parameters: the mean DLP of CTV was 460±205 mGy·cm, and the mean CT dose index (CTDI) was 6.6±21.6 mGy. The mean DLP of CTPA was 270.6±82.3 mGy·cm, and the mean CTDI was 10.1±2.2 mGy.

No statistically significant difference was found in sex, age, smoking status, hypertension, and diabetes mellitus between the DVT with APE group and the DVT without APE group (P>0.05) (Table 1).

Comparison of energy spectrum parameters between two groups

The differences in 40 keV CT values, λ, Eff-Z, and I(W) values between the two groups were statistically significant (P<0.05). Specifically, the 40 keV CT values, λ, Eff-Z, and I(W) values of the DVT without APE group were higher than those of the DVT with APE group (P<0.05) (Table 2).

Univariate and multivariate regression analyses

The results of univariate and multivariate logistic regression analyses showed that 40 keV CT value, λ, Eff-Z, and I(W) value were independent predictors of the risk of APE in patients with DVT (P<0.05) (Tables 3,4).

Predictive efficacy analysis of energy spectrum parameters

The AUC of 40 keV CT value, λ, Eff-Z, and I(W) value for predicting the risk of APE in patients DVT were 0.791 [95% confidence interval (CI): 0.681–0.901], 0.726 (95% CI: 0.592–0.860), 0.745 (95% CI: 0.620–0.869), and 0.739 (95% CI: 0.617–0.860) (Table 5). DeLong’s test showed that there was no statistically significant difference between the 40 keV CT values, λ, Eff-Z, and I(W) values (Table 6), suggesting that these single parameters do not significantly differ in predicting APE risk.

Additionally, the combined curve of the four parameters produced an AUC of 0.930 (95% CI: 0.868–0.992) (Figure 2, Table 5). The DeLong’s test showed that the predictive value of the combined curve was significantly higher than that of the single-parameter curve (P<0.05) (Table 7). This indicates that the combined model demonstrates superior discriminative ability compared to the single-parameter model, offering more accurate predictions of APE risk. The high sensitivity and specificity also provide a more reliable basis for the prediction results, demonstrating the significant improvement in performance after integrating multiple parameters, which compensates for the limitations of single parameters in prediction accuracy of APE risk.

Figure 2 ROC curve analysis for each parameter. λ: slope of the energy spectrum. AUC, area under the curve; CT, computed tomography; Eff-Z, effective atomic number; I(W), iodine-water; ROC, receiver operating characteristic.

The H-L test showed that the combined curve had a better calibration performance (Figure 3). The clinical decision curve showed that the combined model had the highest net benefit at a risk probability >0.01, was the most effective in predicting the risk of APE, and had the highest clinical application value (Figure 4).

Figure 3 Calibration curve.

Figure 4 Clinical decision curve.

Discussion

In our study, we explored the utility of energy spectral CT parameters in predicting the risk of APE in patients with DVT. Our findings revealed that 40 keV CT value, λ, Eff-Z, and I(W) value were independent predictors of the risk of APE in patients with DVT. The combined application exhibits a strong predictive ability for the risk of APE in patients with DVT. It facilitates timely intervention and the implementation of appropriate treatment strategies, thereby reducing the incidence of APE. A thrombus consists of different proportions of erythrocytes, fibrin, leukocytes, and platelets. A linear correlation has been documented between the attenuation of the CT value of thrombus and the erythrocyte proportion (14). Thrombus with a high erythrocyte content exhibit elevated densities on CT, whereas platelets and fibrin show a negative correlation with HU density (15). The formation of fibrin networks and their impact on clot strength have been extensively investigated (16). Several studies utilizing conventional CT have proposed that CT value of thrombus in patients with DVT may serve as a predictor of the risk of APE in these patients (17,18). Jeong et al. (18) identified that as the density of DVT increases, likelihood of thrombus migration into the pulmonary circulation also arises, potentially leading to an APE. Consequently, the higher the CT value of the thrombus, the greater the risk of spontaneous thrombus dissolution and subsequent migration of thrombus into the pulmonary circulation. Therefore, there is a correlation between CT values of thrombi and stability.

