CT Images Of Patients Research Articles

Development and validation of fully automated robust deep learning models for multi-organ segmentation from whole-body CT images.

This study aimed to develop a deep-learning framework to generate multi-organ masks from CT images in adult and pediatric patients. A dataset consisting of 4082 CT images and ground-truth manual segmentation from various databases, including 300 pediatric cases, were collected. In strategy#1, the manual segmentation masks provided by public databases were split into training (90%) and testing (10% of each database named subset #1) cohort. The training set was used to train multiple nnU-Net networks in five-fold cross-validation (CV) for 26 separate organs. In the next step, the trained models from strategy #1 were used to generate missing organs for the entire dataset. This generated data was then used to train a multi-organ nnU-Net segmentation model in a five-fold CV (strategy#2). Models' performance were evaluated in terms of Dice coefficient (DSC) and other well-established image segmentation metrics. The lowest CV DSC for strategy#1 was 0.804±0.094 for adrenal glands while average DSC > 0.90 were achieved for 17/26 organs. The lowest DSC for strategy#2 (0.833±0.177) was obtained for the pancreas, whereas DSC > 0.90 was achieved for 13/19 of the organs. For all mutual organs included in subset #1 and subset #2, our model outperformed the TotalSegmentator models in both strategies. In addition, our models outperformed the TotalSegmentator models on subset #3. Our model was trained on images with significant variability from different databases, producing acceptable results on both pediatric and adult cases, making it well-suited for implementation in clinical setting.

Deep learning and machine learning in CT-based COPD diagnosis: Systematic review and meta-analysis.

With advancements in medical technology and science, chronic obstructive pulmonary disease (COPD), one of the world's three major chronic diseases, has seen numerous remarkable outcomes when combined with artificial intelligence, particularly in disease diagnosis. However, the diagnostic performance of these AI models still lacks comprehensive evidence. Therefore, this study quantitatively analyzed the diagnostic performance of AI models in CT images of COPD patients, aiming to promote the development of related research in the future. PubMed, Cochrane Library, Web of Science, and Embase were retrieved up to September 1, 2024. The QUADAS-2 evaluation tool was used to assess the quality of the included studies. Meta-analysis of the included researches was performed using Stata18, RevMan 5.4, and Meta-Disc 1.4 software to merge sensitivity, specificity and plot a summary receiver operating characteristic curve (SROC). Heterogeneity was assessed using the Q statistic, and sources of inter-study heterogeneity were explored through meta-regression analysis. Twenty-two of 3280 identified studies were eligible. Meta-analysis was performed on 15 of these studies, encompassing a total of 22,817 patients for which statistical metrics were reported or could be calculated. Seven studies were based on deep learning (DL) model, three on machine learning (ML) model, and five on DL model with multiple-instance learning (MIL) mechanisms. One study evaluated both DL and ML models. The meta-analysis results showed that the pooled sensitivity of all DL and ML models was 86% (95%CI 78-91%), specificity was 87% (95%CI 83-91%), and area under the curve was 93% (95%CI 90-95%). Subgroup analyses revealed no significant difference in diagnostic sensitivity and specificity between DL and ML models (sensitivity 82% (95%CI 76-87%), 93% (95%CI 85-97%); specificity 87% (95%CI 79-91%), 84% (95%CI 79-88%), and the DL model with MIL (sensitivity 87% (95%CI 61-96%); specificity 89% (95%CI 78-95%) improved the performance of DL model, but this improvement was not statistically significant (p>0.05). Both DL and ML models for diagnosing COPD using CT images exhibited high accuracy. There was no significant difference in diagnostic efficacy between the two types of AI models, and the addition of the MIL mechanism may enhance the performance of the DL model.

