Unenhanced Images Research Articles

Association of Cardiovascular Risk Factors and Coronary Calcium Burden with Epicardial Adipose Tissue Volume Obtained from PET-CT Imaging in Oncological Patients.

Whole-body positron emission tomography (PET)-computed tomography (CT) imaging performed for oncological purposes may provide additional parameters such as the coronary artery calcium (CAC) and epicardial adipose tissue (EAT) volume with cost-effective prognostic information in asymptomatic people beyond traditional cardiovascular risk factors. We evaluated the feasibility of measuring the CAC score and EAT volume in cancer patients without known coronary artery disease (CAD) referred to whole-body 18F-FDG PET-CT imaging, regardless of the main clinical problem. We also investigated the potential relationships between traditional cardiovascular risk factors and CAC with EAT volume. A total of 109 oncological patients without overt CAD underwent whole-body PET-CT imaging with 18F-fluorodeoxyglucose (FDG). Unenhanced CT images were retrospectively viewed for CAC and EAT measurements on a dedicated platform. Overall, the mean EAT volume was 99 ± 49 cm3. Patients with a CAC score ≥ 1 were older than those with a CAC = 0 (p < 0.001) and the prevalence of hypertension was higher in patients with detectable CAC as compared to those without (p < 0.005). The EAT volume was higher in patients with CAC than in those without (p < 0.001). For univariable age, body mass index (BMI), hypertension, and CAC were associated with increasing EAT values (all p < 0.005). However, the correlation between the CAC score and EAT volume was weak, and in multivariable analysis only age and BMI were independently associated with increased EAT (both p < 0.001), suggesting that potential prognostic information on CAC and EAT is not redundant. This study demonstrates the feasibility of a cost-effective assessment of CAC scores and EAT volumes in oncological patients undergoing whole-body 18F-FDG PET-CT imaging, enabling staging cancer disease and atherosclerotic burden by a single test already included in the diagnostic work program, with optimization of the radiation dose and without additional costs.

The incremental value of Histogram Analysis in the differentiation between hyperdense cysts and solid renal masses on unenhanced CT images.

To investigate the utility of voxel histogram analysis (HA) for differentiating hyperdense renal cysts from small solid masses on unenhanced computed tomography (CT) scans. A retrospective analysis of 99 hyperdense cystic lesions and 28 solid malignant lesions was conducted using a radiological database (from 2015 to 2021) and a pathological database (from 2010 to 2020). The study investigated the distribution of voxel attenuation values using percentiles to establish reliable criteria for differentiation after drawing a region of interest (ROI) in the centre of the lesions. The standard of reference was a histopathological diagnosis for malignant masses and contrast-enhanced CT or magnetic resonance imaging (MRI) for cysts. HA provided higher diagnostic accuracy than the conventional mean attenuation value of 70 Hounsfield Units (HU). For the 75th and 90th percentiles ± 1 standard deviation, accuracies of 77.2% (95% confidence interval 68.9-84.2%) for the 75th and 68.5% (59.7-76.4%) for the 90th were found, versus 37.0% (28.6-46.0%) for the 70 HU threshold criterion. A Gaussian distribution of voxel attenuation values was observed in 88.9% of the lesions, suggesting that it is feasible to calculate these parameters from a single measurement. The study underscores the potential of HA as a valuable tool for characterizing hyperdense cysts on unenhanced CT by using the same ROI for measuring lesion attenuation. HA could offer additional value beyond the 70 HU criterion and possibly influence clinical decisions. Multi-institutional studies are necessary for external validation to confirm its generalizability and more extensive applicability.

A vision transformer-based deep transfer learning nomogram for predicting lymph node metastasis in lung adenocarcinoma.

