Contrast-enhanced Dual-energy CT Research Articles

Differentiating between benign and malignant breast lesions using dual-energy CT-based model: development and validation

ObjectivesTo develop and validate a dual-energy CT (DECT)-based model for noninvasively differentiating between benign and malignant breast lesions detected on DECT.Materials and methodsThis 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.ResultsThis 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.ConclusionThe DECT-based model showed a favorable diagnostic performance for noninvasive differentiation between benign and malignant breast lesions detected on DECT.Critical relevance statementThe 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.Key PointsIt 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.Graphical

Nomogram based on dual-energy CT-derived extracellular volume fraction for the prediction of microsatellite instability status in gastric cancer

PurposeTo develop and validate a nomogram based on extracellular volume (ECV) fraction derived from dual-energy CT (DECT) for preoperatively predicting microsatellite instability (MSI) status in gastric cancer (GC).Materials and methodsA total of 123 patients with GCs who underwent contrast-enhanced abdominal DECT scans were retrospectively enrolled. Patients were divided into MSI (n=41) and microsatellite stability (MSS, n=82) groups according to postoperative immunohistochemistry staining, then randomly assigned to the training (n=86) and validation cohorts (n=37). We extracted clinicopathological characteristics, CT imaging features, iodine concentrations (ICs), and normalized IC values against the aorta (nICs) in three enhanced phases. The ECV fraction derived from the iodine density map at the equilibrium phase was calculated. Univariate and multivariable logistic regression analyses were used to identify independent risk predictors for MSI status. Then, a nomogram was established, and its performance was evaluated by ROC analysis and Delong test. Its calibration performance and clinical utility were assessed by calibration curve and decision curve analysis, respectively.ResultsThe ECV fraction, tumor location, and Borrmann type were independent predictors of MSI status (all P &amp;lt; 0.05) and were used to establish the nomogram. The nomogram yielded higher AUCs of 0.826 (0.729–0.899) and 0.833 (0.675–0.935) in training and validation cohorts than single variables (P&amp;lt;0.05), with good calibration and clinical utility.ConclusionsThe nomogram based on DECT-derived ECV fraction has the potential as a noninvasive biomarker to predict MSI status in GC patients.

Evaluation of Radiation Dose Effect on Lung Function Using Iodine Maps Derived From Dual-Energy Computed Tomography

PurposeThere is interest in using dual-energy computed tomography (DECT) to evaluate organ function before and after radiotherapy. The purpose of this study (trial identifier: XXXX) is to assess longitudinal changes in lung perfusion using iodine maps derived from DECT in lung cancer patients treated with conventional or stereotactic radiotherapy (RT). MethodsFor 48 prospectively enrolled lung cancer patients, a contrast-enhanced DECT using a dual-source CT simulator was acquired pre-treatment and at 6 and 12 months post-treatment. Pulmonary functions tests (PFT) were obtained at baseline and at 6 and 12 months post-treatment. Iodine maps were extracted from the DECT images using a previously described 2-material decomposition framework. Longitudinal iodine maps were normalized using a reference region defined as all voxels with perfusion in the top 10% outside of the 5 Gy isodose volume. Normalized functional responses (NFR) were calculated for three dose ranges: <5 Gy, 5-20 Gy and >20 Gy. Mixed model analysis was used to assess the correlation between dose metrics and NFR. Pearson correlation was used to assess if NFR are correlated with PFT changes. ResultsOut of the 48 patients, 21 (44%) were treated with stereotactic body radiation therapy (SBRT) and 27 (56%) were treated with conventionally fractionated IMRT. 31 out of these 48 patients were ultimately included in data analysis. It was found that NFR is linearly correlated with dose (p < 0.001) for both groups. The number of months elapsed post-RT is also found to correlate with NFR (p = 0.029), although this correlation was not observed for the SBRT subgroup. The NFR is not found to correlate with PFT changes. ConclusionDECT derived iodine maps are a promising method for detailed anatomic evaluation of radiation effect on lung function, including potentially subclinical changes.

Characterization of tumors of jaw: Additive value of contrast enhancement and dual-energy computed tomography.

