Unenhanced CT Scans Research Articles

6966 A Rhythmic Life-Threatening Enigma Unveiled: KCNH2 Mutation-Associated Torsades de Points Unmasking Primary Hyperaldosteronism in Cardiological Realm

Abstract Disclosure: S. Dejprapasorn: None. N. Nopparatana: None. S. Soonthornpun: None. R. Leelawattana: None. N. Kietsiriroje: None. S. Limumpornpetch: None. T. Nilmoje: None. W. Lohawijarn: None. T. Wongsuttipakorn: None. P. Choochuen: None. P. Limumpornpetch: None. Introduction: Primary hyperaldosteronism (PA) is characterized by hypertension and hypokalemia. However, its ability to masquerade life-threatening Torsades de Pointes makes it an unusual and dismissed diagnosis, revealing the link between PA and serious arrythmia. Clinical Case A 30-year-old female experienced three episodes of palpitations, followed by syncope. Upon admission, her blood pressure was 147/87 mmHg. ECG showed prolonged QT intervals with potassium level was 2.6 mmol/L (3.5-5.0 mmol/L). Continuous cardiac monitoring captured Torsades de Pointes. Despite potassium replacement, long QT still persisted. Pathogenic variant, c.2592+1G>A, within KCNH2 was discovered. Aldosterone concentration showed 69.65 ng/dL (9.70-62.60 ng/dL) and plasma renin activity was 0.12 ng/mL/h (1.50-5.70 ng/mL/h). Aldosterone-to-renin ratio was 580. Aldosterone post 4-h saline infusion test was 31.71 ng/dL which were consistent with PA. Adrenal unenhanced CT scan identified bilateral adrenal nodules (right 1.3 cm, left 1.1 cm). After receiving a cardioverter-defibrillator and spironolactone, the patient has had no palpitations, and their potassium level has remained normal. Clinical lessons: PA rarely presents ventricular arrhythmia. KCNH2 mutation may link between long QT syndrome (LQTS) and PA arises. LQTS type 2 is attributable to KCNH2 mutations, impacting potassium channel subunits crucial for adjusting currents during sympathetic activation. This is essential for shortening the QT interval with increasing heart rate. KCNH2 mutations disrupt this process, prolonging the QT interval and heightening the risk of arrhythmias. Aldosterone synthesis relies on calcium signaling triggered by potassium currents. Adrenal cortex hosts approximately 90 potassium channels genes, including KCNJ5 and KCNH2. Mutations in gene can disrupt the calcium signaling cascade, leading to excessive aldosterone production. While KCNJ5 mutation is well-documented in PA, the potential correlation between KCNH2 mutations and a similar mechanism remains unexplored in clinical reports to date. This case highlights the importance of a thorough comprehension of genetic cause contributing to pathogenesis of PA with complex clinical presentations. Presentation: 6/1/2024

A CT-based machine learning model for using clinical-radiomics to predict malignant cerebral edema after stroke: a two-center study.

This research aimed to create a machine learning model for clinical-radiomics that utilizes unenhanced computed tomography images to assess the likelihood of malignant cerebral edema (MCE) in individuals suffering from acute ischemic stroke (AIS). The research included 179 consecutive patients with AIS from two different hospitals. These patients were randomly assigned to training (n = 143) and validation (n = 36) sets with an 8:2 ratio. Using 3DSlicer software, the radiomics features of regions impacted by infarction were derived from unenhanced CT scans. The radiomics features linked to MCE were pinpointed through a consistency test, Student's t test and the least absolute shrinkage and selection operator (LASSO) method for selecting features. Clinical parameters associated with MCE were also identified. Subsequently, machine learning models were constructed based on clinical, radiomics, and clinical-radiomics. Ultimately, the efficacy of these models was evaluated by measuring the operating characteristics of the subjects through their area under the curve (AUCs). Logistic regression (LR) was found to be the most effective machine learning algorithm, for forecasting the MCE. In the training and validation cohorts, the AUCs of clinical model were 0.836 and 0.773, respectively, for differentiating MCE patients; the AUCs of radiomics model were 0.849 and 0.818, respectively; the AUCs of clinical and radiomics model were 0.912 and 0.916, respectively. This model can assist in predicting MCE after acute ischemic stroke and can provide guidance for clinical treatment and prognostic assessment.