Multi-scale channel attention U-Net: a novel framework for automated gallbladder segmentation in medical imaging

ObjectivesTo develop a novel automatic delineation model, the Multi-Scale Channel Attention U-Net (MCAU-Net) model, for gallbladder segmentation on CT images of patients with liver cancer.MethodsWe retrospectively collected the CT images from 120 patients with liver cancer, based on which ground truth was manually delineated by physicians. The images and ground truth constitute a dataset, which was proportionally divided into a training set (54%), a validation set (6%), and a test set (40%). Data augmentation was performed on the training set. Our proposed MCAU-Net model was employed for gallbladder segmentation and its performance was evaluated using Dice Similarity Coefficient (DSC), Jaccard Similarity Coefficient (JSC), Positive Predictive Value (PPV), Sensitivity (SE), Hausdorff Distance (HD), Relative Volume Difference (RVD), and Volumetric Overlap Error (VOE) metrics.ResultsOn the test set, MCAU-Net achieved DSC, JSC, PPV, SE, HD, RVD, and VOE values of 0.85 ± 0.22, 0.79 ± 0.23, 0.92 ± 0.14, 0.84 ± 0.23, 2.75 ± 0.98, 0.18 ± 0.48, and 0.22 ± 0.42, respectively. Compared to the control models, U-Net, SEU-Net and TransUNet, the MCAU-Net improved DSC 0.06, 0.04 and 0.06, JSC by 0.09, 0.06 and 0.09, PPV by 0.08, 0.08 and 0.05, SE by 0.05,0.05 and 0.07, and reduced HD by 0.45, 0.28 and 0.41, RVD by 0.07, 0.03 and 0.07, VOE by 0.04, 0.02 and 0.08 respectively. Qualitative results revealed that MCAU-Net produced smoother and more accurate boundaries, closer to the expert delineation, with less over-segmentation and under-segmentation and improved robustness.ConclusionsThe MCAU-Net model significantly improves gallbladder segmentation on CT images. It satisfies clinical requirements and enhances the efficiency of physicians, particularly in segmenting complex anatomical structures.

Deep learning-based Monte Carlo dose prediction for heavy-ion online adaptive radiotherapy and fast quality assurance: A feasibility study.

Online adaptive radiotherapy (OART) and rapid quality assurance (QA) are essential for effective heavy ion therapy (HIT). However, there is a shortage of deep learning (DL) models and workflows for predicting Monte Carlo (MC) doses in such treatments. This study seeks to address this gap by developing a DL model for independent MC dose (MCDose) prediction, aiming to facilitate OART and rapid QA implementation for HIT. A MC dose prediction DL model called CAM-CHD U-Net for HIT was introduced, based on the GATE/Geant4 MC simulation platform. The proposed model improved upon the original CHD U-Net by adding a Channel Attention Mechanism (CAM). Two experiments were conducted, one with CHD U-Net (Experiment 1) and another with CAM-CHD U-Net (Experiment 2), and involved data from 120 head and neck cancer patients. Using patient CT images, three-dimensional energy matrices, and ray-masks as inputs, the model completed the entire MC dose prediction process within a few seconds. In Experiment 2, within the Planned Target Volume (PTV) region, the average gamma passing rate (3%/3mm) between the predicted dose and true MC dose reached 99.31%, and 96.48% across all body voxels. Experiment 2 demonstrated a 46.15% reduction in the mean absolute difference in in organs at risk compared to Experiment 1. By extracting relevant parameters of radiotherapy plans, the CAM-CHD U-Net model can directly and accurately predict independent MC dose, and has a high gamma passing rate with the ground truth dose (the dose obtained after a complete MC simulation). Our workflow enables the implementation of heavy ion OART, and the predicted MCDose can be used for rapid QA of HIT.

Assessing the role of chest CT in minor blunt trauma: evaluation of the NEXUS decision instrument across an expanded population.

