On-Site Computed Tomography-Derived Fractional Flow Reserve to Guide Management of Patients With Stable Coronary Artery Disease: The TARGET Randomized Trial.

BACKGROUND: Computed tomography-derived fractional flow reserve (CT-FFR) using on-site machine learning enables identification of both the presence of coronary artery disease and vessel-specific ischemia. However, it is unclear whether on-site CT-FFR improves clinical or economic outcomes when compared with the standard of care in patients with stable coronary artery disease. METHODS: In total, 1216 patients with stable coronary artery disease and an intermediate stenosis of 30% to 90% on coronary computed tomographic angiography were randomized to an on-site CT-FFR care pathway using machine learning or to standard care in 6 Chinese medical centers. The primary end point was the proportion of patients undergoing invasive coronary angiography without obstructive coronary artery disease or with obstructive disease who did not undergo intervention within 90 days. Secondary end points included major adverse cardiovascular events, quality of life, symptoms of angina, and medical expenditure at 1 year. RESULTS: Baseline characteristics were similar in both groups, with 72.4% (881/1216) having either typical or atypical anginal symptoms. A total of 421 of 608 patients (69.2%) in the CT-FFR care group and 483 of 608 patients (79.4%) in the standard care group underwent invasive coronary angiography. Compared with standard care, the proportion of patients undergoing invasive coronary angiography without obstructive coronary artery disease or with obstructive disease not undergoing intervention was significantly reduced in the CT-FFR care group (28.3% [119/421] versus 46.2% [223/483]; P<0.001). Overall, more patients underwent revascularization in the CT-FFR care group than in the standard care group (49.7% [302/608] versus 42.8% [260/608]; P=0.02), but major adverse cardiovascular events at 1 year did not differ (hazard ratio, 0.88 [95% CI, 0.59-1.30]). Quality of life and symptoms improved similarly during follow-up in both groups, and there was a trend towards lower costs in the CT-FFR care group (difference, -¥4233 [95% CI, -¥8165 to ¥973]; P=0.07). CONCLUSIONS: On-site CT-FFR using machine learning reduced the proportion of patients with stable coronary artery disease undergoing invasive coronary angiography without obstructive disease or requiring intervention within 90 days, but increased revascularization overall without improving symptoms or quality of life, or reducing major adverse cardiovascular events. REGISTRATION: URL: https://www. CLINICALTRIALS: gov; Unique identifier: NCT03901326.

One-stop patient-specific myocardial blood flow quantification technique based on allometric scaling law.

Establishing a patient-specific and non-invasive technique to derive blood flow as well as coronary structural information from one single cardiac CT imaging modality. 336 patients with chest pain or ST segment depression on electrocardiogram were retrospectively enrolled. All patients underwent adenosine-stressed dynamic CT myocardial perfusion imaging (CT-MPI) and coronary computed tomography angiography (CCTA) in sequence. Relationship between myocardial mass (M) and blood flow (Q), defined as log(Q) = b · log(M) + log(Q0), was explored based on the general allometric scaling law. We used 267 patients to obtain the regression results and found strong linear relationship between M (gram) and Q (mL/min) (b = 0.786, log(Q0) = 0.546, r = 0.704; p < 0.001). We Also found this correlation was applicable for patients with either normal or abnormal myocardial perfusion (p < 0.001). Datasets from the other 69 patients were used to validate this M-Q correlation and found the patient-specific blood flow could be accurately estimated from CCTA compared to that measured from CT-MPI (146.480 ± 39.607 vs 137.967 ± 36.227, r = 0.816, and 146.480 ± 39.607 vs 137.967 ± 36.227, r = 0.817, for the left ventricle region and LAD-subtended region, respectively, all unit in mL/min). In conclusion, we established a technique to provide general and patient-specific myocardial mass-blood flow correlation obeyed to allometric scaling law. Blood flow information could be directly derived from structural information acquired from CCTA.

A deep learning-based fully automatic and clinical-ready framework for regional myocardial segmentation and myocardial ischemia evaluation.