Predicting standardized uptake value of brown adipose tissue from CT scans using convolutional neural networks.

The standard method for identifying active Brown Adipose Tissue (BAT) is [18F]-Fluorodeoxyglucose ([18F]-FDG) PET/CT imaging, which is costly and exposes patients to radiation, making it impractical for population studies. These issues can be addressed with computational methods that predict [18F]-FDG uptake by BAT from CT; earlier population studies pave the way for developing such methods by showing some correlation between the Hounsfield Unit (HU) of BAT in CT and the corresponding [18F]-FDG uptake in PET. In this study, we propose training convolutional neural networks (CNNs) to predict [18F]-FDG uptake by BAT from unenhanced CT scans in the restricted regions that are likely to contain BAT. Using the Attention U-Net architecture, we perform experiments on datasets from four different cohorts, the largest study to date. We segment BAT regions using predicted [18F]-FDG uptake values, achieving 23% to 40% better accuracy than conventional CT thresholding. Additionally, BAT volumes computed from the segmentations distinguish the subjects with and without active BAT with an AUC of 0.8, compared to 0.6 for CT thresholding. These findings suggest CNNs can facilitate large-scale imaging studies more efficiently and cost-effectively using only CT.

Pulmonary embolism detection without intravenous contrast using electron density and Z-effective maps from dual-energy CT

Abstract Purpose This study aims to evaluate the feasibility of using electron density (ED) maps combined with Z-effective (Zeff) images obtained from unenhanced dual-layer dual-energy CT (dl-DECT) scans of the chest for the detection of pulmonary embolism (PE). Materials and methods A retrospective analysis was conducted on consecutive patients who underwent for contrast-enhanced chest CT (CECT) clinically suspected of PE or acute aortic syndrome. These scans were performed on a single dl-DECT scanner between October 2021 and November 2023. To distinguish emboli from circulating blood, color-coded maps were generated from the ED dataset superimposed on Zeff images, which were acquired from the unenhanced phase. Two radiologists with different levels of expertise independently assessed the presence of PE in the generated ED-Zeff maps, blinded to CECT results, which served as the reference standard. Diagnostic accuracy of ED-Zeff maps was assessed for each reader. Results The final study cohort comprised 150 patients, with 92 males (mean age: 68 ± 10 years, range: 47-93 years) and 58 females (mean age: 66 ± 15 years, range 38-89 years). ED-Zeff maps demonstrated high diagnostic performance, yielding accuracy, sensitivity, and specificity, respectively, of 86.67% (113/150, 95% CI, 80.16%-91.66%), 85% (17/20, 95% CI, 79.89%-92.19%), and 86.92% (113/130, 95% CI, 79.89%-92.19%). Ed-Zeff maps were able to identify PE in 85% of positive cases. Cohen’s kappa coefficient indicated excellent intra- and interobserver agreement (κ ≥ 0.9). Conclusion ED maps combined with Zeff images from unenhanced dl-DECT scans represent a feasible tool for detecting PE and may prove useful in evaluating patients with contraindications to iodinated contrast.

Could CT Radiomic Analysis of Benign Adrenal Incidentalomas Suggest the Need for Further Endocrinological Evaluation?