Lymph node metastasis (LNM) plays a crucial role in the management of lung cancer; however, the ability of chest computed tomography (CT) imaging to detect LNM status is limited. This study aimed to develop and validate a vision transformer-based deep transfer learning nomogram for predicting LNM in lung adenocarcinoma patients using preoperative unenhanced chest CT imaging. This study included 528 patients with lung adenocarcinoma who were randomly divided into training and validation cohorts at a 7:3 ratio. The pretrained vision transformer (ViT) was utilized to extract deep transfer learning (DTL) feature, and logistic regression was employed to construct a ViT-based DTL model. Subsequently, the model was compared with six classical convolutional neural network (CNN) models. Finally, the ViT-based DTL signature was combined with independent clinical predictors to construct a ViT-based deep transfer learning nomogram (DTLN). The ViT-based DTL model showed good performance, with an area under the curve (AUC) of 0.821 (95% CI, 0.775-0.867) in the training cohort and 0.825 (95% CI, 0.758-0.891) in the validation cohort. The ViT-based DTL model demonstrated comparable performance to classical CNN models in predicting LNM, and the ViT-based DTL signature was then used to construct ViT-based DTLN with independent clinical predictors such as tumor maximum diameter, location, and density. The DTLN achieved the best predictive performance, with AUCs of 0.865 (95% CI, 0.827-0.903) and 0.894 (95% CI, 0845-0942), respectively, surpassing both the clinical factor model and the ViT-based DTL model (p<0.001). This study developed a new DTL model based on ViT to predict LNM status in lung adenocarcinoma patients and revealed that the performance of the ViT-based DTL model was comparable to that of classical CNN models, confirming that ViT was viable for deep learning tasks involving medical images. The ViT-based DTLN performed exceptionally well and can assist clinicians and radiologists in making accurate judgments and formulating appropriate treatment plans.

Can combined tomosynthesis with unenhanced MRI be used as a predictive tool for lymphovascular invasion?

BackgroundThe presence of lymphovascular invasion (LVI) in cases with breast cancer is considered a bad prognostic sign. The purpose of this study is to compare the efficacy of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) versus unenhanced magnetic resonance imaging (UE-MRI + DBT) in predicting LVI in women with pathologically confirmed breast cancer.MethodsThis prospective self-controlled study enrolled a total of 70 cases of pathologically proven breast cancer. All the patients underwent tomosynthesis, non-contrast, and post-contrast MRI. Depending on the broken halo sign seen in tomosynthesis, peritumoral edema, dark rim diffusion at diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) values evaluated in MRI.ResultsThe accuracy of LVI detection by tomosynthesis was 58%; unenhanced and enhanced MRI had the same results at 60%. The accuracy of detecting LVI was raised to 64% by combining the tomosynthesis results with unenhanced MRI.ConclusionsTomosynthesis parameters are promising tools in detecting LVI in breast cancer with better diagnostic accuracy in combination with unenhanced MRI.

Comparison of Conventional and Virtual Non-contrast Abdominal Images Using the Third-Generation Dual-Source Dual-Energy Computed Tomography.

To determine the efficacy and safety of virtual unenhanced imaging by comparing the attenuation values of virtual and true unenhanced images acquired using third-generation dual-source dual-energy computed tomography (dsDECT). Single-energy non-contrast and dual-energy arterial and venous phase images of 97 patients who underwent triphasic abdominal computed tomography (CT) were included in this retrospective study. Virtual unenhanced images were generated for the arterial (a) and venous (v) phases using two dsDECT algorithms. The attenuation values were measured on the true and virtual unenhanced images of the liver, spleen, kidney, gallbladder, paraspinal muscle, aorta, subcutaneous fat, retroperitoneal fat, and renal cysts. A statistically significant difference was observed between the attenuation values ​​of true and virtual unenhanced images for all tissues (p < 0.001-0.025), except the venous phase virtual unenhanced images of the kidney, renal cysts, and gallbladder (p = 0.061-0.325). The proportion of cases with differences of ≥ 10 Hounsfield unit (HU) in the attenuation values between the virtual and true unenhanced images ranged from 3% to 8% for renal parenchyma, renal cysts, and gallbladder using this algorithm; however, this proportion was up to 90% for adipose tissue. No significant correlation was observed between the body mass index and attenuation differences between the true and virtual unenhanced images, except for those of the aorta and paraspinal muscle. Virtual unenhanced images acquired using third-generation dsDECT cannot replace true unenhanced images in clinical practice owing to the difference between the attenuation values and variability of attenuation between true and virtual unenhanced images.