In our study, the group of DVT without APE exhibited higher 40 keV CT values compared to the group of DVT with APE. These findings contrasted with results from previous conventional CT scans. Borggrefe et al. (19) conducted a comparative analysis of thrombus composition using conventional CT and energy spectral CT. Consistent with our study findings, they concluded that thrombi with a high erythrocyte content appear less dense on energy spectral CT than those with a lower erythrocyte percentage. This discrepancy may be attributed to the DVT without APE group having a higher fibrin content, which facilitates greater penetration and localization of iodine contrast within the clot compared to thrombi with a high erythrocyte content. Supporting this hypothesis, Hou et al. (20) identified a significant correlation between thrombus fibrinogen content and iodine contrast uptake, with fibrin/platelet-rich thrombi demonstrating higher permeability than those dominated by RBCs. In addition to iodine contrast, low-energy X-rays notably increased CT values, with iodine exhibiting an amplifying effect on CT values at low keV levels, surpassing those observed at standard keV levels (21). This phenomenon results in more pronounced enhancement at low energies during angiography using iodine contrast, leading to a substantial increase in the contrast between blood vessels and surrounding tissues. Consequently, even a small amount of iodine-containing contrast can induce notable changes in CT values. However, these studies did not delve into the predictive value of these parameters for thrombotic stability and clinical outcomes.

Our study findings indicated that the λHU value of the group with DVT without APE was higher than that of the DVT-APE group. The λHU value reflects the degree of change in the attenuation of a substance under varying keV conditions (22). The principle underlying the higher λHU in the DVT without APE group is analogous to the increase in CT value at 40 keV, where a high fibrin content is associated with iodine uptake, resulting in higher attenuation coefficients. Jiang et al. (14) corroborated the lower λHU values of RBC dominant thrombi through in vitro thrombus studies. Variations in thrombus composition are reflected in the slope of the energy spectrum curve, serving as a crucial parameter for distinguishing thrombi of different compositions. This parameter also facilitates the exploration of the relationship between DVT and APE, thereby providing a foundation for further quantification of thrombus heterogeneity.

The Eff-Z represents the average atomic number of a mixture and characterizes electronic changes at the atomic level. Different substances possess distinct effective atomic numbers, enabling differentiation between substances with similar densities but varying compositions through the calculation and analysis of this parameter (23). This approach is currently prevalent in the field of stone compositional profiling. Urological stones exhibit diverse compositions, with variations in the atomic numbers of their components, which manifest as distinct characteristics in energy spectrum imaging (24). Analyzing stone composition based on atomic number can substantially enhance the relevance and efficacy of treatment plans. The findings of this study suggested that the observed differences in Eff-Z between the DVT without APE group and the DVT with APE group may be attributed to variations in thrombus composition. These compositional differences potentially influence the uptake or distribution of the contrast agent within the body. Specifically, the Eff-Z was higher in the DVT without APE group, possibly owing to a greater fibrin content in the thrombus, which facilitates the penetration of the contrast agent into the coagulum. This increased Eff-Z in the DVT without APE group may also result from more localized contrast and the higher effective atomic number of the iodine-based contrast agent. The elevated Eff-Z observed in the group of DVT without APE may be attributed to the increased fibrin content within their thrombi. This higher fibrin content facilitates the penetration of the contrast agent into the clot (20), resulting in a greater local accumulation of the contrast agent and consequently, a higher Eff-Z.

The I(W) value represents the absolute concentration of iodine in the iodinated contrast agent as depicted on iodine-based maps, which are highly sensitive to variations in tissue and organ perfusion (25), thereby enabling comprehensive quantification of iodine uptake in tissues. Several studies have utilized the iodine content from iodine-based images to estimate the extent of active bleeding in the small intestine (26,27). Additionally, researchers have employed energy spectrum CT iodine (water) paired with base substance content and energy spectrum curves to differentiate between portal vein thrombosis and malignant emboli (27). The findings of our study demonstrated a difference in the I(W) value between the DVT without APE group and the DVT with APE group. This suggests that the I(W) value may reflect the iodine contrast agent content within the thrombus, potentially indicating variations in contrast agent uptake and perfusion between the two groups of thrombus. This insight could facilitate the analysis of thrombus characteristics and elucidate the potential relationship between different thrombus types and the risk of APE from the perspective of iodine contrast agent concentration.