We studied the application of CT texture analysis in adrenal incidentalomas with baseline characteristics of benignity that are highly suggestive of adenoma to find whether there is a correlation between the extracted features and clinical data. Patients with hormonal hypersecretion may require medical attention, even if it does not cause any symptoms. A total of 206 patients affected by adrenal incidentaloma were retrospectively enrolled and divided into non-functioning adrenal adenomas (NFAIs, n = 115) and mild autonomous cortisol secretion (MACS, n = 91). A total of 136 texture parameters were extracted in the unenhanced phase for each volume of interest (VOI). Random Forest was used in the training and validation cohorts to test the accuracy of CT textural features and cortisol-related comorbidities in identifying MACS patients. Twelve parameters were retained in the Random Forest radiomic model, and in the validation cohort, a high specificity (81%) and positive predictive value (74%) were achieved. Notably, if the clinical data were added to the model, the results did not differ. Radiomic analysis of adrenal incidentalomas, in unenhanced CT scans, could screen with a good specificity those patients who will need a further endocrinological evaluation for mild autonomous cortisol secretion, regardless of the clinical information about the cortisol-related comorbidities.

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.

Accuracy and time efficiency of a novel deep learning algorithm for Intracranial Hemorrhage detection in CT Scans

PurposeTo evaluate a deep learning-based pipeline using a Dense-UNet architecture for the assessment of acute intracranial hemorrhage (ICH) on non-contrast computed tomography (NCCT) head scans after traumatic brain injury (TBI).Materials and methodsThis retrospective study was conducted using a prototype algorithm that evaluated 502 NCCT head scans with ICH in context of TBI.Four board-certified radiologists evaluated in consensus the CT scans to establish the standard of reference for hemorrhage presence and type of ICH. Consequently, all CT scans were independently analyzed by the algorithm and a board-certified radiologist to assess the presence and type of ICH. Additionally, the time to diagnosis was measured for both methods.ResultsA total of 405/502 patients presented ICH classified in the following types: intraparenchymal (n = 172); intraventricular (n = 26); subarachnoid (n = 163); subdural (n = 178); and epidural (n = 15). The algorithm showed high diagnostic accuracy (91.24%) for the assessment of ICH with a sensitivity of 90.37% and specificity of 94.85%. To distinguish the different ICH types, the algorithm had a sensitivity of 93.47% and a specificity of 99.79%, with an accuracy of 98.54%. To detect midline shift, the algorithm had a sensitivity of 100%. In terms of processing time, the algorithm was significantly faster compared to the radiologist’s time to first diagnosis (15.37 ± 1.85 vs 277 ± 14 s, p < 0.001).ConclusionA novel deep learning algorithm can provide high diagnostic accuracy for the identification and classification of ICH from unenhanced CT scans, combined with short processing times. This has the potential to assist and improve radiologists’ ICH assessment in NCCT scans, especially in emergency scenarios, when time efficiency is needed.

Radiomics signature for automatic hydronephrosis detection in unenhanced Low-Dose CT

PurposeTo investigate the diagnostic performance of an automatic pipeline for detection of hydronephrosis on kidney's parenchyma on unenhanced low-dose CT of the abdomen. MethodsThis retrospective study included 95 patients with confirmed unilateral hydronephrosis in an unenhanced low-dose CT of the abdomen. Data were split into training (n = 67) and test (n = 28) cohorts. Both kidneys for each case were included in further analyses, whereas the kidney without hydronephrosis was used as control. Using the training cohort, we developed a pipeline consisting of a deep-learning model for automatic segmentation (a Convolutional Neural Network based on nnU-Net architecture) of the kidney's parenchyma and a radiomics classifier to detect hydronephrosis. The models were assessed using standard classification metrics, such as area under the ROC curve (AUC), sensitivity and specificity, as well as semantic segmentation metrics, including Dice coefficient and Jaccard index. ResultsUsing manual segmentation of the kidney’s parenchyma, hydronephrosis can be detected with an AUC of 0.84, a sensitivity of 75% and a specificity of 82%, a PPV of 81% and a NPV of 77%. Automatic kidney segmentation achieved a mean Dice score of 0.87 and 0.91 for the right and left kidney, respectively. Additionally, automatic segmentation achieved an AUC of 0.83, a sensitivity of 86%, specificity of 64%, PPV of 71%, and NPV of 82%. ConclusionOur proposed radiomics signature using automatic kidney's parenchyma segmentation allows for accurate hydronephrosis detection on unenhanced low-dose CT scans of the abdomen independently of widened renal pelvis. This method could be used in clinical routine to highlight hydronephrosis to radiologists as well as clinicians, especially in patients with concurrent parapelvic cysts and might reduce time and costs associated with diagnosing hydronephrosis.