Myocardial ischemia diagnosis with CT perfusion imaging (CTP) is important in coronary artery disease management. Traditional analysis procedure is time-consuming and error-prone due to the semi-manual and operator-dependent nature. To improve the diagnostic performance, a deep learning-based, fully automatic, and clinical-ready framework was developed. Two collaborating deep learning networks including a 3D U-Net for left ventricle segmentation and a CNN for anatomical landmarks detection were trained on 276 subjects. With our processing framework, the 17-segment left ventricular model was automatically generated conformed to the clinical standard. Myocardial blood flow computed by commercial software was extracted within each segment and visualized against the bull's eye plot. The performance was validated on another 45 subjects. Coronary angiography and invasive fractional flow reserve measurements were also performed in these patients to serve as the gold standard for myocardial ischemia diagnosis. As a result, the diagnostic accuracy for our method was 81.08%, much higher than that for commercially available CTP analysis software (56.75%), and our method demonstrated a higher consistency (Kappa coefficient 0.759 vs. 0.585). Besides, the average processing time of our method was much lower (30 ± 10.5 s/subject vs. over 30 min/subject). In conclusion, the proposed deep learning-based framework could be a promising tool for assisting CTP analysis.

A prospective randomized trial comparing microwave and radiofrequency ablation for the treatment of liver metastases using a dual ablation system ─ The Mira study.

PURPOSE: The aim of this study was to prospectively compare the therapy response and safety of microwave (MWA) and radiofrequency ablation (RFA) for the treatment of liver metastases using a dual ablation system. METHODS: Fifty patients with liver metastases (23 men, mean age: 62.8 ± 11.8 years) were randomly assigned to MWA or RFA for thermal ablation using a one generator dual ablation system. Magnetic resonance imaging (MRI) was acquired before treatment and 24 h post ablation. The morphologic responses to treatment regarding size, volume, necrotic areas, and diffusion characteristics were evaluated by MRI. Imaging follow-up was obtained for one year in three months intervals, whereas clinical follow-up was obtained for two years in all patients. RESULTS: Twenty-six patients received MWA and 24 patients received RFA (mean diameter: 1.6 cm, MWA: 1.7 cm, RFA: 1.5 cm). The mean volume 24 h after ablation was 37.0 cm3 (MWA: 50.5 cm3, RFA: 22.9 cm3, P < 0.01). The local recurrence rate was 0% (0/26) in the MWA-group and 8.3% (2/24) in the RFA-group (P = 0.09). The rate of newly developed malignant formations was 38.0% (19/50) for both groups (MWA: 38.4%, RFA: 37.5%, P = 0.07). The overall survival rate was 70.0% (35/50) after two years (MWA: 76.9%, RFA: 62.5%, P = 0.60). No major complications were reported. CONCLUSION: In conclusion, MWA and RFA are both safe and effective methods for the treatment of liver metastases with MWA generating greater volumes of ablation. No significant differences were found for overall survival, rate of neoplasm, or major complications between both groups.

Prevalence and Associated Risk Factors of Pulmonary Embolism in Children and Young Adults With Nephrotic Syndrome: A Chinese Large Cohort Study.

PURPOSE: Nephrotic syndrome (NS) is highly associated with an increased risk of pulmonary embolism (PE) in children and young adults. However, few studies have specified the risk factors of PE in children and young adults with NS. We sought to determine the prevalence and associated factors of PE confirmed with computed tomography pulmonary angiography in Chinese children and young adults with NS. METHODS: Data from 444 children and young adults with NS who had computed tomography pulmonary angiography from December 2010 to October 2018 were retrospectively analyzed. The prevalence of PE was estimated for different age, sex, and histopathologic types of NS. Multivariable logistic regression was used to identify independent risk factors of PE in children and young adults with NS. Models incorporating the independent risk factors were evaluated using receiver operation characteristic curves. Area under the curve was used to determine the best-performing prognosticators for predicting PE. RESULTS: There were 444 patients in the study cohort (310 male patients, 134 female patients; mean age 19±3 y; range: 6 to 25 y). PE was present in 24.8% of the participants (110 of 444, 18.2% female). Children and young adult NS patients with PE tend to be older, male, to have a previous thromboembolism history and smoking, and have a higher level of proteinuria, D-dimer, and serum albumin (P<0.05 for all). Children and young adults with membranous nephropathy are likely to have a higher incidence of PE than those with other types of nephropathy. Membranous nephropathy and proteinuria were significant predictors of PE in children and young adults with NS (P<0.05 for all). The area under the curves of each model for the presence of PE in children and young adults with NS based on biochemical parameters and clinical information (model 1), adjusted for proteinuria (model 2), and adjusted for membranous nephropathy (model 3) were 0.578, 0.657, and 0.709, respectively. Compared with model 1, model 2, and model 3 showed statistically significant differences (model 1 vs. model 2, P=0.0336; model 1 vs. model 3, P=0.0268). There was no statistically significant difference between model 2 and model 3 (P=0.2947). CONCLUSION: This study identified membranous nephropathy and proteinuria as independent associated factors of PE in children and young adults with NS, which can be noted as a risk factor to guide clinician management in this population.