Currently, the differentiation of jaw tumors is mainly based on the lesion's morphology rather than the enhancement characteristics, which are important in the differentiation of neoplasms across the body. There is a paucity of literature on the enhancement characteristics of jaw tumors. This is mainly because, even though computed tomography (CT) is used to evaluate these lesions, they are often imaged without intravenous contrast. This study hypothesised that the enhancement characteristics of the solid component of jaw tumors can aid in the differentiation of these lesions in addition to their morphology by dual-energy CT, therefore improving the ability to differentiate between various pathologies. To evaluate the role of contrast enhancement and dual-energy quantitative parameters in CT in the differentiation of jaw tumors. Fifty-seven patients with jaw tumors underwent contrast-enhanced dual-energy CT. Morphological analysis of the tumor, including the enhancing solid component, was done, followed by quantitative analysis of iodine concentration (IC), water concentration (WC), HU, and normalized IC. The study population was divided into four subgroups based on histopathological analysis-central giant cell granuloma (CGCG), ameloblastoma, odontogenic keratocyst (OKC), and other jaw tumors. A one-way ANOVA test for parametric variables and the Kruskal-Wallis test for non-parametric variables were used. If significant differences were found, a series of independent t-tests or Mann-Whitney U tests were used. Ameloblastoma was the most common pathology (n = 20), followed by CGCG (n = 11) and OKC. CGCG showed a higher mean concentration of all quantitative parameters than ameloblastomas (P < 0.05). An IC threshold of 31.35 × 100 μg/cm3 had the maximum sensitivity (81.8%) and specificity (65%). Between ameloblastomas and OKC, the former showed a higher mean concentration of all quantitative parameters (P < 0.001), however when comparing unilocular ameloblastomas with OKCs, the latter showed significantly higher WC. Also, ameloblastoma had a higher IC and lower WC compared to "other jaw tumors" group. Enhancement characteristics of solid components combined with dual-energy parameters offer a more precise way to differentiate between jaw tumors.

Advancing Differentiation of Hepatic Metastases in Malignant Melanoma through Dual-Energy Computed Tomography Rho/Z Maps.

The aim of this study is to evaluate the diagnostic accuracy of dual-energy computed tomography (DECT)-based Rho/Z maps in differentiating between metastases and benign liver lesions in patients diagnosed with malignant melanoma compared to conventional CT value measurements. This retrospective study included 73 patients (mean age, 70 ± 13 years; 43 m/30 w) suffering from malignant melanoma who had undergone third-generation DECT as part of tumor staging between December 2017 and December 2021. For this study, we measured Rho (electron density) and Z (effective atomic number) values as well as Hounsfield units (HUs) in hypodense liver lesions. Values were compared, and diagnostic accuracy for differentiation was computed using receiver operating characteristic (ROC) curve analyses. Additional performed MRI or biopsies served as a standard of reference. A total of 136 lesions (51 metastases, 71 cysts, and 14 hemangiomas) in contrast-enhanced DECT images were evaluated. The most notable discrepancy (p < 0.001) between measured values and the highest diagnostic accuracy for distinguishing melanoma metastases from benign cysts was observed for the Z (0.992; 95% CI, 0.956-1) parameters, followed by Rho (0.908; 95% CI, 0.842-0.953) and finally HU120kV (0.829; 95% CI, 0.751-0.891). Conversely, when discriminating between liver metastases and hemangiomas, the HU120kV parameters showed the most significant difference (p < 0.001) and yielded the highest values for diagnostic accuracy (0.859; 95% CI, 0.740-0.937), followed by the Z parameters (0.790; 95% CI, 0.681-0.876) and finally the Rho values (0.621; 95% CI, 0.501-0.730). Rho and Z measurements derived from DECT allow for improved differentiation of liver metastases and benign liver cysts in patients with malignant melanoma compared to conventional CT value measurements. In contrast, in differentiation between liver hemangiomas and metastases, Rho/Z maps show inferior diagnostic accuracy. Therefore, differentiation between these two lesions remains a challenge for CT imaging.

Radiomics Features Using Dual-energy CT for Lymph Nodes After Preoperative Chemotherapy for Esophageal Cancer.