Impact of different peak tube voltage settings on adrenal adenomas attenuation at unenhanced CT.

To assess the influence of peak tube voltage peak setting on adrenal adenomas (AA) attenuation on unenhanced abdominal CT. IRB-approved retrospective observational cohort study. We included 89 patients with imaging-defined AAs with shortest diameter > 6 mm who underwent two or more unenhanced abdominal CTs using at least two different peak tube voltage settings. Two readers independently measured adenoma attenuation on different CT acquisitions by drawing a round ROI on 3 mm thick axial MPR reconstructions encompassing at least 2/3 of the lesion's surface. The mean of the values measured by the two readers was used for further analysis. Interobserver variability was assessed (Intraclass Correlation Coefficient). Attenuation values measured on 100, 110 and 140 kVp acquisitions were compared with standard 120 kVp ones (Bland-Altman analysis). We included 275 unenhanced abdominal CTs (3.1 ± 0.9/patient) in image analysis; 131 acquired at 120 kVp, 65 at 100 kVp, 59 at 110 kVp, and 20 at 140 kVp. 107 lesions were detected in 89 patients (1-4/patient), with a mean maximum diameter of 17 ± 6 mm. Interobserver agreement in attenuation measurement was excellent (ICC: 0.95, CI (92-97)). Median adenoma attenuation was significantly lower on 100 kVp images than on 120 kVp ones (-1 HU, IQR (-5 to 3.6), vs, 2.5 HU, IQR (-1.5 to 8.5); p < 0.001) whereas we didn't find statistically significant differences in adenoma attenuation between 110 kVp or 140 kVp and 120 kVp ones. AA attenuation is significantly lower on unenhanced CT scans acquired at 100 kVp than on those acquired at "standard" 120 kVp. AA attenuation is significantly lower at 100 kVp in comparison to 120 kVp. This might be exploited to increase unenhanced CT sensitivity in adenoma characterisation, but further studies including non-adenoma lesions are mandatory to confirm this hypothesis. CT scans are often acquired using peak tube voltage settings different from the "standard" 120 kVp. AA attenuation varies if CT scans are acquired using different tube peak voltage settings. At 100 kVp AAs show a significantly lower attenuation than at 120 kVp.

Systemic immune-inflammation index mediates the association between metabolic dysfunction-associated fatty liver disease and sub-clinical carotid atherosclerosis: a mediation analysis.

Limited research has been conducted to quantitatively assess the impact of systemic inflammation in metabolic dysfunction-associated fatty liver disease (MAFLD) and sub-clinical carotid atherosclerosis (SCAS). The systemic immune-inflammation index (SII), which integrates inflammatory cells, has emerged as a reliable measure of local immune response and systemic inflammation Therefore, this study aims to assess the mediating role of SII in the association between MAFLD and SCAS in type 2 diabetes mellitus (T2DM). This study prospectively recruited 830 participants with T2DM from two centers. Unenhanced abdominal CT scans were conducted to evaluate MAFLD, while B-mode carotid ultrasonography was performed to assess SCAS. Weighted binomial logistic regression analysis and restricted cubic splines (RCS) analyses were employed to analyze the association between the SII and the risk of MAFLD and SCAS. Mediation analysis was further carried out to explore the potential mediating effect of the SII on the association between MAFLD and SCAS. The prevalence of both MAFLD and SCAS significantly increased as the SII quartiles increased (P<0.05). MAFLD emerged as an independent factor for SCAS risk across three adjusted models, exhibiting odds ratios of 2.15 (95%CI: 1.31-3.53, P < 0.001). Additionally, increased SII quartiles and Ln (SII) displayed positive associations with the risk of MAFLD and SCAS (P < 0.05). Furthermore, a significant dose-response relationship was observed (P for trend <0.001). The RCS analyses revealed a linear correlation of Ln (SII) with SCAS and MAFLD risk (P for nonlinearity<0.05). Importantly, SII and ln (SII) acted as the mediators in the association between MAFLD and SCAS following adjustments for shared risk factors, demonstrating a proportion-mediated effect of 7.8% and 10.9%. SII was independently correlated with MAFLD and SCAS risk, while also acting as a mediator in the relationship between MAFLD and SCAS.