Influence of coronary stenosis location on diagnostic performance of machine learning-based fractional flow reserve from CT angiography.

BACKGROUND: Compared with invasive fractional flow reserve (FFR), coronary CT angiography (cCTA) is limited in detecting hemodynamically relevant lesions. cCTA-based FFR (CT-FFR) is an approach to overcome this insufficiency by use of computational fluid dynamics. Applying recent innovations in computer science, a machine learning (ML) method for CT-FFR derivation was introduced and showed improved diagnostic performance compared to cCTA alone. We sought to investigate the influence of stenosis location in the coronary artery system on the performance of ML-CT-FFR in a large, multicenter cohort. METHODS: Three hundred and thirty patients (75.2% male, median age 63 years) with 502 coronary artery stenoses were included in this substudy of the MACHINE (Machine Learning Based CT Angiography Derived FFR: A Multi-Center Registry) registry. Correlation of ML-CT-FFR with the invasive reference standard FFR was assessed and pooled diagnostic performance of ML-CT-FFR and cCTA was determined separately for the following stenosis locations: RCA, LAD, LCX, proximal, middle, and distal vessel segments. RESULTS: ML-CT-FFR correlated well with invasive FFR across the different stenosis locations. Per-lesion analysis revealed improved diagnostic accuracy of ML-CT-FFR compared with conventional cCTA for stenoses in the RCA (71.8% [95% confidence interval, 63.0%-79.5%] vs. 54.8% [45.7%-63.8%]), LAD (79.3 [73.9-84.0] vs. 59.6 [53.5-65.6]), LCX (84.1 [76.0-90.3] vs. 63.7 [54.1-72.6]), proximal (81.5 [74.6-87.1] vs. 63.8 [55.9-71.2]), middle (81.2 [75.7-85.9] vs. 59.4 [53.0-65.6]) and distal stenosis location (67.4 [57.0-76.6] vs. 51.6 [41.1-62.0]). CONCLUSION: In a multicenter cohort with high disease prevalence, ML-CT-FFR offered improved diagnostic performance over cCTA for detecting hemodynamically relevant stenoses regardless of their location.

Serial coronary CT angiography-derived fractional flow reserve and plaque progression can predict long-term outcomes of coronary artery disease.

OBJECTIVE: To investigate the utility of coronary CT angiography-derived fractional flow reserve (FFRCT) and plaque progression in patients undergoing serial coronary CT angiography for predicting major adverse cardiovascular events (MACE). METHODS: This retrospective study evaluated patients suspected or known coronary artery disease who underwent serial coronary CT angiography examinations between January 2006 and December 2017 and followed up until June 2019. The primary endpoint was MACE, defined as acute coronary syndrome, rehospitalization due to progressive angina, percutaneous coronary intervention, or cardiac death. FFRCT and plaque parameters were analyzed on a per-vessel and per-patient basis. Univariable and multivariable COX regression analysis determined predictors of MACE. The prognostic value of FFRCT and plaque progression were assessed in nested models. RESULTS: Two hundred eighty-four patients (median age, 61 years (interquartile range, 54-70); 202 males) were evaluated. MACE was observed in 45 patients (15.8%, 45/284). By Cox multivariable regression modeling, vessel-specific FFRCT ≤ 0.80 was associated with a 2.4-fold increased risk of MACE (HR (95% CI): 2.4 (1.3-4.4); p = 0.005) and plaque progression was associated with a 9-fold increased risk of MACE (HR (95% CI): 9 (3.5-23); p < 0.001) after adjusting for clinical and imaging risk factors. FFRCT and plaque progression improved the prediction of events over coronary artery calcium (CAC) score and high-risk plaques (HRP) in the receiver operating characteristics analysis (area under the curve: 0.70 to 0.86; p = 0.002). CONCLUSIONS: Fractional flow reserve and plaque progression assessed by serial coronary CT angiography predicted the risk of future MACE. KEY POINTS: • Vessel-specific CT angiography-derived fractional flow reserve (FFRCT) ≤ 0.80 and plaque progression improved the prediction of events over current risk factors. • Major adverse cardiovascular events (MACE) significantly increased with the presence of plaque progression at follow-up stratified by the FFRCT change group.