Predictive Value of Quantitative Parameters of 18F-FDG PET/CT in Patients with Liposarcoma.

The purpose of this study was to evaluate the predictive features of baseline F-18-fluorodeoxy-D-glucose positron emission tomography (18F-FDG PET)/computed tomography (CT) parameters in patients with dedifferentiated liposarcomas (DDLPSs) and well-differentiated liposarcomas (WDLPSs) receiving systemic treatment. A total of 24 patients with liposarcoma who underwent longitudinal 18F-FDG PET/CT in systemic therapy were included. All volumetric segmentation of each tumor section and semiquantitative imaging parameters were extracted from the axial field of view from both PET and CT images. Maximum, mean, and minimum standardized uptake values (SUVmax, SUVmean, and SUVmin), Hounsfield units (HUs), and their respective changes from baseline and posttreatment were calculated. The voxel values from unenhanced CT images were correlated with PET-derived parameters. The 18F-FDG uptake decreased by more than 56% on average in responders for both SUVmax and SUVmean in DDLPS. There was a decrease in HUmax in DDLPS among responders. Using AUC > 0.8 as a reasonable predictor, we found that the ratios of SUVmaxD/HUmean, SUVmaxD/HUmedian, and SUVmeanD/HUmedian at baseline were significant indicators of the response to treatment in patients with liposarcoma. The changes in SUVmean and not just SUVmax parameters could be considered as accurate tumor response indicators. For the first time, we introduced baseline SUV/HU ratios as a valuable diagnostic tool in predicting liposarcoma treatment outcomes. This ability was not revealed by classic semiquantitative PET or CT parameters at baseline.

Case Report: - Sudden sensorineural hearing loss and vertigo due to inner ear hemorrhage

Sudden deafness is a common cause of emergency consultation in otology. Usually, despite investigations, no etiology is known. Intracochlear hemorrhage is a rare cause of sudden sensorineural hearing loss (SSNHL) and may be associated with various labyrinthine disorders. In such cases, magnetic resonance imaging (MRI) is the clef of the diagnosis. We report the case of a 70-year-old patient who was referred to our department for sudden hearing loss, tinnitus, and vertigo. Otoscopic and neurological examinations were normal, and pure-tone audiometry revealed left profound sensorineural hearing loss. Videonystagmography (VNG) revealed left vestibular deficit. MRI demonstrated a high signal intensity inside the cochlea on unenhanced T1-weighted images, and no other abnormalities were found; in particular, no enhancement after intravenous administration of gadolinium. No etiology was identified. Vertigo disappeared rapidly with corticosteroid treatment and hyperbaric oxygen therapy, but hearing did not improve. Intra-labyrinthine hemorrhage causing SSNHL is rare, and the hearing prognosis is poor.

Improved detection of small pulmonary embolism on unenhanced computed tomography using an artificial intelligence-based algorithm - a single centre retrospective study.

To preliminarily verify the feasibility of a deep-learning (DL) artificial intelligence (AI) model to localize pulmonary embolism (PE) on unenhanced chest-CT by comparison with pulmonary artery (PA) CT angiography (CTA). In a monocentric study, we retrospectively reviewed 99 oncological patients (median age in years: 64 (range: 28-92 years); percentage of female: 39.4%) who received unenhanced and contrast-enhanced chest CT examinations in one session between January 2020 and October 2022 and who were diagnosed incidentally with PE. Findings in the unenhanced images were correlated with the contrast-enhanced images, which were considered the gold standard for central, segmental and subsegmental PE. The new algorithm was trained and tested based on the 99 unenhanced chest-CT image data sets. Based on them, candidate boxes, which were output by the model, were post-processed by evaluating whether the predicted box intersects with the patient's lung segmentation at any position. The AI-based algorithm proved to have an overall sensitivity of 54.5% for central, of 81.9% for segmental and 80.0% for subsegmental PE if taking n = 20 candidate boxes into account. Depending on the localization of the pulmonary embolism, the detection rate for only one box was: 18.1% central, 34.7% segmental and 0.0% subsegmental. The median volume of the clots differed significantly between the three subgroups and was 846.5 mm3 (IQR:591.1-964.8) in central, 201.3 mm3 (IQR:98.3-390.9) in segmental and 110.6 mm3 (IQR:94.3-128.0) in subsegmental PA (p < 0.05). The new algorithm proved to have high sensitivity in detecting PE in particular in segmental/subsegmental localization and may guide to decide whether a second contrast enhanced CT is necessary.