Can we screen opportunistically for low bone mineral density using CT scans of the shoulder and artificial intelligence?

To evaluate whether the CT attenuation of bones seen on shoulder CT scans could be used to predict low bone mineral density (BMD) (osteopenia/osteoporosis), and to compare the performance of two machine learning models to predict low BMD. In this study, we evaluated 194 patients aged 50 years or greater (69.2 ± 9.1 years; 170 females) who underwent unenhanced shoulder CT scans and dual-energy X-ray absorptiometry within 1 year of each other between January 1, 2010, and December 31, 2021. The CT attenuation of the humerus, glenoid, coracoid, acromion, clavicle, first, second, and third ribs was obtained using 3D-Slicer. Support vector machines (SVMs) and k-nearest neighbours (kNN) were used to predict low BMD. DeLong test was used to compare the areas under the curve (AUCs). A CT attenuation of 195.4 Hounsfield Units of the clavicle had a sensitivity of 0.577, specificity of 0.781, and AUC of 0.701 to predict low BMD. In the test dataset, the SVM had sensitivity of 0.686, specificity of 1.00, and AUC of 0.857, while the kNN model had sensitivity of 0.966, specificity of 0.200, and AUC of 0.583. The SVM was superior to the CT attenuation of the clavicle (P = .003) but not better than the kNN model (P = .098). The CT attenuation of the clavicle was best for predicting low BMD; however, a multivariable SVM was superior for predicting low BMD. SVM utilizing the CT attenuations at many sites was best for predicting low BMD.

Deep-learning segmentation to select liver parenchyma for categorizing hepatic steatosis on multinational chest CT

Unenhanced CT scans exhibit high specificity in detecting moderate-to-severe hepatic steatosis. Even though many CTs are scanned from health screening and various diagnostic contexts, their potential for hepatic steatosis detection has largely remained unexplored. The accuracy of previous methodologies has been limited by the inclusion of non-parenchymal liver regions. To overcome this limitation, we present a novel deep-learning (DL) based method tailored for the automatic selection of parenchymal portions in CT images. This innovative method automatically delineates circular regions for effectively detecting hepatic steatosis. We use 1,014 multinational CT images to develop a DL model for segmenting liver and selecting the parenchymal regions. The results demonstrate outstanding performance in both tasks. By excluding non-parenchymal portions, our DL-based method surpasses previous limitations, achieving radiologist-level accuracy in liver attenuation measurements and hepatic steatosis detection. To ensure the reproducibility, we have openly shared 1014 annotated CT images and the DL system codes. Our novel research contributes to the refinement the automated detection methodologies of hepatic steatosis on CT images, enhancing the accuracy and efficiency of healthcare screening processes.

Contrast-enhanced CT in the differential diagnosis of bladder cancer and paraganglioma.

