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BACKGROUND: Global shortages of iodinated contrast media (ICM) during COVID-19 pandemic forced the imaging community to use ICM more strategically in CT exams. PURPOSE: The purpose of this work is to provide a quantitative framework for preserving iodine CNR while reducing ICM dosage by either lowering kV in single-energy CT (SECT) or using lower energy virtual monochromatic images (VMI) from dual-energy CT (DECT) in a phantom study. MATERIALS AND METHODS: In SECT study, phantoms with effective diameters of 9.7, 15.9, 21.1, and 28.5 cm were scanned on SECT scanners of two different manufacturers at a range of tube voltages. Statistical based iterative reconstruction and deep learning reconstruction were used. In DECT study, phantoms with effective diameters of 20, 29.5, 34.6, and 39.7 cm were scanned on DECT scanners from three different manufacturers. VMIs were created from 40 to 140 keV. ICM reduction by lowering kV levels for SECT or switching from SECT to DECT was calculated based on the linear relationship between iodine CNR and its concentration under different scanning conditions. RESULTS: On SECT scanner A, while matching CNR at 120 kV, ICM reductions of 21%, 58%, and 72% were achieved at 100, 80, and 70 kV, respectively. On SECT scanner B, 27% and 80% ICM reduction was obtained at 80 and 100 kV. On the Fast-kV switch DECT, with CNR matched at 120 kV, ICM reductions were 35%, 30%, 23%, and 15% with VMIs at 40, 50, 60, and 68 keV, respectively. On the dual-source DECT, ICM reductions were 52%, 48%, 42%, 33%, and 22% with VMIs at 40, 50, 60, 70, and 80 keV. On the dual-layer DECT, ICM reductions were 74%, 62%, 45%, and 22% with VMIs at 40, 50, 60, and 70 keV. CONCLUSIONS: Our work provided a quantitative baseline for other institutions to further optimize their scanning protocols to reduce the use of ICM.

Objectives: To perform a multi-reader comparison of multiparametric dual-energy computed tomography (DECT) images reconstructed with deep-learning image reconstruction (DLIR) and standard-of-care adaptive statistical iterative reconstruction-V (ASIR-V). Methods: This retrospective study included 100 patients undergoing portal venous phase abdominal CT on a rapid kVp switching DECT scanner. Six reconstructed DECT sets (ASIR-V and DLIR, each at three strengths) were generated. Each DECT set included 65 keV monoenergetic, iodine, and virtual unenhanced (VUE) images. Using a Likert scale, three radiologists performed qualitative assessments for image noise, contrast, small structure visibility, sharpness, artifact, and image preference. Quantitative assessment was performed by measuring attenuation, image noise, and contrast-to-noise ratios (CNR). For the qualitative analysis, Gwet's AC2 estimates were used to assess agreement. Results: DECT images reconstructed with DLIR yielded better qualitative scores than ASIR-V images except for artifacts, where both groups were comparable. DLIR-H images were rated higher than other reconstructions on all parameters (p-value < 0.05). On quantitative analysis, there was no significant difference in the attenuation values between ASIR-V and DLIR groups. DLIR images had higher CNR values for the liver and portal vein, and lower image noise, compared to ASIR-V images (p-value < 0.05). The subgroup analysis of patients with large body habitus (weight ≥ 90 kg) showed similar results to the study population. Inter-reader agreement was good-to-very good overall. Conclusion: Multiparametric post-processed DECT datasets reconstructed with DLIR were preferred over ASIR-V images with DLIR-H yielding the highest image quality scores. Clinical relevance statement: Deep-learning image reconstruction in dual-energy CT demonstrated significant benefits in qualitative and quantitative image metrics compared to adaptive statistical iterative reconstruction-V. Key Points: Dual-energy CT (DECT) images reconstructed using deep-learning image reconstruction (DLIR) showed superior qualitative scores compared to adaptive statistical iterative reconstruction-V (ASIR-V) reconstructed images, except for artifacts where both reconstructions were rated comparable. While there was no significant difference in attenuation values between ASIR-V and DLIR groups, DLIR images showed higher contrast-to-noise ratios (CNR) for liver and portal vein, and lower image noise (p value < 0.05). Subgroup analysis of patients with large body habitus (weight ≥ 90 kg) yielded similar findings to the overall study population. [ABSTRACT FROM AUTHOR]

Purpose: Evaluating sacral fractures is crucial in fragility fractures of the pelvis. Dual-energy CT (DECT) is considered useful for diagnosing unclear fractures on single-energy CT (SECT). This study aims to investigate the effectiveness of DECT in diagnosing sacral fractures. Methods: Thirty cases with suspected sacral fractures underwent SECT, DECT, and MRI. The exams were evaluated by two groups: three inexperienced surgeons (Group I) and three experienced surgeons (Group E). Diagnoses were made initially using SECT (pre-DECT) and then reassessed including DECT (post-DECT). This process was repeated twice. Presence of fractures was determined based on MRI. Sensitivity, specificity, inter-rater and intra-rater reliability, and diagnostic accuracy were calculated. Diagnostic accuracy was statistically compared between two groups. Results: Sensitivity was 0.73 in pre-DECT and 0.9 in post-DECT, while specificity was 0.83 in pre-DECT and 0.91 in post-DECT. Sensitivity significantly improved with the addition of DECT (McNemar test: p < 0.001). Intra-rater reliability (Fleiss’ kappa coefficient) was 0.44 in pre-DECT and 0.76 in post-DECT. Inter-rater reliability (Cohen’s kappa coefficient) was 0.6 in pre-DECT and 0.81 in post-DECT. Diagnostic accuracy was significantly lower in group I than group E in pre-DECT (P = 0.019, 0.048), but there was no significant difference between two groups in post-DECT. Conclusion: Combined use of DECT with SECT improved the detection rate of sacral fractures and enhanced intra-rater and inter-rater reliability. High diagnostic accuracy was achieved regardless of the observer’s experience. These results indicate that DECT is a useful imaging modality for diagnosing sacral fractures. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to quantitatively assess the diagnostic value of bone marrow edema (BME) detection on virtual non-calcium (VNCa) images calculated from dual-energy CT (DECT) in people with diabetes mellitus and suspected Charcot neuro-osteoarthropathy (CN). Materials and Methods: People with diabetes mellitus and suspected CN who underwent DECT of the feet (80kVp/Sn150kVp) were included retrospectively. Two blinded observers independently measured CT values on VNCa images using circular regions of interest in five locations in the midfoot (cuneiforms, cuboid and navicular) and the calcaneus of the contralateral or (if one foot was available) the ipsilateral foot. Two clinical groups were formed, one with active CN and one without active CN (no-CN), based on the clinical diagnosis. Results: Thirty-two people with diabetes mellitus and suspected CN were included. Eleven had clinically active CN. The mean CT value in the midfoot was significantly higher in the CN group (-55.6 ± 18.7 HU) compared to the no-CN group (-94.4 ± 23.5 HU; p < 0.001). In the CN group, the difference in CT value between the midfoot and calcaneus was statistically significant (p = 0.003); this was not the case in the no-CN group (p = 0.357). The overall observer agreement was good for the midfoot (ICC = 0.804) and moderate for the calcaneus (ICC = 0.712). Sensitivity was 100.0% and specificity was 71.4% using a cutoff value of -87.6 HU. Conclusion: The detection of BME on VNCa images has a potential value in people with diabetes mellitus and suspected active CN. [ABSTRACT FROM AUTHOR]