Utilizing radiomics for differential diagnosis of inverted papilloma and chronic rhinosinusitis with polyps based on unenhanced CT scans

To evaluate whether radiomics models based on unenhanced paranasal sinuses CT images could be a useful tool for differentiating inverted papilloma (IP) from chronic rhinosinusitis with polyps (CRSwNP). This retrospective study recruited 240 patients with CRSwNP and 106 patients with IP from three centers. 253 patients from Qilu Hospital were randomly divided into the training set (n = 151) and the internal validation set (n = 102) with a ratio of 6:4. 93 patients from the other two centers were used as the external validation set. The patients with the unilateral disease (n = 115) from Qilu Hospital were selected to further develop a subgroup analysis. Lesion segmentation was manually delineated in CT images. Least absolute shrinkage and selection operator algorithm was performed for feature reduction and selection. Decision tree, support vector machine, random forest, and adaptive boosting regressor were employed to establish the differential diagnosis models. 43 radiomic features were selected for modeling. Among the models, RF achieved the best results, with an AUC of 0.998, 0.943, and 0.934 in the training set, the internal validation set, and the external validation set, respectively. In the subgroup analysis, RF achieved an AUC of 0.999 in the training set and 0.963 in the internal validation set. The proposed radiomics models offered a non-invasion and accurate differential approach between IP and CRSwNP and has some significance in guiding clinicians determining the best treatment plans, as well as predicting the prognosis.

Multi-reader multiparametric DECT study evaluating different strengths of iterative and deep learning-based image reconstruction techniques.

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). 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. 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. Multiparametric post-processed DECT datasets reconstructed with DLIR were preferred over ASIR-V images with DLIR-H yielding the highest image quality scores. Deep-learning image reconstruction in dual-energy CT demonstrated significant benefits in qualitative and quantitative image metrics compared to adaptive statistical iterative reconstruction-V. 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.

An unenhanced 3D-FLAIR sequence using long repetition time and constant flip angle to image endolymphatic hydrops.

To evaluate a three-dimensional fluid-attenuated inversion recovery (3D-FLAIR) sequence using a long repetition time (TR) and constant flip angle (CFA) in differentiating between perilymph and endolymph in a phantom study, and unenhanced endolymphatic hydrops (EH) imaging in a patient study. Three solutions in similar ion and protein concentrations with endolymph, perilymph, and cerebrospinal fluid were prepared for variable flip angle (VFA) 3D-FLAIR (TR 10,000 ms) and CFA (120°) 3D-FLAIR using different TR (10,000, 16,000, and 20,000 ms). Fifty-two patients with probable or definite Meniere's disease received unenhanced CFA (120°) 3D-FLAIR using a long TR (20,000 ms) and 4-h-delay enhanced CFA (120°) 3D-FLAIR (TR 16,000 ms). Image quality, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) of them were compared. Agreement in the evaluation of the EH degree between them was analyzed. In the phantom study, CNRs between perilymphatic and endolymphatic samples of VFA 3D-FLAIR (TR 10,000 ms) and CFA 3D-FLAIR (TR 10,000, 16,000, and 20,000 ms) were 6.66 ± 1.30, 17.90 ± 2.76, 23.87 ± 3.09, and 28.22 ± 3.15 (p < 0.001). In patient study, average score (3.65 ± 0.48 vs. 4.19 ± 0.40), SNR (34.56 ± 9.80 vs. 51.40 ± 11.27), and CNR (30.66 ± 10.55 vs. 45.08 ± 12.27) of unenhanced 3D-FLAIR were lower than enhanced 3D-FLAIR (p < 0.001). Evaluations of the two sequences showed excellent agreement in the cochlear and vestibule (Kappa value: 0.898 and 0.909). The CFA 3D-FLAIR sequence using a long TR could be used in unenhanced EH imaging with high accuracy. Unenhanced imaging of endolymphatic hydrops is valuable in the diagnosis and follow-up of patients, especially those who cannot receive contrast-enhanced MRI. Ion and protein concentration differences can be utilized in differentiating endolymph and perilymph on MRI. Endolymphatic and perilymphatic samples could be differentiated in vitro on this 3D-FLAIR sequence. This unenhanced 3D-FLAIR sequence is in excellent agreement with the enhanced constant flip angle 3D-FLAIR sequence.