We sought to summarize the value of contrast-enhanced computed tomography (CECT) in the differential diagnosis of bladder paraganglioma (BPG) and bladder cancer. The medical records of 19 patients with BPG (13 males, 6 females) and 56 patients with bladder cancer (49 males, 7 females) between November 2007 and June 2023 were retrospectively reviewed. All patients underwent unenhanced and contrast-enhanced CT scanning. Patient age (46.4 ± 11.1years vs. 58.6 ± 16.0years), tumor calcification (1/19 vs. 18/56), stalk (0/19 vs. 10/56), internal vessels (15/19 vs. 19/56) and the enlarged adjacent supplying artery (14/19 vs. 10/56) were significantly different between BPG and bladder cancer (P < 0.05). The CT value in the corticomedullary phase (92.4 ± 16.6HU vs. 64.0 ± 14.5HU) and the contrast-enhanced value in the corticomedullary phase (54.5 ± 17.4HU vs. 28.5 ± 12.8HU) were significantly greater in BPG patients than in bladder cancer patients (P < 0.001), with corresponding area under the curve values of 0.930 and 0.912, respectively. The optimal cutoff values were 83.2HU and 38.5HU, respectively. A CT value > 83.2HU in the corticomedullary phase and a contrast-enhanced CT value > 38.5 HU in the corticomedullary phase were used to indicate BPG with sensitivities of 78.9% and 89.5%, respectively, and specificities of 94.6% and 75.0%, respectively. The corticomedullary phase of CECT plays an important role in the preoperative differential diagnosis of BPG and bladder cancer.

Efficacy of Ultrasound in Detecting Renal Calculi Keeping Non-Enhanced Computed Tomography as a Reference Standard

Background: Renal calculi, are a prevalent health issue afflicting 10 to 15% of the world's population. Objective: The objective of this study was to compare ultrasonography and conventional (non-enhanced) CT in diagnosing kidney calculi. Methods: In 2020 at Gurki Trust Hospital in Lahore, 100 patients suspected of having kidney calculi were enrolled in a cross-sectional study employing ultrasonography and unenhanced CT scans. To assess the sensitivity and specificity of ultrasonography in identifying renal calculi, non-enhanced CT was used as the gold standard, and the presence or absence of renal calculi was recorded for each imaging modality. Results: Non-enhanced CT detected kidney calculi in 56 of 100 patients, whereas ultrasonography only detected them in 44. With a sensitivity of 78.6% and a specificity of 97.8%, ultrasonography was found to be highly effective at diagnosing kidney calculi. It had a predictive value of 97.7% for the positive and 80% for the negative. Conclusion: Our study found that ultrasound is an effective diagnostic tool for detecting renal calculi, with high specificity and moderate sensitivity compared to non-enhanced CT. Our findings suggest that ultrasound may be particularly useful in settings where non-enhanced CT is not readily available or is contraindicated. Further research is needed to determine the diagnosis and management of renal calculi.

Imaging of intracranial arterial disease: a comparison between MRI and unenhanced CT.

Arterial calcifications on unenhanced CT scans and vessel wall lesions on MRI are often used interchangeably to portray intracranial arterial disease. However, the extent of pathology depicted with each technique is unclear. We investigated the presence and distribution of these two imaging findings in patients with a history of cerebrovascular disease. We analyzed CT and MRI data from 78 patients admitted for stroke or TIA at our institution. Vessel wall lesions were assessed on 7 T MRI sequences, while arterial calcifications were assessed on CT scans. The number of vessel wall lesions, severity of intracranial internal carotid artery (iICA) calcifications, and overall presence and distribution of the two imaging findings were visually assessed in the intracranial arteries. At least one vessel wall lesion or arterial calcification was assessed in 69 (88%) patients. Only the iICA and vertebral arteries (VA) showed a substantial number of both calcifications and vessel wall lesions. The other vessels showed almost exclusively vessel wall lesions. The number of vessel wall lesions was associated with the severity of iICA calcification (p = 0.013). The number of vessel wall lesions increases with the severity of iICA calcifications. Nonetheless, the distribution of vessel wall lesions on MRI and arterial calcifications on CT shows remarkable differences. These findings support the need for a combined approach to examine intracranial arterial disease.