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This study aimed to optimize CT esophagography by identifying effective oral contrast dilution ratios and exploring the advantages of dual-energy CT (DECT) over conventional CT for improving image quality. Ex vivo experiments using iodine contrast agents (320–400 mgI/mL) at 21 dilution ratios were scanned at three voltages, with additional dual-energy scans generating various reconstruction images. Image quality was assessed both objectively and subjectively. The study found significant variability in image quality across different dilution ratios. Specific dilution ratios that produced image quality comparable to the control group (a commercial oral contrast agent) and those meeting the standards for clinical diagnosis and high-quality images were identified based on image quality assessments. Recommendations for preparing 100 mL of oral contrast solution were provided, such as for achieving high-quality images at a scanning voltage of 100 kVp: the optimal dilution ratios are 1:6 to 1:19 for 320 mgI/mL, and 1:8 to 1:19 for 350 to 400 mgI/mL. Additionally, beam-hardening artifacts were significantly reduced in DECT images. These findings provide valuable guidance for improving CT esophagography protocols. [ABSTRACT FROM AUTHOR]

Objective: To investigate the effect of radiomics models obtained from dual-energy CT (DECT) material decomposition images and virtual monoenergetic images (VMIs) in predicting the pathological grading of bladder urothelial carcinoma (BUC). Materials and methods: A retrospective analysis of preoperative DECT examination was conducted on 112 patients diagnosed with BUC. This cohort included 76 cases of high-grade urothelial carcinoma and 36 cases of low-grade urothelial carcinoma. DECT can provide material decomposition images of venous phase Iodine maps and Water maps based on the differences in attenuation of substances, as well as VMIs at 40 to 140 keV (interval 10 keV). A total of 13 image sets were obtained, and radiomics features were extracted and analyzed from each set to achieve preoperative prediction of BUC. The best features related to BUC were identified by recursive feature elimination (RFE), the Minimum Redundancy Maximum Relevance (mRMR), and the Least Absolute Shrinkage and Selection Operator (LASSO) in order. A five-fold cross-validation method was used to divide the samples into training and testing sets, and models for pathological prediction of BUC grading were constructed by a random forest (RF) classifier. Receiver operating curves (ROC) were plotted to evaluate the performance of 13 models obtained from each image set. Results: Despite the notable differences in the best radiomics features chosen from each image set, all the features selected from 40 to 100 keV VMIs included the Dependence Variance of the GLDM feature set. There were no statistically significant differences in the area under the curve (AUC) between the training set and the testing set for all 13 models. In the testing set, the AUCs of the models established through 40 keV to 140 keV (interval of 10 keV) image sets were 0.895, 0.874, 0.855, 0.889, 0.841, 0.868, 0.852, 0.847, 0.889, 0.887 and 0.863 respectively. The AUCs for the models established using the Iodine maps and Water maps image sets were 0.873 and 0.852, respectively. Conclusion: Despite the differences in the selected radiomic features from DECT multi-parameter images, the performance of radiomics models in predicting the pathological grading of BUC was not affected by the variations in the types of images used for model training. [ABSTRACT FROM AUTHOR]

To explore the value of splenic hemodynamic parameters from low-dose one-stop dual-energy and perfusion CT (LD-DE&PCT) in non-invasively predicting high-risk esophageal varices (HREV) in cirrhotic patients. We retrospectively analyzed cirrhotic patients diagnosed with esophageal varices (EV) through clinical, laboratory, imaging, and endoscopic examinations from September 2021 to December 2023 in our hospital. All patients underwent LD-DE&PCT to acquire splenic iodine concentration and perfusion parameters. Radiation dose was recorded. Patients were classified into non-HREV and HREV groups based on endoscopy. Univariate and multivariate logistic regression analysis were performed, and the prediction model for HREV was constructed. P < 0.05 was considered statistically significant. Univariate analysis revealed that significant differences were found in portal iodine concentration (PIC), blood flow (BF), permeability surface (PS), spleen volume (V-S), total iodine concentration (TIC), and total blood volume of the spleen (BV-S) between groups. TIC demonstrated the highest predictive value with an area under the curve (AUC) value of 0.87. Multivariate analysis showed that PIC, PS, and BV-S were independent risk factors for HREV. The logistic regression model for predicting HREV had an AUC of 0.93. The total radiation dose was 20.66 ± 4.07 mSv. Splenic hemodynamic parameters obtained from LD-DE&PCT can non-invasively and accurately assess the hemodynamic status of the spleen in cirrhotic patients with EV and predict the occurrence of HREV. Despite the retrospective study design and limited sample size of this study, these findings deserve further validation through prospective studies with larger cohorts. [ABSTRACT FROM AUTHOR]

Purpose: Periprosthetic joint infection (PJI) is a severe complication following joint replacement surgery, and there is a current lack of rapid, accurate, and non-invasive diagnostic methods. This study aims to assess the effectiveness of using dual-energy computed tomography (DECT) to quantify the maximum long and short axes of the external iliac lymph nodes (LNs) as a diagnostic tool for PJI. Patients and Methods: In this prospective controlled study, 112 patients undergoing total hip or total knee revision surgery were enrolled. All patients underwent preoperative DECT scanning to measure the maximum long and short axes of the external iliac LNs on the affected side. The diagnostic value of lymph node size for PJI was assessed using receiver operating characteristic curves. Results: The AUC (Area Under the Curve) quantifies the diagnostic method's ability to distinguish between positive and negative outcomes in this study. A value closer to 1.0 indicates better discriminatory performance. Compared to erythrocyte sedimentation rate (Area Under the Curve (AUC) = 0.834), percentage of polymorphonuclear leukocytes (AUC = 0.621), and C-reactive protein (AUC = 0.765), the maximum long axis (AUC =0.895) and maximum short axis (AUC = 0.858) of the external iliac LNs better differentiated PJI from aseptic failure. For diagnosing PJI, the threshold for the maximum long axis of the LNs was 20.5 mm, with a sensitivity of 84.21% and a specificity of 87.84%. For the maximum short axis, the threshold was 8.5 mm, with a sensitivity of 89.47% and a specificity of 82.43%. Combining the maximum long and short axes of the external iliac LNs enhanced the diagnostic accuracy for PJI. Conclusion: Measuring the long and short axes of external iliac LNs using DECT is an effective diagnostic approach for PJI, aiding in the differentiation between PJI and aseptic failure following joint replacement surgery. [ABSTRACT FROM AUTHOR]