Predicting of Hepatic Steatosis in Living Liver Donor via CT Liver Attenuation Index (LAI) and Fibroscan Controlled Attenuation Parameter (CAP) Correlation with Biopsy Result

Abstract Background The most prevalent persistent parenchymatous liver alterations in healthy individuals are thought to be hepatic steatosis. The liver biopsy is the most crucial procedure for the identification and measurement of hepatic steatosis. By identifying the liver attenuation index (LAI) at an unenhanced CT image, hepatic steatosis can be evaluated without the risk of liver resection. Objective Using liver biopsy histological analysis as a reference standard, to examine the precision of the CT liver attenuation index (LAI) and fibroscan controlled attenuation parameter (CAP) for quantitative evaluation of macro vesicular steatosis in living related liver donors. Methods In this study comparing CT liver attenuation index &amp; fibroscan controlled attenuation parameter with liver pathology of steatosis in subject's candidate for liver living donors 50 subjects in each two study's correlation with biopsy results, conducted at Ain Shams Shams Specialized hospital and other Private hospitals over about 2 years. Results Our study reported that liver – spleen attenuation index of 9 is the cutoff value with sensitivity 100% and specificity 80% that make it a very good method to exclude donor to have steatosis ≥15%, which mean if donor had LS index &amp;lt; 9 we can safely do proceed do liver biopsy. Our study reported that CAP measurement had an AUROC OF 0.780, for detecting steatosis ≥ 15 %, with sensitivity is only 60% with specificity as CT LAI of 80%, which consider low compared to other study, that could be due to small number of donors in out study with steatosis ≥ 15 % (5 case from 50 donors) unlike the other studies. Conclusion When used to estimate the amount of liver fat in liver donors, the examined CAP and CT indices worked equally. But according to multivariate analysis, the only factor strongly linked with hepatic steatosis in alive donors was the CT L-S attenuation index. We contend that the combination of CT L-S attenuation index and CAP allows for the detection of the degree of hepatic steatosis and can be used as an option to liver biopsy, reserving liver biopsy for those with positive steatosis donors.

Radiomics and visual analysis for predicting success of transplantation of heterotopic glioblastoma in mice with MRI

BackgroundQuantifying tumor growth and treatment response noninvasively poses a challenge to all experimental tumor models. The aim of our study was, to assess the value of quantitative and visual examination and radiomic feature analysis of high-resolution MR images of heterotopic glioblastoma xenografts in mice to determine tumor cell proliferation (TCP).MethodsHuman glioblastoma cells were injected subcutaneously into both flanks of immunodeficient mice and followed up on a 3 T MR scanner. Volumes and signal intensities were calculated. Visual assessment of the internal tumor structure was based on a scoring system. Radiomic feature analysis was performed using MaZda software. The results were correlated with histopathology and immunochemistry.Results21 tumors in 14 animals were analyzed. The volumes of xenografts with high TCP (H-TCP) increased, whereas those with low TCP (L-TCP) or no TCP (N-TCP) continued to decrease over time (p < 0.05). A low intensity rim (rim sign) on unenhanced T1-weighted images provided the highest diagnostic accuracy at visual analysis for assessing H-TCP (p < 0.05). Applying radiomic feature analysis, wavelet transform parameters were best for distinguishing between H-TCP and L-TCP / N-TCP (p < 0.05).ConclusionVisual and radiomic feature analysis of the internal structure of heterotopically implanted glioblastomas provide reproducible and quantifiable results to predict the success of transplantation.