To evaluate the NEXUS Chest CT ALL decision instrument (DI) in reducing unnecessary chest CT imaging in minor blunt trauma patients while preserving high sensitivity for detecting clinically meaningful injuries. Additionally, we examined the impact of delayed presentation, chronic disease, and anticoagulation/anti-aggregation medications on trauma outcomes. This retrospective study included 853 adult minor blunt trauma patients who underwent chest CT in the emergency department (ED) of Tel-Aviv Sourasky Medical Center between 2018 and 2022. Clinically meaningful outcomes were defined as trauma-related interventions or hospital admissions. The NEXUS Chest CT DI criteria, along with three additional criteria, were analyzed using logistic regression to identify independent predictors for the primary outcome. These predictors formed a modified DI, and its performance was compared to the original NEXUS DI. Among 853 patients (median age 44.5years, 64.2% male), 230 (27.0%) had trauma-related chest CT findings, and 64 (7.5%) experienced clinically meaningful outcomes. Independent predictors included abnormal chest X-ray (aOR 6.5, p < 0.001), chronic disease (aOR 5.2, p < 0.001), sternal tenderness (aOR 4.7, p = 0.007), rapid deceleration (aOR 3.7, p < 0.001), and chest wall tenderness (aOR 3.1, p < 0.001). The NEXUS DI achieved 92.1% sensitivity, reducing imaging by 41.3%, while the modified DI increased sensitivity to 98.4% with a 34.3% imaging reduction. The NEXUS Chest CT ALL DI significantly reduces unnecessary imaging while maintaining high diagnostic precision. A modified version enhances sensitivity, refining decision-making in emergency care. Integrating such decision tools, particularly in cases of minor trauma, is highly recommended to optimize resource use and improve patient outcomes.

Assessing the subarachnoid space anatomy on clinical imaging: utilizing normal and pathology to understand compartmentalization of the subarachnoid space

BackgroundThe goal of the study is to use CT imaging in patients with aSAH to evaluate the anatomic distribution of hemorrhage and compartmentalization of subarachnoid space to investigate potential in vivo visualization of recently discovered layer named subarachnoid lymphatic-like membrane (SLYM).MethodsWe conducted a retrospective cohort study of cases with aneurysmal SAH (aSAH) at our institution between January 2015 and June 2022. Subarachnoid hemorrhage distribution into superficial and deep subarachnoid spaces was classified based on proximity to the dural or pial surfaces, respectively, as seen on multiplanar CT head.ResultsA total of 97 patients with aSAH were included. Patients with lower modified Fisher score (MFS) of 1-2 were more likely to have SAH compartmentalizing in the “deep” pial-adjacent subarachnoid space. Patients with higher MFS of 3-4 were more likely to have SAH in both “superficial” and “deep” compartments along the brainstem. There is a significant association between the severity of aSAH - quantified by the MFS - and the distribution of the blood. Patients with higher MFS scores were roughly 7.6 times (p-value = 0.049) more likely to have hemorrhage at the “Superficial” juxta-dural subarachnoid compartment when compared to those with lower MFS scores.ConclusionThis study suggests an imaging correlate to the recently discovered “SLYM”, potentially influencing aSAH compartmentalization, particularly in low-grade bleeds. While compartmentalization is limited in high grade cases, these findings warrant further investigation with advanced imaging techniques to validate this membrane’s role and potential impact on CSF flow and aSAH pathophysiology.

AAPM Truth-based CT (TrueCT) reconstruction grand challenge.