Prostate lesions are common in middle-aged and elderly men. Dual-energy CT (DECT) can capture projection data at high and low X-ray energies in a single scan, significantly enhancing its ability to differentiate substances compared with traditional CT. DECT not only provides routine anatomical information of lesions, tissues, and organs, but also generates virtual single-energy spectral images, virtual decalcification images, effective atomic number diagrams, iodine maps, electron cloud density maps, and other energy spectral images through post-processing. Additionally, DECT derives relevant quantitative parameters such as iodine concentration, standardized iodine concentration, and slope of the energy spectrum decay curve, aiding in the functional evaluation of diseased tissues. This capability is crucial for the early detection and qualitative and quantitative evaluation of prostate lesions. Numerous studies have shown that DECT has great potential for the differential diagnosis of prostate lesions, preoperative staging, and proton therapy. This article reviews the research and application advancements of DECT in prostate lesions and explores future research directions.

Objective: To investigate the feasibility of dual-energy computed tomography (DECT) in foot arteriography for small vessel image quality in patients with diabetes . Methods: A total of 30 patients with diabetes were enrolled prospectively between January and November 2022 in the radiology department of our hospital, where DECT was acquired immediately after the CT angiography (routine CTA group) of the lower extremity.The optimal virtual monochromatic images (DECT group) were derived from the DECT data. Image quality was assessed by measuring the CT values and noise in foot arteries, and signal-tonoise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated. The arterial course and small vessel display were scored using a 5-point scale (1, poor; 5, excellent). Results: The CNR and SNR were significantly better in the DECT group than in the routine CTA group. The DECT group received higher subjective scores on the posterior tibial artery and the foot arteries (all > 3) than did the routine CTA group. Conclusion: Compared to the routine CTA imaging, DECT offers superior image quality for foot arteriography, thereby enhancing the diagnostic accuracy of foot arteriography and bolstering physicians’ confidence in disease diagnosis.

Objective: This study aimed to evaluate the effectiveness of a nomogram model based on dual-energy computed tomography (CT) quantitative parameters combined with clinical information for distinguishing metastatic from non-metastatic lymph nodes in patients with papillary thyroid carcinoma (PTC). Methods: This retrospective study was conducted in 103 patients with PTC from 2021 to 2023. Each patient underwent preoperative neck dual-energy-enhanced CT, and postoperative pathological results revealed 105 lymph nodes as metastatic and 136 as non-metastatic. Parameters, such as iodine concentration (IC), in the arterial phase, lymph node shape, short diameter, margin, degree of enhancement, cystic changes, calcification, arterial phase enhancement pattern, and thyroid nodule capsule integrity were analyzed. Univariate and multivariate logistic regression analyses identified independent risk factors for lymph node metastasis that were incorporated into the nomogram. The model performance was evaluated using a calibration curve. Results: Multivariate logistic regression analysis showed IC in the arterial phase ≥ 2.6 mg/mL, significant arterial phase enhancement, uneven arterial phase enhancement pattern, diameter ≥10 mm, unclear margins, irregular shape, and incomplete thyroid nodule capsule as independent risk factors for lymph node metastasis. The nomogram achieved an area under the curve of 0.996 (95% confidence interval (CI); 0.993~0.996) with a cutoff value of 0.09, sensitivity of 99%, and specificity of 95%. Conclusion: The dual-energy CT-based nomogram demonstrated a significant clinical value in the preoperative assessment of lymph node metastasis in patients with PTC, potentially assisting in the formulation of individualized surgical plans.

Objective To investigate the value of dual-energy CT (DECT) quantitative parameters combined with clinical laboratory indicators in differentiation of portal vein tumor thrombus (PVTT) from thrombosis (PVT). Methods A case-control study was conducted on 65 patients diagnosed with portal vein thrombosis (n=39) or tumor embolus (n=26) who underwent abdominal dual-energy enhanced CT examination in our department from May 2022 to March 2024. Their clinical and imaging data were collected. Linear blending image (LB), non-linear blending image (NLB), 40 keV and 100 keV virtual monoenergetic image (VMI), iodine image and electron density/effective atomic number image (Rho/Zeff) were reconstructed with the aid of post-processing workstation. The image characteristics of thrombus were evaluated in LB images, including whether the vessel wall at the thrombus was smooth, whether the vessel at the thrombus was widen, and whether the embolus was neovascularization. The quantitative parameters and clinical laboratory indicators of PVT and PVTT in DECT were compared. Laboratory indicators included apha-fetoprotein (AFP), carcinoembryonic antigen (CEA), alanine aminotransferase (ALT) and aspartate aminotransferase (AST). Univariate and multivariate logistic regression analyses were employed to analyze DECT quantitative parameters, embolus image characteristics and clinical laboratory indicators. Then receiver operating characteristic (ROC) curve was plotted to evaluate the diagnostic efficiency of the quantitative parameters and combined model of DECT. Results Among the DECT parameters, except for Rho, the other parameters in PVTT were significantly higher than those in PVT (P < 0.05). Multivariate logistic regression analysis showed that there were obvious differences in LB, NLB, CEA and AST (P < 0.05), and NLB had better diagnostic efficacy (AUC: 0.830, sensitivity: 53.85%, specificity: 100.00%, accuracy: 81.54%). The combined model based on DECT quantitative parameters and clinical laboratory indicators (LB+NLB+CEA+AST) obtained the best diagnostic efficacy (AUC: 0.983, sensitivity: 96.15%, specificity: 92.31%, accuracy: 93.85%). Conclusion The combined imaging and clinical model based on DECT provides a reliable reference for the differential diagnosis of benign and malignant portal vein embolus, and it has potential application prospects.

Head and neck cancer represents the seventh most common neoplasm worldwide, with squamous cell carcinoma being the most represented histologic variant. The rising incidence of the neoplastic pathology of this district, coupled with the drastic changes in its epidemiology over the past decades, have posed significant challenges to physicians worldwide in terms of diagnosis, prognosis, and treatment. In order to meet these challenges, a considerable amount of effort has been spent by the authors of the recent literature to explore new technologies and their possible employment for the better diagnostic and prognostic definition of head and neck squamous cell carcinoma (HNSCC). Among these technologies, a growing interest has been gathering around the possible applications of dual-energy computed tomography (DECT) in head and neck pathology. Dual-energy computed tomography (DECT) utilizes two distinct X-ray energy spectra to obtain two datasets in a single scan, allowing for material differentiation based on unique attenuation profiles. DECT offers key benefits such as enhanced contrast resolution, reduced beam-hardening artifacts, and precise iodine quantification through monochromatic reconstructions. It also creates material decomposition images, like iodine maps, aiding in tumor characterization and therapy assessment. This paper aims to summarize recent findings on the use of DECT in HNSCC, providing a comprehensive overview to aid further research and exploration in the field.

