BACKGROUND: Prostate cancer is one of the most common malignant tumors in middle-aged and elderly men and carries significant prognostic implications, and recent studies suggest that dual-energy computed tomography (DECT) utilizing new virtual monoenergetic images can enhance cancer detection rates. This study aimed to assess the impact of virtual monoenergetic images reconstructed from DECT arterial phase scans on the image quality of prostate lesions and their diagnostic performance for prostate cancer.
METHODS: We conducted a retrospective analysis of 83 patients with prostate cancer or prostatic hyperplasia who underwent DECT scans at Meizhou People\'s Hospital between July 2019 and December 2023. The variables analyzed included age, tumor diameter and serum prostate-specific antigen (PSA) levels, among others. We also compared CT values, signal-to-noise ratio (SNR), subjective image quality ratings, and contrast-to-noise ratio (CNR) between virtual monoenergetic images (40-100 keV) and conventional linear blending images. Receiver operating characteristic (ROC) curve analyses were performed to evaluate the diagnostic efficacy of virtual monoenergetic images (40 keV and 50 keV) compared to conventional images.
RESULTS: Virtual monoenergetic images at 40 keV showed significantly higher CT values (168.19 ± 57.14) compared to conventional linear blending images (66.66 ± 15.5) for prostate cancer (P < 0.001). The 50 keV images also demonstrated elevated CT values (121.73 ± 39.21) compared to conventional images (P < 0.001). CNR values for the 40 keV (3.81 ± 2.13) and 50 keV (2.95 ± 1.50) groups were significantly higher than the conventional blending group (P < 0.001). Subjective evaluations indicated markedly better image quality scores for 40 keV (median score of 5) and 50 keV (median score of 5) images compared to conventional images (P < 0.05). ROC curve analysis revealed superior diagnostic accuracy for 40 keV (AUC: 0.910) and 50 keV (AUC: 0.910) images based on CT values compared to conventional images (AUC: 0.849).
CONCLUSIONS: Virtual monoenergetic images reconstructed at 40 keV and 50 keV from DECT arterial phase scans substantially enhance the image quality of prostate lesions and improve diagnostic efficacy for prostate cancer.

Resorbable inferior vena cava (IVC) filters require embedded contrast for image-guided placement and integrity monitoring. We calculated correction factors to account for partial volume averaging of thin nanoparticle (NP)-embedded materials, accounting for object and slice thicknesses, background signal, and nanoparticle concentration. We used phantoms containing polycaprolactone disks embedded with bismuth (Bi) or ytterbium (Yb): 0.4- to 1.2-mm-thick disks of 20 mg ml-1NPs (thickness phantom), 0.4-mm-thick disks of 0-20 mg ml-1NPs in 2 mg ml-1iodine (concentration phantom), and 20 mg ml-1NPs in 0.4-mm-thick disks in 0-10 mg ml-1iodine (background phantom). Phantoms were scanned on a dual-source CT with 80, 90, 100, and 150 kVp with tin filtration and reconstructed at 1.0- to 1.5-mm slice thickness with a 0.1-mm interval. Following scanning, disks were processed for inductively coupled plasma optical emission spectrometry (ICP-OES) to determine NP concentration. Mean and maximum CT numbers (HU) of all disks were measured over a 0.5-cm2area for each kVp. HU was converted to concentration using previously measured calibrations. Concentration measurements were corrected for partial volume averaging by subtracting residual slice background and extrapolating disk thickness to both nominal and measured slice sensitivity profiles (SSP, mm). Slice thickness to agreement (STTA, mm) was calculated by replacing the CT-derived concentrations with ICP-OES measurements and solving for thickness. Slice thickness correction factors improved agreement with ICP-OES for all measured data. Yb corrections resulted in lower STTA than Bi corrections in the concentration phantom (1.01 versus 1.31 STTA/SSP, where 1.0 is perfect agreement), phantoms with varying thickness (1.30 versus 1.87 STTA/SSP), and similar ratio in phantoms with varying background iodine concentration (1.34 versus 1.35 STTA/SSP). All measured concentrations correlated strongly with ICP-OES and all corrections for partial volume averaging increased agreement with ICP-OES concentration, demonstrating potential for monitoring the integrity of thin IVC resorbable filters with CT.