Progress has been made in a triplet preoperative chemotherapy regimen for advanced esophageal cancer. We performed a preliminary investigation of the radiomics features of pathological lymph node metastasis after neoadjuvant chemotherapy using dual-energy computed tomography (DECT). From January to December 2022, 36 lymph nodes from 10 patients with advanced esophageal cancer who underwent contrast-enhanced DECT after neoadjuvant chemotherapy and radical surgery in our department were studied. Radiomics features were extracted from iodine-based material decomposition images at the portal venous phase constructed by DECT using MATLAB analysis software. Receiver operating characteristic (ROC) analysis and cut-off values were determined for the presence or absence of pathological metastasis. ROC for the short axis of the pathologically positive lymph nodes showed an AUC of 0.713. Long run emphasis (LRE) within gray-level run-length matrix (GLRLM) was confirmed with a high AUC of 0.812. Sensitivity and specificity for lymph nodes with a short axis >10 mm were 0.222 and 1, respectively. Sensitivity and specificity for LRE within GLRLM were 0.722 and 0.833, respectively. Sensitivity and specificity for small zone emphasis (SZE) within gray-level size zone matrix (GLSZM) were 0.889 and 0.667, and zone percentage (ZP) values within GLSZM were 0.722 and 0.778, respectively. Discrimination of existing metastases using radiomics showed significantly higher sensitivity compared to lymph node short axis >10 mm (odds ratios of LRE, SZE, and ZP: 9.1, 28, and 9.1, respectively). Evaluation of radiomics analysis using DECT may enable a more detailed evaluation of lymph node metastasis after neoadjuvant chemotherapy.

Dual-energy computed tomography for evaluating nodal staging in lung adenocarcinoma: correlation with surgical pathology.

To ascertain the performance of dual-energy CT (DECT) with iodine quantification in differentiating malignant mediastinal and hilar lymph nodes (LNs) from benign ones, focusing on patients with lung adenocarcinoma. In this study, patients with suspected lung cancer received a preoperative contrast-enhanced DECT scan from Jun 2018 to Dec 2020. Quantitative DECT parameters and the size were compared between metastatic and benign LNs. Their diagnostic performances were analyzed by the ROC curves and compared by using the two-sample t test. 72 patients (23 men, 49 women; mean age 62.5 ± 10.1 years) fulfilled the inclusion criteria. A total of 98 LNs (67 benign, 31 metastatic) were analyzed. The iodine concentration normalized by muscle (NICmuscle) was significantly higher (P < 0.001) in metastatic LNs (4.79 ± 1.70) than in benign ones (3.00 ± 1.45). The optimal threshold of NICmuscle was 3.44, which yielded AUC: 0.798, sensitivity: 83.9%, specificity: 73.1%, accuracy: 76.5%, respectively. Applying the established size parameters with 10mm as the threshold yielded AUC: 0.600, sensitivity: 29.0%, specificity: 91.0%, accuracy: 71.4%, respectively. The diagnostic performance of NICmuscle was significantly better (P = 0.007) than the performance obtained using the established size parameters. For lung adenocarcinoma, the quantitative measurement of NICmuscle derived from DECT is useful for differentiating benign and metastatic mediastinal and hilar LNs before surgical intervention.

Quantifying iodine concentration in the normal bowel wall using dual-energy CT: influence of patient and contrast characteristics

This study aimed to establish quantitative references of the normal bowel wall iodine concentration (BWIC) using dual energy CT (DECT). This single-center retrospective study included 248 patients with no history of gastrointestinal disease who underwent abdominal contrast-enhanced DECT between January and April 2022. The BWIC was normalized by the iodine concentration of upper abdominal organs (BWICorgan,) and the iodine concentration (IC) of the aorta (BWICaorta). BWIC decreased from the stomach to the rectum (mean 2.16 ± 0.63 vs. 2.19 ± 0.63 vs. 2.1 ± 0.58 vs. 1.67 ± 0.47 vs. 1.31 ± 0.4 vs. 1.18 ± 0.34 vs. 0.94 ± 0.26 mgI/mL for the stomach, duodenum, jejunum, ileum, right colon, left colon and rectum, respectively; P < 0.001). By multivariate analysis, BWIC was associated with a higher BMI (OR:1.01, 95% CI 1.00–1.02, P < 0.001) and with a higher injected contrast dose (OR: 1.51; 95% CI 1.36–1.66, P < 0.001 and 2.06; 95% CI 1.88–2.26, P < 0.001 for 500 mgI/kg and 600 mgI/kg doses taking 400 mgI/kg dose as reference). The BWICorgan was shown independent from patients and contrast-related variables while the BWICaorta was not. BWIC varies according to bowel segments and is dependent on the total iodine dose injected. It shall be normalized with the IC of the upper abdominal organs.