Accurate measurements of electron density and effective atomic number are essential for precise imaging diagnosis and dose estimation in the multiparametric functional analysis of dual-energy computed tomography (CT). This paper provides a review of research progress pertaining to different dual-energy imaging modalities of CT for measuring electron density and effective atomic number accurately. Its aim is to offer a reference for improving the accuracy of imaging diagnosis and treatment planning.

Purpose: The aim of this study was to evaluate the diagnostic performance of virtual non-calcium imaging using dual-energy computed tomography (CT) for acute sacrococcygeal injuries diagnosed by radiologists with varying levels of experience. Methods: A prospective study was employed and 29 patients presenting with acute coccygeal pain due to trauma between November 2021 and January 2024, involving a total of 174 vertebrae (145 sacral, 29 coccygeal), were enrolled. Both magnetic resonance imaging (MRI) and DECT data were acquired. Three radiologists with 1, 4, and 10 years of experience analyzed conventional CT and VNCa color-coded images, using MRI as the reference standard. Results: Following the application of VNCa imaging, the inter-rater agreement among the three radiologists significantly improved, with Kappa values increasing from 0.452, 0.615, and 0.735 to 0.775, 0.825, and 0.897, respectively. Diagnostic accuracy also notably increased to 92.5%, 94.3%, and 96.6% for the three radiologists, with no statistically significant differences observed among them. Quantitative analysis revealed an average VNCa CT value of (−84.2±20.3) HU for normal bone marrow and (−37.4±18.8) HU for bone marrow edema, showing a significant difference between the two. Using MRI as the reference standard, the area under the curve for distinguishing normal bone marrow from BME based on VNCa CT values was 0.962, with an optimal cutoff of −57.1 HU, yielding a sensitivity and specificity of 91.9％ and 90.2％, respectively. Conclusion: DECT VNCa imaging significantly enhances the diagnostic efficacy of radiologists with varying levels of experience in acute coccygeal injury assessment, particularly benefiting less-experienced physicians. VNCa CT values contribute substantially to diagnostic accuracy in this context.

With the popularization of high-resolution thin-layer CT, the detection rate of early asymptomatic lung cancer that manifests as solitary pulmonary nodules has significantly increased. The differentiation of benign from malignant nodules and histopathological classification are the keys to and challenges of clinical diagnoses, the accuracy of which is of great significance for treatment decision-making. Traditional CT evaluates nodules based on their size, density, and morphological characteristics with single modal imaging. Dual-energy CT achieves material separation based on attenuation differences in the same tissue at two different energy levels, which allows CT imaging to evolve from a single-parameter to multi-parameter measurements. This not only provides more valuable information for the early and accurate diagnosis of nodules but also facilitates measurements of tumor progression and heterogeneity. In addition, the combination of radiomics based on artificial intelligence and multi-parameter images by dual-energy CT has shown great potential in diagnosis, and the related investigations are in exploratory stages. This review will cover the application of dual-energy CT in the diagnosis of solitary pulmonary nodules, with a focus on the meaning of multi-parameter images for the ccurate diagnosis of pulmonary nodules. Further, the future directions of this diagnostic imaging technique are discussed as well.

The article addresses the diagnostic value of the combined use of computed tomography (CT) perfusion and dual-energy CT (DECT) in patients with neuroendocrine tumors. It emphasizes the heterogeneity and complexity of these neoplasms, primarily affecting the gastrointestinal tract, bronchopulmonary system, and pancreas. While conventional CT is widely employed in their diagnosis, the combination of CT perfusion and dual-energy CT offers greater precision, particularly in detecting synchronous tumors and characterizing their vascularization. A clinical case of a patient with chronic abdominal symptoms, whose diagnosis was facilitated using both combined techniques, is presented. The discussion explores how CT perfusion assesses tumor vascularization and how dual-energy CT improves soft tissue differentiation, resulting in increased diagnostic accuracy. It is highlighted that this approach not only enhances detection rates but also positively impacts clinical management and healthcare costs. Therefore, the importance of considering these advanced tools in the diagnosis of neuroendocrine tumors to improve diagnostic precision and efficiency in patient care is underscored.

Objective This article evaluates the ability of low-energy (40 keV) virtual monoenergetic images (VMIs) in the local diagnosis of cervical cancer compared with that of conventional computed tomography (C-CT) and magnetic resonance imaging (MRI), using clinicopathologic staging as a reference.

Abstract Background To explore the value of dual-energy computed tomography (DECT) in differentiating pathological subtypes and the expression of immunohistochemical markers Ki-67 and thyroid transcription factor 1 (TTF-1) in patients with non-small cell lung cancer (NSCLC). Methods Between July 2022 and May 2024, patients suspected of lung cancer who underwent two-phase contrast-enhanced DECT were prospectively recruited. Whole-tumor volumetric and conventional spectral analysis were utilized to measure DECT parameters in the arterial and venous phase. The DECT parameters model, clinical-CT radiological features model, and combined prediction model were developed to discriminate pathological subtypes and predict Ki-67 or TTF-1 expression. Multivariate logistic regression analysis was used to identify independent predictors. The diagnostic efficacy was assessed by the area under the receiver operating characteristic curve (AUC) and compared using DeLong’s test. Results This study included 119 patients (92 males and 27 females; mean age, 63.0 ± 9.4 years) who was diagnosed with NSCLC. When applying the DECT parameters model to differentiate between adenocarcinoma and squamous cell carcinoma, ROC curve analysis indicated superior diagnostic performance for conventional spectral analysis over volumetric spectral analysis (AUC, 0.801 vs. 0.709). Volumetric spectral analysis exhibited higher diagnostic efficacy in predicting immunohistochemical markers compared to conventional spectral analysis (both P