Dual-Energy computed tomography (DECT) with its various advanced techniques, including Virtual Non-Contrast (VNC), effective atomic number (Z-eff) calculation, Z-maps, Iodine Density Index (IDI), and so on, holds great promise in the diagnosis and management of urogenital tumours. In this narrative review, we analyze the current status of knowledge of this technology to provide better lesion characterization, improve the staging accuracy, and give more precise treatment response assessments in relation to urological tumours.

UNASSIGNED: The effects of obesity on pulmonary gas and blood distribution in patients with acute respiratory failure remain unknown. Dual-energy computed tomography (DECT) is a X-ray-based method used to study regional distribution of gas and blood within the lung. We hypothesized that 1) regional gas/blood mismatch can be quantified by DECT; 2) obesity influences the global and regional distribution of pulmonary gas and blood; 3) regardless of ventilation modality (invasive vs. non-invasive ventilation), patients\' body mass index (BMI) has an impact on pulmonary gas/blood mismatch.
UNASSIGNED: This single-centre prospective observational study enrolled 118 hypoxic COVID-19 patients (92 male) in need of respiratory support and intensive care who underwent DECT. The cohort was divided into three groups according to BMI: 1. BMI<25 kg/m2 (non-obese), 2. BMI = 25-40 kg/m2 (overweight to obese), and 3. BMI>40 kg/m2 (morbidly obese). Gravitational analysis of Hounsfield unit distribution of gas and blood was derived from DECT and used to calculate regional gas/blood mismatch. A sensitivity analysis was performed to investigate the influence of the chosen ventilatory modality and BMI on gas/blood mismatch and adjust for other possible confounders (i.e., age and sex).
UNASSIGNED: 1) Regional pulmonary distribution of gas and blood and their mismatch were quantified using DECT imaging. 2) The BMI>40 kg/m2 group had less hyperinflation in the non-dependent regions and more lung collapse in the dependent regions compared to the other BMI groups. In morbidly obese patients, gas and blood were more evenly distributed; therefore, the mismatch was lower than in other patients (30% vs. 36%, p < 0.05). 3) An increase in BMI of 5 kg/m2 was associated with a decrease in mismatch of 3.3% (CI: 3.67% to -2.93%, p < 0.05). Neither the ventilatory modality nor age and sex affected the gas/blood mismatch (p > 0.05).
UNASSIGNED: 1) In a hypoxic COVID-19 population needing intensive care, pulmonary gas/blood mismatch can be quantified at a global and regional level using DECT. 2) Obesity influences the global and regional distribution of gas and blood within the lung, and BMI>40 kg/m2 improves pulmonary gas/blood mismatch. 3) This is true regardless of the ventilatory mode and other possible confounders, i.e., age and sex.
UNASSIGNED: Clinicaltrials.gov, identifier NCT04316884, NCT04474249.

BACKGROUND: This study aimed to evaluate the potential of dual-energy computed tomography (CT) to distinguish postoperative ascites, pancreatic fistula, and abscesses.
METHODS: Patients who underwent biliary and pancreatic surgery performed at our institution between June 2021 and February 2022 were included in the study. Postoperative body fluid samples were collected through a drain or percutaneous drainage. These samples were set in a phantom, and imaging data were obtained using dual-energy CT. Image analysis was performed to obtain CT values at each energy in virtual monoenergetic images (VMIs), effective atomic number, iodine map, and virtual non-contrast (VNC) images. VMIs were calculated from 80 and 140 kVp tube data at 10 kV each from 40-140 kV. Additionally, the effective atomic number, iodine map, and VNC images were reconstructed from the material decomposition process using water and iodine as the base material pair.
RESULTS: In this study, 25 patients (eight with abscess and 17 with ascites) were included. No significant association was observed between the presence or absence of abscess and malignancy or surgical procedure. The intervention was performed in six of the eight patients with abscesses. In contrast, five of the 17 patients with postoperative ascites required intervention. A significant relationship was observed between the intervention and the presence of an abscess. Significant differences in C-reactive protein values and the incidence of fever were observed between the groups. Only VNC showed a significant difference between the groups.
CONCLUSIONS: VNC using dual-energy CT could differentiate abscesses from postoperative fluid.