Virtual Noncontrast Reconstructions on Contrast-Enhanced Dual-Energy CT for the Differentiation of Vertebral Venous Congestion and Sclerotic Metastasis.

Virtual Noncontrast Reconstructions on Contrast-Enhanced Dual-Energy CT for the Differentiation of Vertebral Venous Congestion and Sclerotic Metastasis.

Evaluation of virtual monochromatic imaging with dual-energy computed tomography of small liver metastases from malignant abdominal tumours: Quantitative and qualitative analyses

Background The assessment of small metastatic liver tumours using dual-energy computed tomography (DECT) has not been fully established. Purpose To assess the effect of low-keV virtual monochromatic imaging (VMI) with non-contrast and contrast-enhanced DECT on the qualitative and quantitative image parameters of small liver metastases. Material and methods Two radiologists retrospectively evaluated 92 metastatic liver tumours (5–20 mm) in 32 patients. Non-contrast and contrast-enhanced VMI were reconstructed at seven energy levels (40–100 keV) with 10-keV intervals. Lesion boundary, lesion delineation, image noise, and overall image quality were evaluated using the visual analogue scale. A high subjective score indicates good overall image quality, clear nodal boundaries and delineation, and less noticeable image noise. Subjective scores were compared using the Kruskal–Wallis test. A quantitative analysis involving the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) was performed. Results The lesion boundary was highest at 40 keV and significantly improved during the non-contrast portal venous phase compared to that at higher keV ( p &lt; .005). The lesion delineation score was significantly higher at 40 keV and tended to decrease at higher keV. Image noise and overall image quality were rated low at low keV; however, those at 80, 90, and 100 keV were rated the highest ( p &lt; .005). The CNR and SNR were highest for non-contrast CT at 100 keV. During the portal venous phase, no significant differences were observed in CNR and SNR at each keV. Conclusion Low-keV imaging using non-contrast and contrast-enhanced DECT is useful for delineating small hepatic metastatic tumours.

Detection and characterization of urinary stones using material-specific images derived from contrast-enhanced dual-energy CT urography.

To determine the accuracy of material-specific images derived from contrast-enhanced dual-energy CT urography (DECTU) in detecting and measuring urinary stones in comparison with that of unenhanced images and its utility in calcified stone differentiation. 105 patients with 202 urinary stones (121 had confirmed composition by infrared spectroscopy) underwent triphasic (unenhanced, portal venous (VP) and excretory phase (EP)) DECTU. Material-specific images were derived in VP and EP with calcium-water, calcium-iodine and CaOxalate_Dihydrate (COD)-Hydroxyapatite (HAP) as basis material pairs. Stone number and size were recorded on unenhanced images and VP and EP material-specific images, where stone densities were also measured. Material densities of calcified stones (pure calcium oxalate [pCaO, n = 34], mixed calcium oxalate [mCaO, n = 14], mixed carbonate phosphate [mCaP, n = 70]) were compared and thresholds for differentiating these stones were determined using receiver operating characteristic analysis. All 202 urinary stones were detected on the unenhanced, calcium (water) and calcium (iodine) images in VP. While the detection rate was significantly decreased to 58 and 64% using calcium (water) and calcium (iodine) images in EP, respectively (all p < 0.001). Stone sizes measured on calcium (iodine) images in VP was similar to that of unenhanced images (10.6 vs 10.7 mm, p > 0.05). Significant differences in material densities were found among pCaO, mCaO and mCaP on COD(HAP) images with AUC of 0.72-0.74 for differentiating these stones. Material-specific images in VP derived from DECTU allow reliably detecting and measuring urinary tract stones in comparison with unenhanced images and can identify calcified stones with moderate diagnostic performance to provide potential 33% dose reduction. Material-specific images, especially the calcium (iodine) images in VP allow for reliable detection of urinary stones.Stone size measurement should be performed on the calcium (iodine) images in VP.Material density measurements on COD-HAP (VP) material decomposition images can be used to differentiate among pure calcium oxalate, mixed calcium oxalate and mixed carbonate phosphate stones with AUC of 0.72-0.74.