Abstract Objectives To develop and validate a quantitative model for predicting serosal invasion based on multi-parameters in preoperative dual-energy CT (DECT). Materials and methods A total of 342 LAGC patients who underwent gastrectomy and DECT from six centers were divided into one training cohort (TC), and two validation cohorts (VCs). Dual-phase enhanced DECT-derived iodine concentration (IC), water concentration, and monochromatic attenuation of lesions, along with clinical information, were measured and collected. The independent predictors among these characteristics for serosal invasion were screened with Spearman correlation analysis and logistic regression (LR) analysis. A quantitative model was developed based on LR classifier with fivefold cross-validation for predicting the serosal invasion in LAGC. We comprehensively tested the model and investigated its value in survival analysis. Results A quantitative model was established using IC, 70 keV, 100 keV monochromatic attenuations in the venous phase, and CT-reported T4a, which were independent predictors of serosal invasion. The proposed model had the area-under-the-curve (AUC) values of 0.889 for TC and 0.860 and 0.837 for VCs. Subgroup analysis showed that the model could well discriminate T3 from T4a groups, and T2 from T4a groups in all cohorts (all p

Abstract Objective To evaluate the prediction value of Dual-energy CT (DECT)-based quantitative parameters and radiomics model in preoperatively predicting muscle invasion in bladder cancer (BCa). Materials and methods A retrospective study was performed on 126 patients with BCa who underwent DECT urography (DECTU) in our hospital. Patients were randomly divided into training and test cohorts with a ratio of 7:3. Quantitative parameters derived from DECTU were identified through univariate and multivariate logistic regression analysis to construct a DECT model. Radiomics features were extracted from the 40, 70, 100 keV and iodine-based material-decomposition (IMD) images in the venous phase to construct radiomics models from individual and combined images using a support vector machine classifier, and the optimal performing model was chosen as the final radiomics model. Subsequently, a fusion model combining the DECT parameters and the radiomics model was established. The diagnostic performances of all three models were evaluated through receiver operating characteristic (ROC) curves and the clinical usefulness was estimated using decision curve analysis (DCA). Results The normalized iodine concentration (NIC) in DECT was an independent factor in diagnosing muscle invasion of BCa. The optimal multi-image radiomics model had predictive performance with an area-under-the-curve (AUC) of 0.867 in the test cohort, better than the AUC = 0.704 with NIC. The fusion model showed an increased level of performance, although the difference in AUC (0.893) was not statistically significant. Additionally, it demonstrated superior performance in DCA. For lesions smaller than 3 cm, the fusion model showed a high predictive capability, achieving an AUC value of 0.911. There was a slight improvement in model performance, although the difference was not statistically significant. This improvement was observed when comparing the AUC values of the DECT and radiomics models, which were 0.726 and 0.884, respectively. Conclusion The proposed fusion model combing NIC and the optimal multi-image radiomics model in DECT showed good diagnostic capability in predicting muscle invasiveness of BCa.

Objective To investigate the differential diagnostic value of dual-energy CT electron cloud density （Rho） and effective atomic number （Z） between benign and malignant thyroid nodules. Methods Imaging data of 68 patients with pathologically confirmed thyroid nodules at Lingnan Hospital， the Third Affiliated Hospital of Sun Yat-sen University from March to December 2023 were retrospectively analyzed. Among the 68 cases， 31 patients were diagnosed with benign thyroid nodules and 37 with malignant thyroid nodules. All patients underwent plain CT scan combined with enhanced dual-energy CT scan of the neck in the first week before surgery. The arterial-phase and venous-phase electron cloud density maps and effective atomic number maps were reconstructed in the post-processing workstation. Rho and Z values of benign and malignant thyroid nodules were compared by Mann-Whitney U test. Statistically significant dual-energy parameters were analyzed and their differential diagnostic efficiency in diagnosing benign and malignant thyroid nodules was calculated using the receiver operating characteristic （ROC） curve. Results Arterial- and venous-phase Z values and venous-phase Rho were not statistically significant between benign and malignant thyroid nodules （all P > 0.05）， whereas arterial-phase Rho value was statistically significant between benign and malignant thyroid nodules （P < 0.05）. The arterial-phase Rho of malignant thyroid nodules was higher than that of the benign thyroid nodules， with an area under the ROC curve （AUC） of 0.711（95%CI 0.586-0.836）， a sensitivity of 73.0%， and a specificity of 64.5%. The differential diagnostic efficiency was the highest when the arterial-phase Rho was 38.6 Hu. Conclusion Dual-energy CT arterial-phase Rho mapping has certain value in the differential diagnosis of benign and malignant thyroid nodules.

Background Purpose Methods Results Conclusion Multi‐material decomposition is an interesting topic in dual‐energy CT (DECT) imaging; however, the accuracy and performance may be limited using the conventional algorithms.In this work, a novel multi‐material decomposition network (MMD‐Net) is proposed to improve the multi‐material decomposition performance of DECT imaging.To achieve dual‐energy multi‐material decomposition, a deep neural network, named as MMD‐Net, is proposed in this work. In MMD‐Net, two specific convolutional neural network modules, Net‐I and Net‐II, are developed. Specifically, Net‐I is used to distinguish the material triangles, while Net‐II predicts the effective attenuation coefficients corresponding to the vertices of the material triangles. Subsequently, the material‐specific density maps are calculated analytically through matrix inversion. The new method is validated using in‐house benchtop DECT imaging experiments with a solution phantom and a pig leg specimen, as well as commercial medical DECT imaging experiments with a human patient. The decomposition accuracy, edge spreading function, and noise power spectrum are quantitatively evaluated.Compared to the conventional multiple material decomposition (MMD) algorithm, the proposed MMD‐Net method is more effective at suppressing image noise. Additionally, MMD‐Net outperforms the iterative MMD approach in maintaining decomposition accuracy, image sharpness, and high‐frequency content. Consequently, MMD‐Net is capable of generating high‐quality material decomposition images.A high performance multi‐material decomposition network is developed for dual‐energy CT imaging. [ABSTRACT FROM AUTHOR]

Background: Our study aims to provide an overview of existing evidence regarding the image quality of dual-energy CT (DECT) employing reduced contrast media (CM) volumes, in comparison to single-energy CT (SECT) with standard CM loads. The advantages, indications, and possible applications of DECT were investigated from the perspective of providing better patient care, minimizing CM volume and managing CM shortage. Methods: In this systematic review (PRISMA methodology), PubMed and WOS were searched from January 2010 to January 2023 by two independent reviewers. The scan and CM characteristics, radiation dose, and results of quantitative (contrast to noise ratio, CNR, and signal to noise ratio, SNR) and qualitative assessment of image quality were collected. Sixty non-duplicated records eligible for full-text screening were examined. Results: Finally, 22 articles (1818 patients) were included. The average CM reduction with DECT ranged between 43.4 ± 11%. Despite the wide variability in CT scan protocols, no differences were found in radiation doses between DECT and SECT. Conclusions: DECT scanners allow the employment of lower CM volumes with equal or better image quality evaluated by quantitative and qualitative analyses and similar dose radiation compared to SECT. Using image reconstructions at low monochromatic energy levels, DECT increases iodine conspicuity and attenuation contributing to CM containment measures. [ABSTRACT FROM AUTHOR]