Case Report - Sudden sensorineural hearing loss and vertigo due to inner ear hemorrhage

Sudden deafness is a common cause of emergency consultation in otology. Usually, despite investigations, no etiology is known. Intracochlear hemorrhage is a rare cause of sudden sensorineural hearing loss (SSNHL) and may be associated with various labyrinthine disorders. In such cases, magnetic resonance imaging (MRI) is the clef of the diagnosis. We report the case of a 70-year-old patient who was referred to our department for sudden hearing loss, tinnitus, and vertigo. Otoscopic and neurological examinations were normal, and pure-tone audiometry revealed left profound sensorineural hearing loss. Videonystagmography (VNG) revealed left vestibular deficit. MRI demonstrated a high signal intensity inside the cochlea on unenhanced T1-weighted images, and no other abnormalities were found; in particular, no enhancement after intravenous administration of gadolinium. No etiology was identified. Vertigo disappeared rapidly with corticosteroid treatment and hyperbaric oxygen therapy, but hearing did not improve. Intra-labyrinthine hemorrhage causing SSNHL is rare, and the hearing prognosis is poor.

Effect of patient characteristics on aortic attenuation in iodinated contrast-enhanced Abdominopelvic CT: A retrospective study

IntroductionContrast Enhanced Computed Tomography (CECT) abdomen and pelvis is a common imaging procedure. Hospitals typically follow fixed protocols of contrast volume administration for triple-phase CECT abdomen and pelvis scans and have found that patients are either underdosed or overdosed with respect to their body habitus. The aim of the study was to correlate different patient characteristics such as Total body weight (TBW), Lean Body Mass (LBM), Body Mass Index (BMI), Body Surface Area (BSA) and Blood Volume (BV) with aortic enhancement in the arterial and portal venous phases for CECT Abdomen and pelvis. MethodsA total of 106 patients who underwent triple-phase CECT abdomen & pelvis were retrospectively studied. A circular region-of-interest (ROI) of 100 mm2 was positioned on descending aorta for unenhanced, arterial, and portal venous phases to measure the aortic enhancement in Hounsfield's units. Measure of contrast attenuation (ΔH) was calculated from the difference of CT values on unenhanced images and contrast images. Correlation analysis was performed to evaluate the relation of patient body characteristics with aortic enhancement. ResultsCorrelation analysis revealed that BMI exhibited the least correlation when compared to the other characteristics in both arterial (r = −0.3; p = 0.002) and portovenous phases (r = −0.35; p < 0.001) whereas TBW, LBW, BSA and BV reported moderate inverse correlations. BV was found to be the strongest of all characteristics under linear regression. ConclusionThe study supports the use of protocols that adjust contrast volume to either TBW, LBW, BSA, or BV for CT abdomen and pelvis scan. Implication of practiceThe right body parameter ensures optimal contrast enhancement, improving the visualization of anatomical structures and helps in adapting tailored contrast injection protocols.

Update on Renal Cell Carcinoma Diagnosis with Novel Imaging Approaches.

This review highlights recent advances in renal cell carcinoma (RCC) imaging. It begins with dual-energy computed tomography (DECT), which has demonstrated a high diagnostic accuracy in the evaluation of renal masses. Several studies have suggested the potential benefits of iodine quantification, particularly for distinguishing low-attenuation, true enhancing solid masses from hyperdense cysts. By determining whether or not a renal mass is present, DECT could avoid the need for additional imaging studies, thereby reducing healthcare costs. DECT can also provide virtual unenhanced images, helping to reduce radiation exposure. The review then provides an update focusing on the advantages of multiparametric magnetic resonance (MR) imaging performance in the histological subtyping of RCC and in the differentiation of benign from malignant renal masses. A proposed standardized stepwise reading of images helps to identify clear cell RCC and papillary RCC with a high accuracy. Contrast-enhanced ultrasound may represent a promising diagnostic tool for the characterization of solid and cystic renal masses. Several combined pharmaceutical imaging strategies using both sestamibi and PSMA offer new opportunities in the diagnosis and staging of RCC, but their role in risk stratification needs to be evaluated. Although radiomics and tumor texture analysis are hampered by poor reproducibility and need standardization, they show promise in identifying new biomarkers for predicting tumor histology, clinical outcomes, overall survival, and the response to therapy. They have a wide range of potential applications but are still in the research phase. Artificial intelligence (AI) has shown encouraging results in tumor classification, grade, and prognosis. It is expected to play an important role in assessing the treatment response and advancing personalized medicine. The review then focuses on recently updated algorithms and guidelines. The Bosniak classification version 2019 incorporates MRI, precisely defines previously vague imaging terms, and allows a greater proportion of masses to be placed in lower-risk classes. Recent studies have reported an improved specificity of the higher-risk categories and better inter-reader agreement. The clear cell likelihood score, which adds standardization to the characterization of solid renal masses on MRI, has been validated in recent studies with high interobserver agreement. Finally, the review discusses the key imaging implications of the 2017 AUA guidelines for renal masses and localized renal cancer.