Dual-energy CT improves differentiation of non-hypervascular pancreatic neuroendocrine neoplasms from CA 19-9-negative pancreatic ductal adenocarcinomas.

To evaluate the utility of dual-energy CT (DECT) in differentiating non-hypervascular pancreatic neuroendocrine neoplasms (PNENs) from pancreatic ductal adenocarcinomas (PDACs) with negative carbohydrate antigen 19-9 (CA 19-9). This retrospective study included 26 and 39 patients with pathologically confirmed non-hypervascular PNENs and CA 19-9-negative PDACs, respectively, who underwent contrast-enhanced DECT before treatment between June 2019 and December 2021. The clinical, conventional CT qualitative, conventional CT quantitative, and DECT quantitative parameters of the two groups were compared using univariate analysis and selected by least absolute shrinkage and selection operator regression (LASSO) analysis. Multivariate logistic regression analyses were performed to build qualitative, conventional CT quantitative, DECT quantitative, and comprehensive models. The areas under the receiver operating characteristic curve (AUCs) of the models were compared using DeLong's test. The AUCs of the DECT quantitative (based on normalized iodine concentrations [nICs] in the arterial and portal venous phases: 0.918; 95% confidence interval [CI] 0.852-0.985) and comprehensive (based on tumour location and nICs in the arterial and portal venous phases: 0.966; 95% CI 0.889-0.995) models were higher than those of the qualitative (based on tumour location: 0.782; 95% CI 0.665-0.899) and conventional CT quantitative (based on normalized conventional CT attenuation in the arterial phase: 0.665; 95% CI 0.533-0.797; all P < 0.05) models. The DECT quantitative and comprehensive models had comparable performances (P = 0.076). Higher nICs in the arterial and portal venous phases were associated with higher blood supply improving the identification of non-hypervascular PNENs.

Insights about cervical lymph nodes: Evaluating deep learning–based reconstruction for head and neck computed tomography scan

PurposeThis study aimed to investigate differences in cervical lymph node image quality on dual-energy computed tomography (CT) scan with datasets reconstructed using filter back projection (FBP), hybrid iterative reconstruction (IR), and deep learning–based image reconstruction (DLIR) in patients with head and neck cancer. MethodSeventy patients with head and neck cancer underwent follow-up contrast-enhanced dual-energy CT examinations. All datasets were reconstructed using FBP, hybrid IR with 30 % adaptive statistical IR (ASiR-V), and DLIR with three selectable levels (low, medium, and high) at 2.5- and 0.625-mm slice thicknesses. Herein, signal, image noise, signal-to-noise ratio, and contrast-to-noise ratio of lymph nodes and overall image quality, artifact, and noise of selected regions of interest were evaluated by two radiologists. Next, cervical lymph node sharpness was evaluated using full width at half maximum. ResultsDLIR exhibited significantly reduced noise, ranging from 3.8 % to 35.9 % with improved signal-to-noise ratio (11.5–105.6 %) and contrast-to-noise ratio (10.5–107.5 %) compared with FBP and ASiR-V, for cervical lymph nodes (p < 0.001). Further, 0.625-mm-thick images reconstructed using DLIR-medium and DLIR-high had a lower noise than 2.5-mm-thick images reconstructed using FBP and ASiR-V. The lymph node margins and vessels on DLIR-medium and DLIR-high were sharper than those on FBP and ASiR-V (p < 0.05). Both readers agreed that DLIR had a better image quality than the conventional reconstruction algorithms. ConclusionDLIR-medium and -high provided superior cervical lymph node image quality in head and neck CT. Improved image quality affords thin-slice DLIR images for dose-reduction protocols in the future.