Objective: The purpose of this study was to investigate the impact of different low-energy virtual monochromatic images (VMIs) in dual-energy CT on the performance of radiomics models for predicting muscle invasive status in bladder cancer (BCa). Materials and methods: A total of 127 patients with pathologically proven muscle-invasive BCa (n = 49) and non-muscle-invasive BCa (n = 78) were randomly allocated into the training and test cohorts at a ratio of 7:3. Feature extraction was performed on the venous phase images reconstructed at 40, 50, 60 and 70-keV (single-energy analysis) or in combination (multi-energy analysis). Recursive feature elimination (RFE) and the least absolute shrinkage and selection operator (LASSO) were employed to select the most relevant features associated with BCa. Models were built using a support vector machine (SVM) classifier. Diagnostic performance was assessed through receiver operating characteristic curves, evaluating sensitivity, specificity, accuracy, precision, and the area-under-the curve (AUC) values. Results: In the test cohort, the multi-energy model achieved the best diagnostic performance with AUC, sensitivity, specificity, accuracy, and precision of 0.917, 0.800, 0.833, 0.821, and 0.750, respectively. Conversely, the single-energy model exhibited lower AUC and sensitivity in predicting the muscle invasion status. Conclusions: By combining information from VMIs of various energies, the multi-energy model displays superior performance in preoperatively predicting the muscle invasion status of bladder cancer. [ABSTRACT FROM AUTHOR]

Copyright of CT Theory & Applications is the property of Editorial Department of CT Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of CT Theory & Applications is the property of Editorial Department of CT Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of CT Theory & Applications is the property of Editorial Department of CT Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Head and neck cancer represents the seventh most common neoplasm worldwide, with squamous cell carcinoma being the most represented histologic variant. The rising incidence of the neoplastic pathology of this district, coupled with the drastic changes in its epidemiology over the past decades, have posed significant challenges to physicians worldwide in terms of diagnosis, prognosis, and treatment. In order to meet these challenges, a considerable amount of effort has been spent by the authors of the recent literature to explore new technologies and their possible employment for the better diagnostic and prognostic definition of head and neck squamous cell carcinoma (HNSCC). Among these technologies, a growing interest has been gathering around the possible applications of dual-energy computed tomography (DECT) in head and neck pathology. Dual-energy computed tomography (DECT) utilizes two distinct X-ray energy spectra to obtain two datasets in a single scan, allowing for material differentiation based on unique attenuation profiles. DECT offers key benefits such as enhanced contrast resolution, reduced beam-hardening artifacts, and precise iodine quantification through monochromatic reconstructions. It also creates material decomposition images, like iodine maps, aiding in tumor characterization and therapy assessment. This paper aims to summarize recent findings on the use of DECT in HNSCC, providing a comprehensive overview to aid further research and exploration in the field. [ABSTRACT FROM AUTHOR]

Purpose: Preliminary dual-energy CT studies have shown that low-energy virtual monoenergetic (VMI) + reconstructions can provide superior image quality compared to standard 120 kV CTA series. The purpose of this study is to evaluate the impact of low-energy VMI reconstructions on quantitative and qualitative image quality, vascular contrast, and diagnostic assessability of the carotid artery in patients undergoing photon-counting CTA examinations. Materials and methods: A total of 122 patients (67 male) who had undergone dual-source photon-counting CTA scans of the carotid artery were retrospectively analyzed in this study. Standard 120 kV CT images and low-keV VMI series from 40 to 100 keV with an interval of 15 keV were reconstructed. Quantitative analyses included the evaluation of vascular CT numbers, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). CT number measurements were performed in the common, external, and internal carotid arteries. Qualitative analyses were performed by three board-certified radiologists independently using five-point scales to evaluate image quality, vascular contrast, and diagnostic assessability of the carotid artery. Results: Mean attenuation, CNR and SNR values were highest in 40 keV VMI reconstructions (HU, 1362.32 ± 457.81; CNR, 33.19 ± 12.86; SNR, 34.37 ± 12.89) followed by 55-keV VMI reconstructions (HU, 736.94 ± 150.09; CNR, 24.49 ± 7.11; SNR, 26.25 ± 7.34); all three mean values at these keV levels were significantly higher compared with the remaining VMI series and standard 120 kV CT series (HU, 154.43 ± 23.69; CNR, 16.34 ± 5.47; SNR, 24.44 ± 7.14) (p < 0.0001). The qualitative analysis showed the highest rating scores for 55 keV VMI reconstructions followed by 40 keV and 70 keV VMI series with a significant difference compared to standard 120 kV CT images series regarding image quality, vascular contrast, and diagnostic assessability of the carotid artery (all comparisons, p < 0.01). Conclusions: Low-keV VMI reconstructions at a level of 40–55 keV significantly improve image quality, vascular contrast, and the diagnostic assessability of the carotid artery compared with standard CT series in photon-counting CTA. [ABSTRACT FROM AUTHOR]

Objectives: To develop and validate a quantitative model for predicting serosal invasion based on multi-parameters in preoperative dual-energy CT (DECT). Materials and methods: A total of 342 LAGC patients who underwent gastrectomy and DECT from six centers were divided into one training cohort (TC), and two validation cohorts (VCs). Dual-phase enhanced DECT-derived iodine concentration (IC), water concentration, and monochromatic attenuation of lesions, along with clinical information, were measured and collected. The independent predictors among these characteristics for serosal invasion were screened with Spearman correlation analysis and logistic regression (LR) analysis. A quantitative model was developed based on LR classifier with fivefold cross-validation for predicting the serosal invasion in LAGC. We comprehensively tested the model and investigated its value in survival analysis. Results: A quantitative model was established using IC, 70 keV, 100 keV monochromatic attenuations in the venous phase, and CT-reported T4a, which were independent predictors of serosal invasion. The proposed model had the area-under-the-curve (AUC) values of 0.889 for TC and 0.860 and 0.837 for VCs. Subgroup analysis showed that the model could well discriminate T3 from T4a groups, and T2 from T4a groups in all cohorts (all p < 0.001). Besides, disease-free survival (DFS) (TC, p = 0.015; and VC1, p = 0.043) could be stratified using this quantitative model. Conclusion: The proposed quantitative model using multi-parameters in DECT accurately predicts serosal invasion for LAGC and showed a significant correlation with the DFS of patients. Critical relevance statement: This quantitative model from dual-energy CT is a useful tool for predicting the serosal invasion of locally advanced gastric cancer. Key Points: Serosal invasion is a poor prognostic factor in locally advanced gastric cancer that may be predicted by DECT. DECT quantitative model for predicting serosal invasion was significantly and positively correlated with pathologic T stages. This quantitative model was associated with patient postoperative disease-free survival. [ABSTRACT FROM AUTHOR]