Diagnostic performance of unenhanced electrocardiogram-gated cardiac CT for detecting myocardial edema.

To assess the diagnostic performance of unenhanced electrocardiogram (ECG)-gated cardiac computed tomography (CT) for detecting myocardial edema, using MRI T2 mapping as the reference standard. This retrospective study protocol was approved by our institutional review board, which waived the requirement for written informed consent. Between December 2017 to February 2019, consecutive patients who had undergone T2 mapping for myocardial tissue characterization were identified. We excluded patients who did not undergo unenhanced ECG-gated cardiac CT within 3 months from MRI T2 mapping or who had poor CT image quality. All patients underwent unenhanced ECG-gated cardiac CT with an axial scan using a third-generation, 320 × 0.5 mm detector-row CT unit. Two radiologists together drew regions of interest (ROIs) in the interventricular septum on the unenhanced ECG-gated cardiac CT images. Using T2 mapping as the reference standard, the diagnostic performance of unenhanced cardiac CT for detecting myocardial edema was evaluated by using the area under the receiver operating characteristic curve with sensitivity and specificity. Youden index was used to find an optimal sensitivity-specificity cutoff point. A cardiovascular radiologist independently performed the measurements, and interobserver reliability was assessed using intraclass correlation coefficients for CT value measurements. A P value of <.05 was considered statistically significant. We included 257 patients who had undergone MRI T2 mapping. Of the 257 patients, 35 patients underwent unenhanced ECG-gated cardiac CT. One patient was excluded from the study because of poor CT image quality. Finally, 34 patients (23 men; age 64.7 ± 14.6 years) comprised our study group. Using T2 mapping, we identified myocardial edema in 19 patients. Mean CT and T2 values for 34 patients were 46.3 ± 2.7 Hounsfield unit and 49.0 ± 4.9 ms, respectively. Mean CT values moderately correlated with mean T2 values (Rho = -0.41; P < .05). Mean CT values provided a sensitivity of 63.2% and a specificity of 93.3% for detecting myocardial edema, with a cutoff value of ≤45.0 Hounsfield unit (area under the receiver operating characteristic curve = 0.77; P < .01). Inter-observer reproducibility in measuring mean CT values was excellent (intraclass correlation coefficient = 0.93; [95% confidence interval: 0.86, 0.96]). Myocardial edema could be detected by CT value of myocardium in unenhanced ECG-gated cardiac CT.

Extracellular volume-based scoring system for tracking tumor progression in pancreatic cancer patients receiving intraoperative radiotherapy