An Efficient Transformer Model for Synthesizing Dual Energy CT from Single Energy Scanner

Dual-energy CT can be used to optimize radiation treatment. Recently, deep learning has been demonstrated to synthesize high-energy CT images from low-energy ones for dose reduction and lower CT system burden. As the state-of-the-art deep learning architecture, the computation burden of Transformer increases quadratically with the feature size, making the model training resource-demanding or even infeasible. Here, we introduce an efficient transformer for the balance between CT image synthesis quality and computational burden. The model is a U-shape deep neural network with encoders and decoders built by Transformer blocks. The model input is low-energy 100kVp CT image and the output is high-energy 140kVp one. Each block has a Self Channel Correlation Unit (SCCU) and a Self Spatial Attention Unit (SSAU). Local shortcuts are applied for both units. Under-sampling operation achieved by pixel shuffling is used to obtain multi-scale feature maps, and the transformer block is applied on each feature scale. Symmetric skip connection sending features from shallow layers to deep layers, thus an additional 1 × 1 convolution is used for feature fusion in each decoder. In a SCCU, the feature is first mapped to one Query, one Key, and one Value. Then the Query and the Key tensors perform matrix multiplication to compute cross covariance of feature channels. The channel correlation score can be obtained by normalization of the covariance, and it is used to weight the Value tensor. As a result, the model complexity only increases linearly with the feature size. Besides the channel weighting, we enhance spatial information using SSAU, where the feature is mapped to two tensors. One tensor after activation is used to point-wisely calibrate another tensor. Additional Transformer blocks are cascaded to the last decoder for feature refinement. Because of the structure similarity of low- and high-energy CT images, a global shortcut is used to ease model training. Clinical iodine contrast-enhanced dual energy CT image datasets of 19 patients are used in this study. The dual-energy scanning is performed by a SOMATOM Definition Flash DECT scanner. We split the datasets into training dataset of 15 patients, validation dataset of 1 patient, and testing dataset of 3 patients. The image size is 512 × 512 with pixel size 0.5 × 0.5 mm2. The U-Net model with 1.95M parameters and 44.87G FLOPS achieved the averaged PSNR value of 44.55 dB (s.t.d. 1.34) and averaged RMSE value of 0.0060 (s.t.d. 0.001). In comparison, our efficient Transformer with 1.408M parameters and 31.375G FLOPS achieved the averaged PSNR value of 44.78 dB (s.t.d. 1.37) and RMSE value of 0.0059 (s.t.d. 0.001), demonstrating our model has better performance with small model size and less computation. The efficient Transformer model allows high-resolution CT image synthesis with small model scale and computation burden from low-energy CT image.

Deep learning-based iodine contrast-augmenting algorithm for low-contrast-dose liver CT to assess hypovascular hepatic metastasis.

To investigate the image quality and diagnostic performance of low-contrast-dose liver CT using a deep learning-based iodine contrast-augmenting algorithm (DLICA) for hypovascular hepatic metastases. This retrospective study included 128 patients who underwent contrast-enhanced dual-energy CT for hepatic metastasis surveillance between July 2019 and June 2022 using a 30% reduced iodine contrast dose in the portal phase. Three image types were reconstructed: 50-keV virtual monoenergetic images (50-keV VMI); linearly blended images simulating 120-kVp images (120-kVp); and post-processed 120-kVp images using DLICA (DLICA 120-kVp). Three reviewers evaluated lesion conspicuity, image contrast, and subjective image noise. We also measured image noise, contrast-to-noise ratios (CNRs), and signal-to-noise ratios (SNRs). The diagnostic performance for hepatic metastases was evaluated using a jackknife alternative free-response receiver operating characteristic method with the consensus of two independent radiologists as the reference standard. DLICA 120-kVp demonstrated significantly higher CNR of lesions to liver (5.7±3.1 vs. 3.8±2.1 vs. 3.8±2.1) and higher SNR compared with 50-keV VMI and 120-kVp (p<0.001 for all). DLICA 120-kVp had significantly lower image noise than 50-kVp VMI for all regions (p<0.001 for all). DLICA 120-kVp also exhibited superior lesion conspicuity (4.0 [3.3-4.3] vs. 3.7 [3.0-4.0] vs. 3.7 [3.0-4.0]), higher image contrast, and lower subjective image noise compared with 50-keV VMI and 120-kVp (p<0.001 for all). Although there was no significant difference in the figure of merit for lesion diagnosis among the three methods (p=0.11), DLICA 120-kVp had a significantly higher figure of merit for lesions with a diameter <20 mm than 50-keV VMI (0.677 vs. 0.648, p=0.007). On a per-lesion basis, DLICA 120-kVp also demonstrated higher sensitivity than the 50-keV VMI (81.2% vs. 72.9%, p<0.001). The specificities per lesion were not significantly different among the three algorithms (p=0.15). DLICA at 120-kVp provided superior lesion conspicuity and image quality and similar diagnostic performance for hypovascular hepatic metastases compared with 50-keV VMI.