Applications of computed tomography (CT) in arthritis imaging have rapidly expanded in recent years due to ongoing technical developments. Dual-energy CT (DECT) has become indispensable in clinical practice, particularly for diagnosing gouty arthritis and assessing bony structural changes. Technological innovations such as low-dose CT and state-of-the-art reconstruction algorithms reduce radiation exposure while maintaining image quality and short acquisition times. This review explores the growing role of CT in arthritis imaging. Recent innovations have extended DECT's utility beyond gout diagnosis to the detection of inflammatory changes in various arthritic conditions. Postprocessing techniques such as the generation of subtraction images and iodine maps provide valuable insights into tissue perfusion and inflammatory activity, crucial for arthritis management. DECT can distinguish calcium from uric acid crystals, facilitating the differential diagnosis of various crystal arthropathies in a variety of clinical settings. This ability is particularly valuable in distinguishing between different clinical conditions in patients with inflammatory joint changes within a single imaging examination. Moreover, the advent of four-dimensional CT promises a better assessment of dynamic joint instabilities and ligament injuries, especially in the wrist. Overall, DECT offers a comprehensive approach to arthritis imaging, from the detection of structural changes to the assessment of active inflammation in joints and tendons. Continuous advances in CT technology, including photon-counting CT, hold promise for further improving diagnostic accuracy and expanding the role of CT in arthritis imaging and therapy monitoring. [ABSTRACT FROM AUTHOR]

Multi‐energy computed tomography (MECT) offers the opportunity for advanced visualization, detection, and quantification of select elements (e.g., iodine) or materials (e.g., fat) beyond the capability of standard single‐energy computed tomography (CT). However, the use of MECT requires careful consideration as substantially different hardware and software approaches have been used by manufacturers, including different sets of user‐selected or hidden parameters that affect the performance and radiation dose of MECT. Another important consideration when designing MECT protocols is appreciation of the specific tasks being performed; for instance, differentiating between two different materials or quantifying a specific element. For a given task, it is imperative to consider both the radiation dose and task‐specific image quality requirements. Development of a quality control (QC) program is essential to ensure the accuracy and reproducibility of these MECT applications. Although standard QC procedures have been well established for conventional single‐energy CT, the substantial differences between single‐energy CT and MECT in terms of system implementations, imaging protocols, and clinical tasks warrant QC tests specific to MECT. This task group was therefore charged with developing a systematic QC program designed to meet the needs of MECT applications. In this report, we review the various MECT approaches that are commercially available, including information about hardware implementation, MECT image types, image reconstruction, and postprocessing techniques that are unique to MECT. We address the requirements for MECT phantoms, review representative commercial MECT phantoms, and offer guidance regarding homemade MECT phantoms. We discuss the development of MECT protocols, which must be designed carefully with proper consideration of MECT technology, imaging task, and radiation dose. We then outline specific recommended QC tests in terms of general image quality, radiation dose, differentiation and quantification tasks, and diagnostic and therapeutic applications. [ABSTRACT FROM AUTHOR]

Objective: To assess the value of material density (MD) images generated from a rapid kilovoltage-switching dual-energy CT (rsDECT) in early detection of peritoneal carcinomatosis (PC). Materials and methods: Thirty patients (60 ± 13 years; 24 women) with PC detected on multiple abdominal DECT scans were included. Four separate DECTs with varying findings of PC from each patient were used for qualitative/quantitative analysis, resulting in a total of 120 DECT scans (n = 30 × 4). Three radiologists independently reviewed DECT images (65 keV alone and 65 keV + MD) for diagnosis of PC (diagnostic confidence, lesion conspicuity, sharpness/delineation and image quality) using a 5-point Likert scale. Quantitative estimation of contrast-to-noise ratio (CNR) was done. Wilcoxon signed-rank test and Odds ratio calculation were used to compare between the two protocols. Inter-observer agreement was evaluated using Kappa coefficient analysis. P values < 0.05 were considered statistically significant. Results: 65 keV + MD images showed a slightly higher sensitivity (89%[95%CI:84,92]) for PC detection compared with 65 keV images alone without statistical significance (84%[95%CI:78,88], p = 0.11) with the experienced reader showing significant improvement (98%[95%CI:93,100] vs. 90%[95%CI:83,94], p = 0.02). On a per-patient basis, use of MD images allowed earlier diagnosis for PC in an additional 13–23% of patients. On sub-group analysis, earlier diagnosis of PC was particularly beneficial in patients with BMI ≤ 29.9 kg/m2. 65 keV + MD images showed higher diagnostic confidence, lesion conspicuity, and lesion sharpness for the experienced reader (p < 0.001). CNR was higher in MD images (1.7 ± 0.5) than 65 keV images (0.1 ± 0.02, p < 0.001). All readers showed moderate interobserver agreement for determining PC by both protocols (κ = 0.58 and κ = 0.47). Conclusion: MD images allow earlier and improved detection of PC with the degree of benefit varying based on reader experience and patient body habitus. [ABSTRACT FROM AUTHOR]

Purpose: To non-invasively quantify pancreatic fibrosis and grade severity of chronic pancreatitis (CP) on dual-energy CT (DECT) and multiparametric MRI (mpMRI). Methods: We included 72 patients (mean age:30years; 59 men) with suspected or confirmed CP from December 2019 to December 2021 graded as equivocal(n = 20), mild(n = 18), and moderate-marked(n = 34) using composite imaging and endoscopic ultrasound criteria. Study patients underwent multiphasic DECT and mpMRI of the abdomen. Normalized iodine concentration(NIC) and fat fraction(FF) on 6-minute delayed DECT, and T1 relaxation time(T1Rt), extracellular volume fraction(ECVf), intravoxel incoherent motion-based perfusion fraction(PF), and magnetization transfer ratio(MTR) on mpMRI of pancreas were compared. 20 renal donors(for DECT) and 20 patients with renal mass(for mpMRI) served as controls. Results: NIC of pancreas in controls and progressive grades of CP were 0.24 ± 0.05, 0.80 ± 0.18, 1.06 ± 0.23, 1.40 ± 0.36, FF were 9.28 ± 5.89, 14.19 ± 5.29, 17.31 ± 5.99, 29.32 ± 12.22, T1Rt were 590.11 ± 61.13, 801.93 ± 211.01, 1006.79 ± 352.18, 1388.01 ± 312.23ms, ECVf were 0.07 ± 0.03, 0.30 ± 0.12, 0.41 ± 0.12, 0.53 ± 0.13, PF were 0.38 ± 0.04, 0.28 ± 0.07, 0.25 ± 0.09, 0.21 ± 0.05 and MTR were 0.12 ± 0.03, 0.15 ± 0.06, 0.21 ± 0.07, 0.26 ± 0.06, respectively. There were significant differences for all quantitative parameters between controls and mild CP; for NIC, PF, and ECVf between controls and progressive CP grades (p < 0.05). Area under curve for NIC, FF, T1Rt, ECVf, PF, and MTR in differentiating controls and mild CP were 1.00, 0.86, 0.95, 1.00, 0.90 and 0.84 respectively and for NIC, FF, ECVf and PF in differentiating controls and equivocal CP were 1.00, 0.76, 0.95 and 0.92 respectively. Conclusion: DECT and mpMRI were useful in quantifying pancreatic fibrosis and grading the severity of CP. NIC was the most accurate marker. [ABSTRACT FROM AUTHOR]