Ultrasound-Assisted Removal of a Wooden Foreign Body Embedded in the Neck

Objectives: The deep submucosal migration of ingested foreign bodies into the pharyngolaryngeal mucosa is a sporadic event, and its management can be very challenging. In the case of the failure of endoscopic retrieval, open surgical techniques are usually required, and intraoperative ultrasonography can become a useful adjunct for identifying their precise localization. Methods: An 84-year-old woman presented with new-onset dysphagia and odynophagia after the accidental ingestion of a fragment of a toothpick a few hours before in the absence of hoarseness or respiratory distress. Ultrasonography and an unenhanced CT scan of the neck revealed a 3 cm linear foreign body embedded into the neck between the left pyriform sinus and the esophageal wall. Results: We report the removal of a fragment of a wooden toothpick deeply lodged between the left pyriform sinus and the esophageal wall, which was managed via an open transcervical approach with the aid of intraoperative ultrasound guidance. Conclusions: We suggest that both preoperative and intraoperative ultrasonography should represent the first-line imaging technique for deeply embedded neck foreign bodies.

Comparison of Ultrasound and Unenhanced CT for diagnosis of Hepatic Steatosis

Background: Hepatic steatosis, characterized by pathologically increased fat deposition in the liver, is a growing health concern, with Non-Alcoholic Fatty Liver Disease (NAFLD) being the most common manifestation. This condition, which may escalate to cirrhosis and steatohepatitis, underscores the need for effective diagnostic strategies. Various non-invasive imaging techniques are pivotal in detecting hepatic steatosis (HS). Objective: The aim of this study was to compare the efficacy of ultrasound and unenhanced CT in the diagnosis of hepatic steatosis, to determine the most reliable non-invasive imaging technique for early detection. Methods: A cross-sectional study was conducted at the Radiological Department of Combined Military Hospital (CMH), Lahore, using a convenience sampling method from April to June 2023. The study involved 100 participants who underwent both ultrasound (USG) and CT scans. Data collection was facilitated through structured performa, and the analysis was executed using SPSS version 26. Results: The study diagnosed hepatic steatosis through both USG and CT scans. Ultrasound assessments revealed that 23% of patients had Grade I HS, 38% had Grade II, and 20% had Grade III. Notably, 19% of the ultrasound examinations showed no signs of HS. In contrast, CT imaging results indicated 24% of participants had no disease with an L/S index greater than 1, while 62% presented with an L/S index between 0.5 to 1, indicative of HS. A critical finding was that 14% were diagnosed with Grade III steatosis, progressing to cirrhosis with an L/S index of less than 0.5. Discrepancies between imaging modalities were highlighted as 20% of patients displayed smooth liver parenchyma on USG contrasted with irregular textures on CT scans. Conclusion: Ultrasound emerges as a viable initial imaging modality for diagnosing hepatic steatosis in early stages due to its accessibility and non-invasiveness. However, for precise quantification of liver fat content, unenhanced CT scans provide superior accuracy. Such insights direct clinicians towards a tailored approach in the management of HS.

Differential effect of atorvastatin and pravastatin on thoracic spine attenuation: A sub-analysis of a randomized clinical trial

BackgroundStatins reduce cardiovascular events and may improve bone mineral density. MethodsWe conducted a sub-analysis of a randomized clinical trial that investigated the differential effect of moderate vs intensive low-density lipoprotein cholesterol (LDL-C) lowering therapies on coronary artery calcium (CAC) scores, and used the acquired images to assess the change in radiological attenuation of selected thoracic vertebrae. Baseline and 12-month unenhanced chest CT scans were performed in 420 hyperlipidemic, postmenopausal women randomized to atorvastatin (ATV) 80 mg/day or pravastatin (PRV) 40 mg/day in the Beyond Endorsed Lipid Lowering with Electron Beam Tomography Scanning (BELLES) trial. Bone attenuation was measured in three contiguous thoracic vertebrae at baseline and 12 months. ResultsThere were no differences in baseline demographic and clinical characteristics between treatment arms. The median percent lowering (interquartile range) in LDL-C was significantly greater with ATV than PRV [-53 (-69 to 20)% vs −28 (-55 to 74)%, p < 0.001], although the CAC score change was similar [12 (-63 to 208)% vs 13 (-75 to 358)%; p = 0.44]. At follow-up, the median bone attenuation loss was significantly greater with PRV than with ATV [-2.6 (-27 to 11)% vs 0 (-11 to 25)%; p < 0.001]. The attenuation loss in the PRV group was comparable to that of a historical untreated general population sample. In the entire cohort, the changes in LDL-C and total cholesterol were inversely correlated with bone attenuation change (p < 0.01). In adjusted multivariable linear regression analyses, race and percent change in LDL-C were independent predictors of bone attenuation change. Age, body mass index, history of smoking, diabetes mellitus, hypertension, peripheral vascular disease, or hormone replacement therapy did not affect percent change in BMD. ConclusionsThese findings support the hypothesis that there is an interaction between bone and cardiometabolic health and that intensive lipid lowering has a beneficial effect on bone health.