Dual-Energy CT Deep Learning Radiomics to Predict Macrotrabecular-Massive Hepatocellular Carcinoma.

Background It is unknown whether the additional information provided by multiparametric dual-energy CT (DECT) could improve the noninvasive diagnosis of the aggressive macrotrabecular-massive (MTM) subtype of hepatocellular carcinoma (HCC). Purpose To evaluate the diagnostic performance of dual-phase contrast-enhanced multiparametric DECT for predicting MTM HCC. Materials and Methods Patients with histopathologic examination-confirmed HCC who underwent contrast-enhanced DECT between June 2019 and June 2022 were retrospectively recruited from three independent centers (center 1, training and internal test data set; centers 2 and 3, external test data set). Radiologic features were visually analyzed and combined with clinical information to establish a clinical-radiologic model. Deep learning (DL) radiomics models were based on DL features and handcrafted features extracted from virtual monoenergetic images and material composition images on dual phase using binary least absolute shrinkage and selection operators. A DL radiomics nomogram was developed using multivariable logistic regression analysis. Model performance was evaluated with the area under the receiver operating characteristic curve (AUC), and the log-rank test was used to analyze recurrence-free survival. Results A total of 262 patients were included (mean age, 54 years ± 12 [SD]; 225 men [86%]; training data set, n = 146 [56%]; internal test data set, n = 35 [13%]; external test data set, n = 81 [31%]). The DL radiomics nomogram better predicted MTM than the clinical-radiologic model (AUC = 0.91 vs 0.77, respectively, for the training set [P < .001], 0.87 vs 0.72 for the internal test data set [P = .04], and 0.89 vs 0.79 for the external test data set [P = .02]), with similar sensitivity (80% vs 87%, respectively; P = .63) and higher specificity (90% vs 63%; P < .001) in the external test data set. The predicted positive MTM groups based on the DL radiomics nomogram had shorter recurrence-free survival than predicted negative MTM groups in all three data sets (training data set, P = .04; internal test data set, P = .01; and external test data set, P = .03). Conclusion A DL radiomics nomogram derived from multiparametric DECT accurately predicted the MTM subtype in patients with HCC. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Chu and Fishman in this issue.

Survival prediction of hepatocellular carcinoma by measuring the extracellular volume fraction with single-phase contrast-enhanced dual-energy CT imaging.

This study aimed to investigate the value of quantified extracellular volume fraction (fECV) derived from dual-energy CT (DECT) for predicting the survival outcomes of patients with hepatocellular carcinoma (HCC) after transarterial chemoembolization (TACE). A total of 63 patients with HCC who underwent DECT before treatment were retrospectively included. Virtual monochromatic images (VMI) (70 keV) and iodine density images (IDI) during the equilibrium phase (EP) were generated. The tumor VMI-fECV and IDI-fECV were measured and calculated on the whole tumor (Whole) and maximum enhancement of the tumor (Maximum), respectively. Univariate and multivariate Cox models were used to evaluate the effects of clinical and imaging predictors on overall survival (OS) and progression-free survival (PFS). The correlation between tumor VMI-fECV and IDI-fECV was strong (both p< 0.001). The Bland-Altman plot between VMI-fECV and IDI-fECV showed a bias of 5.16% for the Whole and 6.89% for the Maximum modalities, respectively. Increasing tumor VMI-fECV and IDI-fECV were positively related to the effects on OS and PFS (both p< 0.05). The tumor IDI-fECV-Maximum was the only congruent independent predictor in patients with HCC after TACE in the multivariate analysis on OS (p = 0.000) and PFS (p = 0.028). Patients with higher IDI-fECV-Maximum values had better survival rates above the optimal cutoff values, which were 35.42% for OS and 29.37% for PFS. The quantified fECV determined by the equilibrium-phase contrast-enhanced DECT can potentially predict the survival outcomes of patients with HCC following TACE treatment.