Different posterior hippocampus and default mode network modulation in young APOE ε4 carriers: a functional connectome-informed phenotype longitudinal study.

To determine the functional connectome change pattern based on subregions of the hippocampus in young APOEε4 carriers during a 3-year follow-up. All the participants (n = 213) were tested for resting-state functional MRI, neuropsychological scales, and APOE genotype. The age- and sex-matched APOE ε4/ε3 (23.9 ± 3.2 years old, 6 female/7 male) carriers and APOE ε3/ε3 (22.9 ± 1.6 years old, 7 female/12 male) carriers were finally followed up. The hippocampus and its anterior/middle/posterior subregion-based functional connectivity (FC) patterns were compared between APOEε4 and APOEε3 groups by a two-sample t-test at baseline and mixed-effect analysis at follow-up. The effective connectivity (EC) patterns among the altered regions of interaction effect were examined in the APOEε4 groups. APOEε4 carries displayed saliently enhanced FC in the right anterior/middle hippocampus and core regions of the default mode network (DMN) (P < 0.05 by Gaussian Random Fields (GRF) correction). However, the APOEε4-by-time interaction was evident in the middle/posterior hippocampus with connection to the lateral temporal lobe and anterior cingulate cortex (ACC) (P < 0.05 by GRF correction). Moreover, the APOEε4 group at follow-up showed increased EC separately from both the left middle hippocampus and lateral temporal lobe to the left posterior hippocampus, and its changes of FC/EC significantly correlated with altered memory function. The posterior hippocampus might be especially vulnerable to early modulation in young APOEε4 carriers. Its connection with the lateral temporal lobe, rather than with DMN, might be the early compensative mechanism of memory function regulation influenced by APOE ε4 in the young adults.

A clinically applicable deep-learning model for detecting intracranial aneurysm in computed tomography angiography images.

Intracranial aneurysm is a common life-threatening disease. Computed tomography angiography is recommended as the standard diagnosis tool; yet, interpretation can be time-consuming and challenging. We present a specific deep-learning-based model trained on 1,177 digital subtraction angiography verified bone-removal computed tomography angiography cases. The model has good tolerance to image quality and is tested with different manufacturers. Simulated real-world studies are conducted in consecutive internal and external cohorts, in which it achieves an improved patient-level sensitivity and lesion-level sensitivity compared to that of radiologists and expert neurosurgeons. A specific cohort of suspected acute ischemic stroke is employed and it is found that 99.0% predicted-negative cases can be trusted with high confidence, leading to a potential reduction in human workload. A prospective study is warranted to determine whether the algorithm could improve patients' care in comparison to clinicians' assessment.

Current Status of Etiology, Epidemiology, Clinical Manifestations and Imagings for COVID-19.

Coronavirus disease 2019 (COVID-19) is a transmissible respiratory disease that was initially reported in Wuhan, China in December 2019. With the alarming levels of COVID-19 spread worldwide, the World Health Organization characterized COVID-19 as a pandemic. Over the past several months, chest CT has played a vital role in early identification, disease severity assessment, and dynamic disease course monitoring of COVID-19. The published data has enriched our knowledge on the etiology, epidemiology, clinical manifestations, and pathologic findings of COVID-19. Additionally, as the imaging spectrum of the disease continues to be defined, extrapulmonary infections or other complications will require further attention. This review aims to provide an updated framework and essential knowledge with which radiologists can better understand COVID-19.

Machine Learning/Deep Neuronal Network: Routine Application in Chest Computed Tomography and Workflow Considerations.

The constantly increasing number of computed tomography (CT) examinations poses major challenges for radiologists. In this article, the additional benefits and potential of an artificial intelligence (AI) analysis platform for chest CT examinations in routine clinical practice will be examined. Specific application examples include AI-based, fully automatic lung segmentation with emphysema quantification, aortic measurements, detection of pulmonary nodules, and bone mineral density measurement. This contribution aims to appraise this AI-based application for value-added diagnosis during routine chest CT examinations and explore future development perspectives.

Utilizing Artificial Intelligence to Determine Bone Mineral Density Via Chest Computed Tomography.