Objective This article evaluates the ability of low-energy (40 keV) virtual monoenergetic images (VMIs) in the local diagnosis of cervical cancer compared with that of conventional computed tomography (C-CT) and magnetic resonance imaging (MRI), using clinicopathologic staging as a reference. Methods This prospective study included 33 patients with pathologically confirmed cervical cancer who underwent dual-energy CT and MRI between 2021 and 2022. The contrast-to-noise ratio (CNR) of the tumor-to-myometrium was compared between C-CT and VMI. Additionally, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) for each local diagnostic parameter were compared between C-CT, VMI, and MRI. Interradiologist agreement was also assessed. Results The mean CNR was significantly higher on VMI (p = 0.002). No significant difference in AUC was found between C-CT and VMI for all local diagnostic parameters, and the specificity of VMI was often significantly less than that of MRI. For parametrial invasion, mean sensitivity, specificity, and AUC for C-CT, VMI, and MRI were 0.81, 0.99, 0.93; 0.64, 0.35, 0.79; and 0.73, 0.67, 0.86, respectively, and MRI had significantly higher specificity and AUC than that of VMI (p = 0.013 and 0.008, respectively). Interradiologist agreement was higher for VMI than C-CT and for MRI than VMI. Conclusion The CNR of VMI was significantly higher than C-CT and interradiologist agreement was better than with C-CT; however, the overall diagnostic performance of VMI did not significantly differ from C-CT and was inferior to MRI. VMI was characterized by low specificity, which should be understood and used for reading. [ABSTRACT FROM AUTHOR]

A 74‐year‐old man was admitted to our emergency department following minor trauma. Plain radiographs and standard computed tomography (CT) scans revealed no signs of fractures. Subsequently, virtual noncalcium (VNCa) images were reconstructed, showing a linear area of bone marrow edema (BME) resembling a femoral neck fracture. Magnetic resonance imaging (MRI) was performed to confirm the presence of BME and an associated intraspongious fracture. In an emergency setting, dual‐energy CT (DECT) and VNCa images can successfully identify occult femoral fractures, especially in patients with mild symptoms and minor trauma, thereby preventing misdiagnosis. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the prediction value of Dual-energy CT (DECT)-based quantitative parameters and radiomics model in preoperatively predicting muscle invasion in bladder cancer (BCa). Materials and methods: A retrospective study was performed on 126 patients with BCa who underwent DECT urography (DECTU) in our hospital. Patients were randomly divided into training and test cohorts with a ratio of 7:3. Quantitative parameters derived from DECTU were identified through univariate and multivariate logistic regression analysis to construct a DECT model. Radiomics features were extracted from the 40, 70, 100 keV and iodine-based material-decomposition (IMD) images in the venous phase to construct radiomics models from individual and combined images using a support vector machine classifier, and the optimal performing model was chosen as the final radiomics model. Subsequently, a fusion model combining the DECT parameters and the radiomics model was established. The diagnostic performances of all three models were evaluated through receiver operating characteristic (ROC) curves and the clinical usefulness was estimated using decision curve analysis (DCA). Results: The normalized iodine concentration (NIC) in DECT was an independent factor in diagnosing muscle invasion of BCa. The optimal multi-image radiomics model had predictive performance with an area-under-the-curve (AUC) of 0.867 in the test cohort, better than the AUC = 0.704 with NIC. The fusion model showed an increased level of performance, although the difference in AUC (0.893) was not statistically significant. Additionally, it demonstrated superior performance in DCA. For lesions smaller than 3 cm, the fusion model showed a high predictive capability, achieving an AUC value of 0.911. There was a slight improvement in model performance, although the difference was not statistically significant. This improvement was observed when comparing the AUC values of the DECT and radiomics models, which were 0.726 and 0.884, respectively. Conclusion: The proposed fusion model combing NIC and the optimal multi-image radiomics model in DECT showed good diagnostic capability in predicting muscle invasiveness of BCa. [ABSTRACT FROM AUTHOR]

Background/Objectives: Fragility fractures of the pelvis (FFP) are characterized by inadequate trauma to a structurally compromised bone, primarily in osteoporosis. Conventional CT studies can be inadequate in identifying FFPs. An MRI of the pelvis is considered the gold standard in diagnosing FFPs. Spectral CT or Dual-Energy CT may have comparable diagnostic accuracy. It provides additional insights into associated bone marrow edema. The aim of this prospective monocentric study is to evaluate the diagnostic accuracy of Spectral CT compared to the gold standard MRI in diagnosing FFP. Methods: Over a 2-year period, patients presenting in the emergency department with clinical suspicion of an FFP were consecutively included. They underwent Spectral CT (GE Revolution 16 cm GSI) upon admission, followed by an MRI. The gold standard for diagnosing FFP is pelvic MRI, showing sensitivity and specificity ranging from 97% to 100%. The acquired images were evaluated and classified using the osteoporotic fractures of the pelvis (OFP) classification. Results: Compared to the reference test, which was the MRI pelvis, the sensitivity of the CT pelvis was determined to be 86.8 (95% confidence interval (CI) 71.9–95.6%) with a specificity of 84.6% (95% CI: 54.6–98.1%, p = 0.453). Spectral CT could identify an additional FFP correctly, exhibiting a sensitivity of 89.5% (95% CI: 75.2–97.1%, p = 0.688), while maintaining the same specificity as the conventional CT. The inter-rater reliability assessment for Spectral CT, conducted by four independent raters, resulted in a Fleiss' Kappa value of 0.516 (95% CI: 0.450–0.582, p < 0.001). Conclusion: The sensitivity of Spectral CT in the detection of pelvic ring fragility fractures shows a slightly lower sensitivity compared to MRI. There were no statistically significant differences observed when compared to conventional CT or MRI. In conclusion, Spectral CT may be beneficial in distinguishing FFP, particularly in cases where a definitive diagnosis is uncertain. Level of Evidence: II. [ABSTRACT FROM AUTHOR]