The Impact of Semaglutide on Liver Fat Assessed by Serial Cardiac CT scans in Patients with Type 2 Diabetes: Results from STOP Trial

IntroductionThere is increasing prevalence of hepatic steatosis. Hepatic steatosis is increasingly recognized as the independent risk factor for cardiovascular mortality. However, there are limited options for the treatment of fatty liver. In this study we evaluated the effect of semaglutide on liver fat as measured by non-contrast cardiac CT scans. MethodsSTOP is a randomized controlled trial that evaluated semaglutide treatment effect on coronary atherosclerosis progression (STOP) in type 2 diabetes. We utilized unenhanced cardiac CT scans to quantify liver fat based on CT Hounsfield attenuation method. A total of 114 subjects qualified for this study of the 140 subjects randomized, 59 in semaglutide group and 55 in the placebo group and were followed for 12 months. Multivariate regression models were used to evaluate the change in liver fat content overtime. Results114 subjects were included in the study, 61% male, mean age of 57.8±8.1 years and mean BMI of 32.0±6.7. The average of the three liver measures showed an improvement in the semaglutide group 1.4±9.0 versus worsening in the placebo group of 1.9±9.5 mean HU. The multivariable linear regression models including age, gender, BMI, hypertension, hyperlipidemia, past smoking and baseline liver attenuation, showed that the semaglutide group average liver attenuation measures improved by 4.4 HU versus placebo across the trial, p=0.002 indicating improvement in the liver fat content in treatment group. ConclusionIn type 2 diabetes patients with hepatic steatosis, treatment with semaglutide resulted in a significant improvement in fatty liver resolution compared to placebo.

Effects of liraglutide on intrapancreatic fat deposition in patients with type 2 diabetes

Ectopic fat deposition is associated with worsening of glycemic control. This study was conducted to determine whether liraglutide reduces ectopic fat deposition, especially in pancreas, in patients with type 2 diabetes (T2D). We retrospectively recruited T2D patients who underwent abdominal unenhanced CT scans both before and after administration of liraglutide (N=13) or glimepiride (N=29). Using CT values of pancreas (P), liver (L) and spleen (S), we defined the indices of intrapancreatic and liver fat as P-S value and L-S value, respectively. Increase of each value suggests the reduction of each fat deposition. The values of HbA1c (p=0.0017) and body weight (p=0.0081) decreased, and L-S (p=0.0024) increased significantly after administration of liraglutide compared with those at baseline. Similarly, P-S tended to increase in the liraglutide group (p=0.0547) and increased significantly in the liraglutide subgroup with fatty pancreas (p=0.0303), defined as having baseline P-S less than-5. In the glimepiride group, P-S did not increase regardless of baseline P-S. Among patients with fatty pancreas, administration of liraglutide tended to be a significant factor for the change in P-S after adjustment for the change in HbA1c (p=0.1090) and the change in visceral fat area (p=0.1030). Intrapancreatic fat deposition was decreased after treatment with liraglutide, but not glimepiride, in T2D patients with fatty pancreas. Liraglutide might reduce intrapancreatic fat deposition independently of decreases in HbA1c and visceral fat volume.