Dual-energy computed tomography for improved visualization of internal jugular chain neck lymph node metastasis and nodal necrosis in head and neck squamous cell carcinoma.

To evaluate and compare the utility of 40-keV virtual monochromatic imaging (VMI) reconstructed from dual-energy computed tomography (DECT) in the assessment of neck lymph node metastasis with 70-keV VMI, which is reportedly equivalent to conventional 120-kVp single-energy computed tomography. Patients with head and neck squamous cell carcinoma who had neck lymph node metastasis in contact with the sternocleidomastoid muscle (SCM) and underwent contrast-enhanced DECT were included. In 40- and 70-keV VMI, contrast differences and contrast noise ratio (CNR) between the solid component of neck lymph node metastasis (SC) and the SCM and between SC and nodal necrosis (NN) were calculated. Two board-certified radiologists independently and qualitatively evaluated the boundary discrimination between SC and SCM and the diagnostic certainty of NN. We evaluated 45 neck lymph node metastases. The contrast difference between SC and SCM and SC and NN were significantly higher at 40-keV VMI than at 70-keV VMI (p < 0.001). The CNR between SC and SCM was significantly higher at 40-keV VMI than at 70-keV VMI (p < 0.001). Scoring of the boundary discrimination between SC and SCM as well as the diagnostic certainty of NN at 40-keV VMI was significantly higher than that at 70-keV VMI (p < 0.001). The inter-rater agreements for these scores were higher at 40-keV VMI than at 70-keV VMI. Additional employing 40-keV VMI in routine clinical practice may be useful in the diagnosis of head and neck lymph node metastases and nodal necrosis.

Dual-energy CT of the pancreas: comparison between virtual non-contrast images and true non-contrast images in the detection of pancreatic lesion.

To evaluate the image quality and diagnostic performance for pancreatic lesion between true non-contrast (TNC) and virtual non-contrast (VNC) images obtained from the dual-energy computed tomography (DECT). One hundred six patients with pancreatic mass underwent contrast-enhanced DECT examinations were retrospectively included in this study. VNC images of the abdomen were generated from late arterial (aVNC) and portal (pVNC) phases. For quantitative analysis, the attenuation differences and reproducibility of abdominal organs were compared between TNC and aVNC/pVNC measurements. Qualitatively image quality was assessed by two radiologists using a five-point scale, and they independently compared the detection accuracy of pancreatic lesions between TNC and aVNC/pVNC images. The volume CT dose index (CTDIvol) and size-specific dose estimates (SSDE) were recorded to evaluate the potential dose reduction when using VNC reconstruction to replace the unenhanced phase. A total of 78.38% (765/976) of the attenuation measurement pairs were reproducible between TNC and aVNC images, and 71.0% (693/976) between TNC and pVNC images. In triphasic examinations, a total of 108 pancreatic lesions were found in 106 patients, and no significant difference in detection accuracy was found between TNC and VNC images (p = 0.587-0.957). Qualitatively, image quality was rated diagnostic (score ≥ 3) in all the VNC images. Calculated CTDIvol and SSDE reduction of about 34% could be achieved by omitting the non-contrast phase. VNC images of DECT provide diagnostic image quality and accurate pancreatic lesions detection, which are a promising alternative to unenhanced phase with a substantial reduction of radiation exposure in clinical routine.

Diagnostic Performance of Contrast-Enhanced Dual-Energy CT Metrics for Differentiating Adrenal Adenomas From Nonadenomas Can Be Affected by Inclusion Criteria.

Diagnostic Performance of Contrast-Enhanced Dual-Energy CT Metrics for Differentiating Adrenal Adenomas From Nonadenomas Can Be Affected by Inclusion CriteriaYasunori Nagayama, MD, PhD1 and Toshinori Hirai, MD, PhD2Audio Available | Share