BACKGROUND: This meta-analysis assessed the efficacy of dual-energy computed tomography (DECT) in the diagnosis of anterior cruciate ligament (ACL) injuries.
METHODS: The literature search was performed up to December 8, 2023, and included a comprehensive examination of several databases: PubMed, Embase, Cochrane Library, Web of Science, China National Knowledge Infrastructure (CNKI), Wanfang, and VIP. Diagnostic metrics sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and a summary receiver operating characteristic (SROC) were determined using a bivariate model analysis. Heterogeneity within the data was explored through subgroup analyses, which considered variables including geographical region, use of magnetic resonance imaging (MRI), arthroscopy, and study design.
RESULTS: The analysis included ten studies encompassing 544 patients. DECT demonstrated substantial diagnostic utility for ACL injuries of the knee, with a sensitivity of 0.91 (95% confidence interval [CI]: 0.88-0.94), a specificity of 0.90 (95% CI: 0.81-0.95), a PLR of 9.20 (95% CI: 4.50-19.00), a NLR of 0.10 (95% CI: 0.06-0.14), a DOR of 97.00 (95% CI: 35.00-268.00), and an area under the curve (AUC) of 0.95 (95% CI: 0.93-0.97). The subgroup analyses consistently showed high diagnostic precision for ACL injuries across Asian population (sensitivity: 0.91, specificity: 0.91, PLR: 9.90, NLR: 0.09, DOR: 105.00, AUC: 0.96), in MRI subgroup (sensitivity: 0.85, specificity: 0.94, PLR: 9.57, NLR: 0.18, DOR: 56.00, AUC: 0.93), in arthroscopy subgroup (sensitivity: 0.92, specificity: 0.89, PLR: 8.40, NLR: 0.09, DOR: 94.00, AUC: 0.95), for prospective studies (sensitivity: 0.92, specificity: 0.88, PLR: 7.40, NLR: 0.09, DOR: 78.00, AUC: 0.95), and for retrospective studies (sensitivity: 0.91, specificity: 0.93, AUC: 0.93).
CONCLUSIONS: DECT exhibits a high value in diagnosing ACL injuries. The significant diagnostic value of DECT provides clinicians with a powerful tool that enhances the accuracy and efficiency of diagnosis and optimizes patient management and treatment outcomes.

Objective: Our study aims to evaluate the value of 256-slice dual-energy computed tomography (DECT) in supporting prostatic artery embolization (PAE) under digital subtraction angiography (DSA) for benign prostatic hyperplasia (BPH). Methods: The study was conducted on 88 patients who underwent PAE to treat BPH from January 2022 to November 2023. Of these, 38 patients who had PAE without DECT were placed in group 1, while the other 50 patients with pre-interventional DECT were assigned to group 2. The results of DECT imaging of the prostate artery (PA) were compared with the results of DSA imaging. Test for statistically significant differences between the variables of the two research groups using the T - student test and Mann-Whitney test algorithms with p < 0.05 corresponding to a 95% confidence interval. The data were analyzed according to medical statistical methods using SPSS 20.0 software. Results: DECT can detect the PA origin in 96.1% of cases, identify atherosclerosis at the root of the artery with a sensitivity of 66.7% and a specificity of 89.5%, and present anastomosis with a sensitivity of 72.7% and a specificity of 72.2%. There is no statistically significant difference in PA diameter on DECT compared to DSA with 95% confidence. Group 2 used DECT for 3D rendering of the PA before PAE had procedure time reduced by 25.8%, fluoroscopy time reduced by 23.2%, dose-area product (DAP) reduced by 25.6%, contrast medium volume reduced by 33.1% compared to group 1 not using DECT, statistically significant with 95% confidence. Conclusion: DECT is a valuable method for planning before PAE to treat BPH. 3D rendering DECT of PA provides anatomical information that minimizes procedure time, fluoroscopy time, dose-area product, and contrast medium volume.

OBJECTIVE: To develop thrombus radiomics models based on dual-energy CT (DECT) for predicting etiologic cause of stroke.
METHODS: We retrospectively enrolled patients with occlusion of the middle cerebral artery who underwent computed tomography (NCCT) and DECT angiography (DECTA). 70 keV virtual monoenergetic images (simulate conventional 120kVp CTA images) and iodine overlay maps (IOM) were reconstructed for analysis. Five logistic regression radiomics models for predicting cardioembolism (CE) were built based on the features extracted from NCCT, CTA and IOM images. From these, the best one was selected to integrate with clinical information for further construction of the combined model. The performance of the different models was evaluated and compared using ROC curve analysis, clinical decision curves (DCA), calibration curves and Delong test.
RESULTS: Among all the radiomic models, model NCCT+IOM performed the best, with AUC = 0.95 significantly higher than model NCCT, model CTA, model IOM and model NCCT+CTA in the training set (AUC = 0.88, 0.78, 0.90,0.87, respectively, P < 0.05), and AUC = 0.92 in the testing set, significantly higher than model CTA (AUC = 0.71, P < 0.05). Smoking and NIHSS score were independent predictors of CE (P < 0.05). The combined model performed similarly to the model NCCT+IOM, with no statistically significant difference in AUC either in the training or test sets. (0.96 vs. 0.95; 0.94 vs. 0.92, both P > 0.05).
CONCLUSIONS: Radiomics models constructed based on NCCT and IOM images can effectively determine the source of thrombus in stroke without relying on clinical information.