This Special Report summarizes the 2022, AAPM grand challenge on Truth-based CT image reconstruction. To provide an objective framework for evaluating CT reconstruction methods using virtual imaging resources consisting of a library of simulated CT projection images of a population of human models with various diseases. Two hundred unique anthropomorphic, computational models were created with varied diseases consisting of 67 emphysema, 67 lung lesions, and 66 liver lesions. The organs were modeled based on clinical CT images of real patients. The emphysematous regions were modeled using segmentations from patient CT cases in the COPDGene Phase I dataset. For the lung and liver lesion cases, 1-6 malignant lesions were created and inserted into the human models, with lesion diameters ranging from 5.6 to 21.9mm for lung lesions and 3.9 to 14.9mm for liver lesions. The contrast defined between the liver lesions and liver parenchyma was 82±12 HU, ranging from 50 to 110 HU. Similarly, the contrast between the lung lesions and the lung parenchyma was defined as 781±11 HU, ranging from 725 to 805 HU. For the emphysematous regions, the defined HU values were -950±17 HU ranging from -918 to -979 HU. The developed human models were imaged with a validated CT simulator. The resulting CT sinograms were shared with the participants. The participants reconstructed CT images from the sinograms and sent back their reconstructed images. The reconstructed images were then scored by comparing the results against the corresponding ground truth values. The scores included both task-generic (root mean square error [RMSE] and structural similarity matrix [SSIM]), and task-specific (detectability index [d'] and lesion volume accuracy) metrics. For the cases with multiple lesions, the measured metric was averaged across all the lesions. To combine the metrics with each other, each metric was normalized to a range of 0 to 1 per disease type, with "0" and "1" being the worst and best measured values across all cases of the disease type for all received reconstructions. The True-CT challenge attracted 52 participants, out of which 5 successfully completed the challenge and submitted the requested 200 reconstructions. Across all participants and disease types, SSIM absolute values ranged from 0.22 to 0.90, RMSE from 77.6 to 490.5 HU, d' from 0.1 to 64.6, and volume accuracy ranged from 1.2 to 753.1mm3. The overall scores demonstrated that participant "A" had the best performance in all categories, except for the metrics of d' for lung lesions and RMSE for liver lesions. Participant "A" had an average normalized score of 0.41±0.22, 0.48±0.32, and 0.42±0.33 for the emphysema, lung lesion, and liver lesion cases, respectively. The True-CT challenge successfully enabled objective assessment of CT reconstructions with the unique advantage of access to a diverse population of diseased human models with known ground truth. This study highlights the significant potential of virtual imaging trials in objective assessment of medical imaging technologies.

Abdominal synthetic CT generation for MR-only radiotherapy using structure-conserving loss and transformer-based cycle-GAN.

Recent deep-learning based synthetic computed tomography (sCT) generation using magnetic resonance (MR) images have shown promising results. However, generating sCT for the abdominal region poses challenges due to the patient motion, including respiration and peristalsis. To address these challenges, this study investigated an unsupervised learning approach using a transformer-based cycle-GAN with structure-preserving loss for abdominal cancer patients. A total of 120 T2 MR images scanned by 1.5 T Unity MR-Linac and their corresponding CT images for abdominal cancer patient were collected. Patient data were aligned using rigid registration. The study employed a cycle-GAN architecture, incorporating the modified Swin-UNETR as a generator. Modality-independent neighborhood descriptor (MIND) loss was used for geometric consistency. Image quality was compared between sCT and planning CT, using metrics including mean absolute error (MAE), peak signal-to-noise ratio (PSNR), structure similarity index measure (SSIM) and Kullback-Leibler (KL) divergence. Dosimetric evaluation was evaluated between sCT and planning CT, using gamma analysis and relative dose volume histogram differences for each organ-at-risks, utilizing treatment plan. A comparison study was conducted between original, Swin-UNETR-only, MIND-only, and proposed cycle-GAN. The MAE, PSNR, SSIM and KL divergence of original cycle-GAN and proposed method were 86.1 HU, 26.48 dB, 0.828, 0.448 and 79.52 HU, 27.05 dB, 0.845, 0.230, respectively. The MAE and PSNR were statistically significant. The global gamma passing rates of the proposed method at 1%/1 mm, 2%/2 mm, and 3%/3 mm were 86.1 ± 5.9%, 97.1 ± 2.7%, and 98.9 ± 1.0%, respectively. The proposed method significantly improves image metric of sCT for the abdomen patients than original cycle-GAN. Local gamma analysis was slightly higher for proposed method. This study showed the improvement of sCT using transformer and structure preserving loss even with the complex anatomy of the abdomen.