ObjectivesTo investigate the value of extracellular volume (ECV) derived from portal-venous phase (PVP) in predicting prognosis in locally advanced pancreatic cancer (LAPC) patients receiving intraoperative radiotherapy (IORT) with initial stable disease (SD) and to construct a risk-scoring system based on ECV and clinical-radiological features.Materials and methodsOne hundred and three patients with LAPC who received IORT demonstrating SD were enrolled and underwent multiphasic contrast-enhanced CT (CECT) before and after IORT. ECV maps were generated from unenhanced and PVP CT images. Clinical and CT imaging features were analyzed. The independent predictors of progression-free survival (PFS) determined by multivariate Cox regression model were used to construct the risk-scoring system. Time-dependent receiver operating characteristic (ROC) curve analysis and the Kaplan–Meier method were used to evaluate the predictive performance of the scoring system.ResultsMultivariable analysis revealed that ECV, rim-enhancement, peripancreatic fat infiltration, and carbohydrate antigen 19-9 (CA19-9) response were significant predictors of PFS (all p < 0.05). Time-dependent ROC of the risk-scoring system showed a satisfactory predictive performance for disease progression with area under the curve (AUC) all above 0.70. High-risk patients (risk score ≥ 2) progress significantly faster than low-risk patients (risk score < 2) (p < 0.001).ConclusionECV derived from PVP of conventional CECT was an independent predictor for progression in LAPC patients assessed as SD after IORT. The scoring system integrating ECV, radiological features, and CA19-9 response can be used as a practical tool for stratifying prognosis in these patients, assisting clinicians in developing an appropriate treatment approach.Critical relevance statementThe scoring system integrating ECV fraction, radiological features, and CA19-9 response can track tumor progression in patients with LAPC receiving IORT, aiding clinicians in choosing individual treatment strategies and improving their prognosis.Key PointsPredicting the progression of LAPC in patients receiving IORT is important.Our ECV-based scoring system can risk stratifying patients with initial SD.Appropriate prognostication can assist clinicians in developing appropriate treatment approaches.Graphical

Quantitative analysis of lung lesions using unenhanced chest computed tomography images.

Chest radiograph and computed tomography (CT) scans can accidentally reveal pulmonary nodules. Malignant and benign pulmonary nodules can be difficult to distinguish without specific imaging features, such as calcification, necrosis, and contrast enhancement. However, these lesions may exhibit different image texture characteristics which cannot be assessed visually. Thus, a computer-assisted quantitative method like histogram analysis (HA) of Hounsfield unit (HU) values can improve diagnostic accuracy, reducing the need for invasive biopsy. In this exploratory control study, nonenhanced chest CT images of 20 patients with benign (10) and cancerous (10) lesion were selected retrospectively. The appearances of benign and malignant lesions were very similar in chest CT images, and only pathology report was used to discriminate them. Free hand region of interest (ROI) was inserted inside the lesion for all slices of each lesion. Mean, minimum, maximum, and standard deviations of HU values were recorded and used to make HA. HA showed that the most malignant lesions have a mean HU value between 30 and 50, a maximum HU less than 150, and a minimum HU between -30 and 20. Lesions outside these ranges were mostly benign. Quantitative CT analysis may differentiate malignant from benign lesions without specific malignancy patterns on unenhanced chest CT image.

CT-based radiomics model using stability selection for predicting the World Health Organization/International Society of Urological Pathology grade of clear cell renal cell carcinoma.

This study aimed to develop a model to predict World Health Organization/International Society of Urological Pathology (WHO/ISUP) low-grade or high-grade clear cell renal cell carcinoma (ccRCC) using 3D multiphase enhanced CT radiomics features (RFs). CT data of 138 low-grade and 60 high-grade ccRCC cases were included. RFs were extracted from four CT phases: non-contrast phase (NCP), corticomedullary phase, nephrographic phase, and excretory phase (EP). Models were developed using various combinations of RFs and subjected to cross-validation. There were 107 RFs extracted from each phase of the CT images. The NCP-EP model had the best overall predictive value (AUC = 0.78), but did not significantly differ from that of the NCP model (AUC = 0.76). By considering the predictive ability of the model, the level of radiation exposure, and model simplicity, the overall best model was the Conventional image and clinical features (CICFs)-NCP model (AUC = 0.77; sensitivity 0.75, specificity 0.69, positive predictive value 0.85, negative predictive value 0.54, accuracy 0.73). The second-best model was the NCP model (AUC = 0.76). Combining clinical features with unenhanced CT images of the kidneys seems to be optimal for prediction of WHO/ISUP grade of ccRCC. This noninvasive method may assist in guiding more accurate treatment decisions for ccRCC. This study innovatively employed stability selection for RFs, enhancing model reliability. The CICFs-NCP model's simplicity and efficacy mark a significant advancement, offering a practical tool for clinical decision-making in ccRCC management.