In summary, our study confirmed that energy spectrum CT parameters can be used to predict the risk of APE in DVT patients, with 40 keV CT values, λ, Eff-Z, and I(W) values serving as independent predictors of APE risk. The combined use of these parameters significantly improves the accuracy of predicting the risk of APE, aiding physicians in identifying patients at risk of APE at an early stage and promptly implementing proactive treatment measures to reduce APE-related complications. Spectral CT remains in the early stage of clinical application in the field of APE, but it holds significant potential for both research and clinical applications. In the future, it is anticipated that image resolution and signal-to-noise ratio will further improve, while scanning parameters and reconstruction algorithms will be optimized. This will enable more precise measurement of spectral CT parameters, reduce patient radiation exposure, and provide high-quality images. Additionally, by conducting in-depth research into the interrelationship between spectral CT parameters and the pathophysiology of APE, or by integrating relevant clinical data, pathological results, advanced technologies (such as machine learning and deep learning), and other imaging data [such as magnetic resonance imaging (MRI) and ultrasound], it may be possible to identify more representative parameters and indicators associated with APE disease stratification and prognosis. This will provide stronger support for the clinical diagnosis and treatment of APE.

Some limitations of our study warrant consideration. Firstly, the relatively small sample size constrains the generalizability of our findings; thus, validation through studies with larger sample sizes is necessary. Secondly, manual delineation of thrombi relies on the experience of the operator, and measurement results may vary between different operators, thereby affecting the accuracy and consistency of the data. Future research may utilize artificial intelligence technology to achieve automatic segmentation and measurement of thrombi, thereby improving the objectivity and reproducibility of thrombus ROI delineation and measurement. Thirdly, our study primarily focused on evaluating the significance of energy spectrum parameters in relation to thrombus stability without exploring their potential association with clinical symptoms and other indicators, such as the absence of symptom duration and dyspnea/chest pain that may be related to the risk of APE. Future research should consider the comprehensive analysis of diverse indicators from imaging and clinical parameters to offer a more comprehensive assessment of thrombus stability. Larger prospective studies will be conducted in the future to validate the results of this study, expand the scope of the study and optimize clinical practice, and actively explore the potential of AI in improving thrombus segmentation and quantitative analysis. Finally, potential limitations of statistical significance: The statistical significance of certain findings does not necessarily mean that they have a significant impact in actual clinical settings. For instance, although the AUC values of various parameters have a certain predictive ability for APE risk, their sensitivity and specificity may be relatively low. While the combined model is effective in predicting outcomes with an AUC value of 0.930 (95% CI: 0.868–0.992), in actual clinical situations, other clinical data and the physician’s professional judgement must be considered simultaneously to ensure a comprehensive assessment of the patient’s
APE risk.

Conclusions

Energy spectrum CT-derived parameters demonstrate predictive value for assessing thrombus stability in DVT. Specifically, 40 keV CT values, λ, Eff-Z, and I(W) values are identified as independent risk factors for predicting APE. Their combined application can more accurately assess the risk of APE in DVT patients, thereby enabling timely intervention and the application of appropriate treatment strategies to improve patient prognosis.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-20/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-20/dss

Funding: This study was supported by the 2024 Government-funded Training Program for Excellence in Clinical Medicine (No. ZF2024229) and the Department of Education of Hebei Province through the Funding Program for Cultivating the Innovative Ability of Graduate Students in Hebei Province (No. CXZZSS2024127).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-20/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 Institutional Review Board of the First Affiliated Hospital of Hebei North University (No. K2024234) 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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