PURPOSE: The purpose of this study was to validate the accuracy of an artificial intelligence (AI) prototype application in determining bone mineral density (BMD) from chest computed tomography (CT), as compared with dual-energy x-ray absorptiometry (DEXA). MATERIALS AND METHODS: In this Institutional Review Board-approved study, we analyzed the data of 65 patients (57 female, mean age: 67.4 y) who underwent both DEXA and chest CT (mean time between scans: 1.31 y). From the DEXA studies, T-scores for L1-L4 (lumbar vertebrae 1 to 4) were recorded. Patients were then divided on the basis of their T-scores into normal control, osteopenic, or osteoporotic groups. An AI algorithm based on wavelet features, AdaBoost, and local geometry constraints independently localized thoracic vertebrae from chest CT studies and automatically computed average Hounsfield Unit (HU) values with kVp-dependent spectral correction. The Pearson correlation evaluated the correlation between the T-scores and HU values. Mann-Whitney U test was implemented to compare the HU values of normal control versus osteoporotic patients. RESULTS: Overall, the DEXA-determined T-scores and AI-derived HU values showed a moderate correlation (r=0.55; P<0.001). This 65-patient population was divided into 3 subgroups on the basis of their T-scores. The mean T-scores for the 3 subgroups (normal control, osteopenic, osteoporotic) were 0.77±1.50, -1.51±0.04, and -3.26±0.59, respectively. The mean DEXA-determined L1-L4 BMD measures were 1.13±0.16, 0.88±0.06, and 0.68±0.06 g/cm, respectively. The mean AI-derived attenuation values were 145±42.5, 136±31.82, and 103±16.28 HU, respectively. Using these AI-derived HU values, a significant difference was found between the normal control patients and osteoporotic group (P=0.045). CONCLUSION: Our results show that this AI prototype can successfully determine BMD in moderate correlation with DEXA. Combined with other AI algorithms directed at evaluating cardiac and lung diseases, this prototype may contribute to future comprehensive preventative care based on a single chest CT.

Uric Acid Has Different Effects on Spontaneous Brain Activities of Males and Females: A Cross-Sectional Resting-State Functional MR Imaging Study.

OBJECTIVE: To explore the relationship among serum uric acid (SUA) levels in different states of disease, human cognition, and spontaneous brain activities by resting-state functional MRI (rs-fMRI). METHODS: We prospectively recruited 100 subjects (age 58 ± 11 years, 55 females) who underwent fasting blood sampling, cognitive tests and rs-fMRI scans. The subjects were divided into two groups by sex and each sex group was further stratified into three subgroups according to SUA level in different states of disease. The amplitude of low-frequency fluctuation (ALFF) method was applied to assess spontaneous brain activity among groups. Pearson's correlation analysis was used to investigate the relationships between the mean ALFF values (mALFF) and cognitive tests. RESULTS: A total of 97 patients completed the study protocol successfully. Significant differences in age, education level, number connection test (NCT), and word fluency were observed among the three subgroups in males (all P < 0.05). Results of group-by-sex interaction were distributed in bilateral pallidum and putamen [voxel P-value < 0.001, cluster P-value < 0.05, Gaussian random field (GRF)-corrected]. The tendency of the SUA effect on mALFF was different in males and females, particularly in corresponding High SUA subgroups (that is pre-hyperuricemia, both P < 0.001). Among the male subjects, mALFF values of the bilateral pallidum and putamen negatively correlated with attention/executive function. CONCLUSION: Our results suggest that elevated SUA levels have different effects on spontaneous brain activities and cognitive function in males and females. Males with pre-hyperuricemia and hyperuricemia are more susceptible to changes in spontaneous brain activities and lower neuropsychological assessment scores, particularly in word fluency tests and NCT, compared to females.

Coronary CT angiography-derived plaque quantification with artificial intelligence CT fractional flow reserve for the identification of lesion-specific ischemia.