A Case Report of Contrasting Pre- and Post-Emergency Percutaneous Catheter Drainage CT Images in Patients Having Trauma with Severe Abdominal Compartment Syndrome: A Valuable Tool for Identifying Hemorrhage Sources

A Case Report of Contrasting Pre- and Post-Emergency Percutaneous Catheter Drainage CT Images in Patients Having Trauma with Severe Abdominal Compartment Syndrome: A Valuable Tool for Identifying Hemorrhage Sources

Analysis of the hemodynamic impact of coronary plaque morphology in mild coronary artery stenosis.

As is well known, plaque morphology plays an important role in the hemodynamics of stenotic coronary arteries, thus their clinic outcomes. However, so far, there has been no research on how the cross-sectional shape of a stenotic lumen affects its hemodynamics. Therefore, this study aims to explore the impact of plaque cross-sectional shape on coronary hemodynamics under mild or moderate stenosis conditions (diameter stenosis degree ≤50 %). A three-dimensional model of the coronary tree was established using CT images of a subject without coronary stenosis. Based on real CT images of patients, six types of plaque cross-sectional morphologies were created at the same location in one main left coronary artery model, controlling for 50 % and 25 % diameter stenosis, respectively. Computational fluid dynamics (CFD) simulations were performed on the six stenosed coronary models and one normal control model under the same boundary conditions. The differences in hemodynamic results among the models were compared. (1) Type III plaque caused the largest disturbance in the flow field. (2) In type IV plaque, the area with an oscillatory shear index (OSI) >0.1 accounted for 11.18 %. (3) Type V plaque exhibited the most prominent vortex flow lines. (4) Hemodynamic parameters within type VI plaques were most similar to those of normal coronary arteries. (5) Area stenosis better reflects the severity of coronary stenosis. Different cross-sectional morphologies can lead to abnormalities in different hemodynamic parameters, leading to different clinical outcomes. Especially, type III plaques are most likely to cause vascular wall damage, while type V plaques warrant caution due to the risk of complications such as thrombosis. Considering plaque cross-sectional morphology can provide doctors with more information and theoretical support for diagnosis.

Effects of Inlet Geometry on Perfusion and Ischemia in Anomalous Aortic Origin of the Right Coronary Artery (AAORCA).

Anomalous aortic origin of a coronary artery (AAOCA) comprises a set of rare congenital abnormalities in the origin or path of the coronary arteries with highly variable clinical implications. This is a pilot feasibility study where we investigated the influence of the anomalous coronary artery inlet architecture on coronary perfusion using coronary blood flow computational simulations to help predict the risk for coronary ischemia in patients with anomalous aortic origin of the right coronary artery (AAORCA) with these types of anomalous coronary artery inlet architectures. We developed a protocol for generating 3D models of patient coronary artery anatomies from an IRB-approved dataset of cardiac CT images of patients with AAORCA at our institution. Coronary blood flow simulations and analysis were performed. Instantaneous flow reserve (iFR), a parameter used clinically in coronary CT analysis to determine risk for ischemia, was compared between models as a measure of ischemia. Comparing the median iFR of the coronary outlets between the four inlet variants and baseline architecture showed important differences. We observed a possible association between the proportion of the semi-minor axis and the semi-major axis of the elliptical AAORCA inlet and iFR. These observations suggest that the elliptical axis quotient may be a significant risk factor for evaluation of AAORCA severity.

An Enhanced U-Net Model for Segmenting CT Images of COVID-19 Patients

In the context of the global COVID-19 pandemic, medical imaging technology has played a crucial role in the diagnosis and treatment of the disease. Accurate segmentation of lesion areas in CT images is critical for assessing the condition and formulating treatment plans. This paper first outlines the importance of medical image segmentation. It then delves into the main challenges faced in image segmentation for COVID-19 diagnosis, including the diversity of lesions, inconsistencies in image quality, and the need for real-time processing. Following this discussion, the paper reviews existing medical image segmentation models, encompassing traditional methods such as watershed and threshold segmentation, as well as advanced deep learning models like U-Net and RCNN. Building on this foundation, the paper proposes an improved image segmentation framework aimed at enhancing the accuracy and processing speed of lesion area segmentation. The goal is to provide a more reliable decision support tool for clinical practice.