OBJECTIVE: To develop and validate a dual-energy CT (DECT)-based model for noninvasively differentiating between benign and malignant breast lesions detected on DECT.
METHODS: This study prospectively enrolled patients with suspected breast cancer who underwent dual-phase contrast-enhanced DECT from July 2022 to July 2023. Breast lesions were randomly divided into the training and test cohorts at a ratio of 7:3. Clinical characteristics, DECT-based morphological features, and DECT quantitative parameters were collected. Univariate analyses and multivariate logistic regression were performed to determine independent predictors of benign and malignant breast lesions. An individualized model was constructed. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic ability of the model, whose calibration and clinical usefulness were assessed by calibration curve and decision curve analysis.
RESULTS: This study included 200 patients (mean age, 49.9 ± 11.9 years; age range, 22-83 years) with 222 breast lesions. Age, lesion shape, and the effective atomic number (Zeff) in the venous phase were significant independent predictors of breast lesions (all p < 0.05). The discriminative power of the model incorporating these three factors was high, with AUCs of 0.844 (95%CI 0.764-0.925) and 0.791 (95% CI 0.647-0.935) in the training and test cohorts, respectively. The constructed model showed a preferable fitting (all p > 0.05 by the Hosmer-Lemeshow test) and provided enhanced net benefits than simple default strategies within a wide range of threshold probabilities in both cohorts.
CONCLUSIONS: The DECT-based model showed a favorable diagnostic performance for noninvasive differentiation between benign and malignant breast lesions detected on DECT.
UNASSIGNED: The combination of clinical and morphological characteristics and DECT-derived parameter have the potential to identify benign and malignant breast lesions and it may be useful for incidental breast lesions on DECT to decide if further work-up is needed.
CONCLUSIONS: It is important to characterize incidental breast lesions on DECT for patient management. DECT-based model can differentiate benign and malignant breast lesions with good performance. DECT-based model is a potential tool for distinguishing breast lesions detected on DECT.

OBJECTIVE: Microvascular invasion (MVI) is a recognized biomarker associated with poorer prognosis in patients with hepatocellular carcinoma. Dual-energy computed tomography (DECT) is a highly sensitive technique that can determine the iodine concentration (IC) in tumour and provide an indirect evaluation of internal microcirculatory perfusion. This study aimed to assess whether the combination of DECT with laboratory data can improve preoperative MVI prediction.
METHODS: This retrospective study enrolled 119 patients who underwent DECT liver angiography at 2 medical centres preoperatively. To compare DECT parameters and laboratory findings between MVI-negative and MVI-positive groups, Mann-Whitney U test was used. Additionally, principal component analysis (PCA) was conducted to determine fundamental components. Mann-Whitney U test was applied to determine whether the principal component (PC) scores varied across MVI groups. Finally, a general linear classifier was used to assess the classification ability of each PC score.
RESULTS: Significant differences were noted (P < .05) in alpha-fetoprotein (AFP) level, normalized arterial phase IC, and normalized portal phase IC between the MVI groups in the primary and validation datasets. The PC1-PC4 accounted for 67.9% of the variance in the primary dataset, with loadings of 24.1%, 16%, 15.4%, and 12.4%, respectively. In both primary and validation datasets, PC3 and PC4 were significantly different across MVI groups, with area under the curve values of 0.8410 and 0.8373, respectively.
CONCLUSIONS: The recombination of DECT IC and laboratory features based on varying factor loadings can well predict MVI preoperatively.
CONCLUSIONS: Utilizing PCA, the amalgamation of DECT IC and laboratory features, considering diverse factor loadings, showed substantial promise in accurately classifying MVI. There have been limited endeavours to establish such a combination, offering a novel paradigm for comprehending data in related research endeavours.