OBJECTIVES: We sought to investigate the diagnostic performance of coronary CT angiography (cCTA)-derived plaque markers combined with deep machine learning-based fractional flow reserve (CT-FFR) to identify lesion-specific ischemia using invasive FFR as the reference standard. METHODS: Eighty-four patients (61 ± 10 years, 65% male) who had undergone cCTA followed by invasive FFR were included in this single-center retrospective, IRB-approved, HIPAA-compliant study. Various plaque markers were derived from cCTA using a semi-automatic software prototype and deep machine learning-based CT-FFR. The discriminatory value of plaque markers and CT-FFR to identify lesion-specific ischemia on a per-vessel basis was evaluated using invasive FFR as the reference standard. RESULTS: One hundred three lesion-containing vessels were investigated. 32/103 lesions were hemodynamically significant by invasive FFR. In a multivariate analysis (adjusted for Framingham risk score), the following markers showed predictive value for lesion-specific ischemia (odds ratio [OR]): lesion length (OR 1.15, p = 0.037), non-calcified plaque volume (OR 1.02, p = 0.007), napkin-ring sign (OR 5.97, p = 0.014), and CT-FFR (OR 0.81, p < 0.0001). A receiver operating characteristics analysis showed the benefit of identifying plaque markers over cCTA stenosis grading alone, with AUCs increasing from 0.61 with ≥ 50% stenosis to 0.83 with addition of plaque markers to detect lesion-specific ischemia. Further incremental benefit was realized with the addition of CT-FFR (AUC 0.93). CONCLUSION: Coronary CTA-derived plaque markers portend predictive value to identify lesion-specific ischemia when compared to cCTA stenosis grading alone. The addition of CT-FFR to plaque markers shows incremental discriminatory power. KEY POINTS: • Coronary CT angiography (cCTA)-derived quantitative plaque markers of atherosclerosis portend high discriminatory power to identify lesion-specific ischemia. • Coronary CT angiography-derived fractional flow reserve (CT-FFR) shows superior diagnostic performance over cCTA alone in detecting lesion-specific ischemia. • A combination of plaque markers with CT-FFR provides incremental discriminatory value for detecting flow-limiting stenosis.

Feasibility of extracellular volume quantification using dual-energy CT.

ObjectiveTo assess the feasibility of dual energy CT (DECT) to derive myocardial extracellular volume (ECV) and detect myocardial ECV differences without a non-contrast acquisition, compared to single energy CT (SECT). MethodsSubjects (n = 35) with focal fibrosis (n = 17), diffuse fibrosis (n = 10), and controls (n = 9) underwent non-contrast and delayed acquisitions to calculate SECT-ECV. DECT-ECV was calculated using the delayed acquisition and the derived virtual non-contrast images. In the control and diffuse fibrotic groups, the entire myocardium of the left ventricle was used to calculate ECV. Two ROIs were placed in the focal fibrotic group, one in normal and one in fibrotic myocardium. ResultsMedian ECV was 33.4% (IQR, 30.1-37.4) using SECT and 34.9% (IQR, 31.2-39.2) using DECT (p = 0.401). For both techniques, focal and diffuse fibrosis had significantly higher ECV values (all p < 0.021) than normal myocardium. There was no systematic bias between DECT and SECT (p = 0.348). SECT had a higher radiation dose (1.1 mSv difference) than DECT (p < 0.001). ConclusionECV can be measured using a DECT approach with only a delayed acquisition. The DECT approach provides similar results at a lower radiation dose compared to SECT.

Dual-energy CT-based iodine quantification to differentiate abdominal malignant lymphoma from lymph node metastasis.

PURPOSE: To investigate the value of dual-energy computed tomography (DECT)-derived iodine and fat quantification in differentiating malignant abdominal lymphoma from lymph node metastasis. MATERIALS AND METHODS: In this retrospective study, 59 patients (39 men; mean age, 62.7 years) with histopathologically-confirmed diagnosis of either malignant lymphoma or lymph node metastasis were included. For each lesion, contrast-enhanced attenuation, as well as DECT-derived iodine density and fat fraction measurements were recorded. Mean attenuation and material density values were compared between malignant lymphomas and lymph node metastases. The receiver operating characteristic (ROC) curve analysis was adopted to estimate the optimal threshold for discriminating between both entities. A control group (n = 60) was analyzed for comparison of attenuation and material density values of normal abdominal lymph nodes. RESULTS: Assessment of DECT-derived iodine density and fat fraction values revealed significant differences between lymph node metastases (1.7 ±â€¯0.4 mg/ml and 15.5 ±â€¯7.3%) and malignant lymphomas (2.5 ±â€¯0.5 mg/ml and 26.7 ±â€¯12.2%) as well as normal lymph nodes (2.4 ±â€¯0.8 mg/ml and 24.1 ±â€¯10.8%) (P ≤ 0.013). An iodine concentration of 2.0 mg/ml represented the optimal threshold to discriminate between lymphoma and lymph node metastasis (sensitivity, 87%; specificity, 89%). Moreover, a significant correlation was found between iodine concentration and fat fraction for both lymphomas and lymph node metastases (P = 0.001). CONCLUSION: DECT enables characterization of abdominal masses as derived iodine and fat fraction values differ significantly between malignant abdominal lymphomas and lymph node metastases.