A Novel Approach to Diagnosis: Using Convolutional Neural Networks to Classify Non-Small Cell Lung Cancers on CT Scans

Background: Lung cancer is the leading cause of cancer-related deaths around the world, making early detection vital to treatment and survival. However, lung cancer has variable clinical presentations, so diagnosis, even by trained medical professionals, is challenging and time- consuming. Deep Learning (DL) has proven effective in training big data to generate accurate diagnoses through classification in a timely manner. Aim and Objective: This study uses data augmentation and hyperparameter optimization methods on convolutional neural networks (CNN) to understand the benefits of various architectures in addition to creating an accurate transfer-based tool for lung cancer diagnosis on CT scans. Materials and Methods: We found a dataset composed of chest CT images of patients with non-small cell lung cancers (NSCLC) categorized into adenocarcinoma, large cell carcinoma, and squamous cell carcinoma and a control group of normal chest CT scans. We tested the CNN architectures VGG16, InceptionV3, ResNet50, and EfficientNetB0 for feature extraction and observed each model on a variety of different metrics such as validation accuracy, statistical errors, and learning rate. Each model was trained on the prechosen data set 2 times at 6 and 12 epochs and several evaluation metrics were recorded. Observation and Results: EfficientNetB0, ResNet50, VGG16, InceptionV3 achieved test accuracies of 98.92%, 91.40%, 77.42%, 70.97% respectively when implemented with hyperparameter tuning and dropout layers. EfficientNetB0 also exceeded the other architectures in metrics such as precision, recall, and F1-score. Conclusion: Convolutional neural networks using EfficientNetB0 yielded a higher accuracy in diagnosis of non-small cell cancer on CT scan which encourages further research in developing robust diagnostic tools using DL to expedite diagnosis, especially in locations with inadequate healthcare resources.

Potential relationship among CT and MRI imaging, histological findings, and treatment outcomes in patients with medication-related osteonecrosis of the jaw undergoing segmental mandibulectomy–A retrospectives study

Potential relationship among CT and MRI imaging, histological findings, and treatment outcomes in patients with medication-related osteonecrosis of the jaw undergoing segmental mandibulectomy–A retrospectives study

LINC01004/hsa-mir-125b-2-3p axis restrains ferroptosis in hepatocellular carcinoma by targeting HSPA4 via ceRNA mechanism

Purpose To investigate the latent mechanisms of hepatocellular carcinoma (HCC) and find therapeutic targets. Methods Differentially expressed and overall survival related genes of HCC, and cell death related genes were intersected to obtain latent target genes. These genes were analyzed using ROC curve for diagnosing HCC. RT-qPCR and Western blot were performed to detect the expression level of genes. Wound healing tests were performed with or without si-HSPA4. Potential ceRNA axis was forecasted using TargetScan and miRanda and the dual luciferase reporter gene assay was used to verify the results. Finally, the images of H&amp;E dye liquor-stained HCC tissue section, the CT images for patients in different tumor stage. Results LINC01004/hsa-miR-125b-2-3p/HSPA4 axis was forecasted and then was verified using dual-luciferase reporter assay. HSPA4 knockdown caused significant reduction of cell proliferation and ferroptosis. Si-HSPA4 related ferroptosis was generated through impairing iron transport via targeting restrain GPX4. For human subjects, the RT-qPCR analysis revealed the that the larger the tumor diameter, the higher the LINC01004, HSPA4, and GPX4 expression, and the lower the hsa-miR-125b-2-3p expression. Conclusion LINC01004/hsa-miR-125b-2-3p/HSPA4 regulatory axis involved in the ferroptosis of the progression of HCC via GPX4 dependent method, providing new therapeutic targets for HCC patients.

Radiomic features based automatic classification of CT lung findings for COVID-19 patients

Introduction. The lung CT images of COVID-19 patients can be typically characterized by three different findings- Ground Glass Opacity (GGO), consolidation and pleural effusion. GGOs have been shown to precede consolidations and has different heterogeneous appearance. Conventional severity scoring only uses total area of lung involvement ignoring appearance of the effected regions. This study proposes a baseline to select heterogeneity/radiomic features that can distinguish these three pathological lung findings.Methods. Four approaches were implemented to select features from a pool of 44 features. First one is a manual feature selection method. The rest are automatic feature selection methods based on Genetic Algorithm (GA) coupled with (1) K-Nearest-Neighbor (GA-KNN), (2) binary-decision-tree (GA-BDT) and (3) Artificial-Neural-Network (GA-ANN). For the purpose of validation, an ANN was trained using the selected features and tested on a completely independent data set.Results. Manual selection of nine radiomic features was found to provide the most accurate results with the highest sensitivity, specificity and accuracy (85.7% overall accuracy and 0.90 area under receiver operating characteristic curve) followed by GA-BDT, GA-KNN and GA-ANN (accuracy 78%, 77.5% and 76.8%).Conclusion. Manually selected nine radiomic features can be used in accurate severity scoring allowing the clinician to plan for more effective personalized treatment. They can also be useful for monitoring the progression of COVID-19 and response to therapy for clinical trials.

EUS-navigovaná radiofrekvenční ablace v pankreatu

Incident cystic lesions of the pancreas are diagnosed with increased frequency due to increased use of CT or MR imaging in asymptomatic patients who undergo imaging for other reasons. Certain lesions such as neuroendocrine tumors, mucinous cystadenomas, and intraductal papillary mucinous neoplasia are at significant risk of the presence or development of malignancy. Endosonographically guided radiofrequency ablation allows selective ablation of the pancreatic lesion with minimal damage to surrounding tissue and could be an alternative treatment modality for patients who do not want or cannot undergo surgery or do not wish to have a long-term follow-up. Based on the available pilot studies, EUS-RFA appears to be a promising technically feasible method for the treatment of pancreatic neoplasia, which has demonstrated high technical and acceptable clinical success rates with low morbidity. This communication offers a clear summary of the use of EUS-RFA in solid and cystic pancreatic neoplasia concluding with two case reports of patients. In both cases, endoscopic treatment was technically feasible and no periprocedural or postprocedural complications occurred. Key words: radiofrequency ablation – pancreas – neuroendocrine tumour – pancreatic cystic lesion

Early pulmonary fibrosis-like changes between delta and pre-delta periods in patients with severe COVID-19 pneumonia on mechanical ventilation

It remains unclear whether pulmonary fibrosis-like changes differ in patients with different SARS-CoV-2 variants. This study aimed to compare pulmonary fibrotic changes between two SARS-CoV-2 variant periods (delta vs. pre-delta) in critically ill patients with SARS-CoV-2 pneumonia. Clinical data and chest CT images of patients with SARS-CoV-2 pneumonia receiving mechanical ventilation were collected from 10 hospitals in South Korea over two periods: delta (July-December, 2021; n = 64) and pre-delta (February, 2020-June, 2021; n = 120). Fibrotic changes on chest CT were evaluated through visual assessment. Of 184 patients, the mean age was 64.6 years, and 60.5% were ale. Fibrosis-like changes on chest CT (median 51 days from enrollment to follow up CT scan, interquartile range 27–76 days) were identified in 75.3%. Delta group showed more fibrosis-like changes (≥ 2) (69.8% vs. 43.1%, P = 0.001) and more frequent reticulation and architectural distortion+/-parenchymal band than pre-delta group. Even after propensity score matching with clinical variables, delta group had more severe (≥ 2) fibrosis-like changes (71.4% vs. 38.8%, P = 0.001), and more frequent reticulation and architectural distortion+/-parenchymal band than pre-delta group. Our data suggest that critically ill patients with SARS-CoV-2 in delta period had more severe pulmonary fibrosis-like changes than those in pre-delta period.

Detection of biventricular volume increase in overweight and obese by using a novel index of the ideal human - a single center non-contrast-enhanced cardiac CT study

Abstract The bi-ventricular volume (BVV) can be measured from non-contrast-enhanced CT images in patients referred for coronary artery calcium (CAC) scoring. Such determined BVV has been found to correlate with left ventricular mass and to predict increased risk of all-cause and CV mortality in patients with type 2 diabetes mellitus. Currently, no data are available that quantifying the effect of body size on BVV using NCE-CT. We used a new indexing parameter that consisted of two parts: height and the difference between numerical values of height and body surface area (BSA). In the "ideal human", the difference is zero. Any deviation from this value determines the share of body mass regardless of height. Aim We hypothesize that a novel indexation way allows for better discrimination of the increased bi-ventricular volume in overweight and obese patients. Method We retrospectively analyzed the CT data from 2466 patients with CAC scoring over the last 15 years. Among them 1606 women and 860 men with an average age of 64 ± 11 years (range 21-93). The fatless bi-ventricular volume was measured manually using commercial software on a dedicated workstation (Vitrea2). Values of the raw (measured, ml) BVV and BVV normalized for height, BSA, and the novel parameter i.e. [h+ (h-BSA)], were compared in patients taking into account body mass index (BMI) categories of normal weight (25% of patients), overweight (40%) and obese (35%). Results Overall, the measured BVV [ml] was significantly greater in males (414±97) than in females (297±66) (p&amp;lt;0.001). BVV decreased non-linearly from before 40ties to mid-60ties, and then remained nearly stable. In normal-weight males, the measured BVV (370±80ml) was greater than in normal-weight females (279±61ml) (p&amp;lt;0.001). In overweight males, the BVV was 411±89ml; in overweight females, the BVV reached 290±59ml (p&amp;lt;0.001). These differences were non-significantly different from the sample of patients with normal BMI. In obese patients, mean values significantly differed between males and females (442±106 vs 321±71 ml, p&amp;lt;0.001). As the absolute value of the BVV should be interpreted in the context of body habitus, normalization for height, BSA, and the novel index were used. The results of these examinations are given in the figure. Conclusions Commonly used methods of indexing the volumetric parameters of the ventricles, as well as the novel indexing approach showed similar results in patients with normal body weight. The proposed indexation allowed for discriminating the increase in BVV that is easy to overlook in overweight. The use of body surface area for volumetric normalization is misleading as it provides similar values in normal, overweight, and obese patients, and should therefore be avoided. We should consider whether ventricular size standards based on a study population under 50 years of age create reliable conditions for detecting ventricular enlargement at an older age.

Trabecular Attenuation of L1 in Adult Patients with Multiple Myeloma: An Observational Study on Low-Dose CT Images.

The aim of this study was to evaluate the impact of trabecular attenuation of the L1 vertebral body in low-dose CT in adult patients with multiple myeloma (MM), smoldering multiple myeloma (SMM), and monoclonal gammopathy of undetermined significance (MGUS). The study population consisted of 22 patients with MGUS and 51 consecutive patients with newly diagnosed MM (SMM, n = 21; symptomatic MM, n = 36). CT scans were conducted using a 128-slice CT scanner (Somatom go.Top, Siemens, Munich, Germany). Low-dose whole-body CT scans were performed at a single time point for each patient. Trabecular bone density values were obtained by defining regions of interest on non-contrast images at the level of L1 vertebra. A threshold of p = 0.05 was applied to determine statistical significance. The median Hounsfield unit (HU) value in patients with MGUS, SMM, and MM was 148 HU (range 81-190), 130 HU (range 93-193), and 92 HU (range 26-190), respectively, with a statistically significant difference between the groups (p = 0.0015). Patients with HU values ≤ 92 had lower progression-free survival with statistically significant differences compared to the group with HU values > 92 (p < 0.0499). This is the earliest evidence of the importance of evaluating L1 attenuation values in low-dose CT images in patients with MGUS, SMM, and MM. Further prospective studies could contribute to reinforcing these results and exploring the clinical applicability and generalization of L1 attenuation values in low-dose whole-body CT scans in routine clinical practice.