ct imaging
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2022 ◽  
Vol 17 (3) ◽  
pp. 907-910
James Yuheng Jiang ◽  
Monica Comsa ◽  
Veronica Chi Ken Wong ◽  
Robert Mansberg

2022 ◽  
Vol 1 ◽  
Mboyo D. T. Vangu ◽  
Jaleelat I. Momodu

Since its introduction into clinical practice, multimodality imaging has revolutionized diagnostic imaging for both oncologic and non-oncologic pathologies. 18F-fluorodeoxyglucose (18F-FDG) PET/CT imaging which takes advantage of increased anaerobic glycolysis that occurs in tumor cells (Warburg effect) has gained significant clinical relevance in the management of most, if not all oncologic conditions. Because FDG is taken by both normal and abnormal tissues, PET/CT imaging may demonstrate several normal variants and imaging pitfalls. These may ultimately impact disease detection and diagnostic accuracy. Imaging specialists (nuclear medicine physicians and radiologists) must demonstrate a thorough understanding of normal and physiologic variants in the distribution of 18F-FDG; including potential imaging pitfalls and technical artifacts to minimize misinterpretation of images. The normal physiologic course of 18F-FDG results in a variable degree of uptake in the stomach, liver, spleen, small and large bowel. Urinary excretion results in renal, ureteric, and urinary bladder uptake. Technical artifacts can occur due to motion, truncation as well as the effects of contrast agents and metallic hardware. Using pictorial illustrations, this paper aims to describe the variants of physiologic 18F-FDG uptake that may mimic pathology as well as potential benign conditions that may result in misinterpretation of PET/CT images in common oncologic conditions of the abdomen and pelvis.

PLoS ONE ◽  
2022 ◽  
Vol 17 (1) ◽  
pp. e0256194
Shengkun Peng ◽  
Lingai Pan ◽  
Yang Guo ◽  
Bo Gong ◽  
Xiaobo Huang ◽  

Objectives COVID-19 and Non-Covid-19 (NC) Pneumonia encountered high CT imaging overlaps during pandemic. The study aims to evaluate the effectiveness of image-based quantitative CT features in discriminating COVID-19 from NC Pneumonia. Materials and methods 145 patients with highly suspected COVID-19 were retrospectively enrolled from four centers in Sichuan Province during January 23 to March 23, 2020. 88 cases were confirmed as COVID-19, and 57 patients were NC. The dataset was randomly divided by 3:2 into training and testing sets. The quantitative CT radiomics features were extracted and screened sequentially by correlation analysis, Mann-Whitney U test, the least absolute shrinkage and selection operator (LASSO) logistic regression (LR) and backward stepwise LR with minimum AIC methods. The selected features were used to construct the LR model for differentiating COVID-19 from NC. Meanwhile, the differentiation performance of traditional quantitative CT features such as lesion volume ratio, ground glass opacity (GGO) or consolidation volume ratio were also considered and compared with Radiomics-based method. The receiver operating characteristic curve (ROC) analysis were conducted to evaluate the predicting performance. Results Compared with traditional CT quantitative features, radiomics features performed best with the highest Area Under Curve (AUC), sensitivity, specificity and accuracy in the training (0.994, 0.942, 1.0 and 0.965) and testing sets (0.977, 0.944, 0.870, 0.915) (Delong test, P < 0.001). Among CT volume-ratio based models using lesion or GGO component ratio, the model combining CT lesion score and component ratio performed better than others, with the AUC, sensitivity, specificity and accuracy of 0.84, 0.692, 0.853, 0.756 in the training set and 0.779, 0.667, 0.826, 0.729 in the testing set. The significant difference of the most selected wavelet transformed radiomics features between COVID-19 and NC might well reflect the CT signs. Conclusions The differentiation between COVID-19 and NC could be well improved by using radiomics features, compared with traditional CT quantitative values.

2022 ◽  
Vol 23 (2) ◽  
pp. 859
Ihsan Hammoura ◽  
Renee H. Fiechter ◽  
Shaughn H. Bryant ◽  
Susan Westmoreland ◽  
Gillian Kingsbury ◽  

The tumor necrosis factor (TNF) and IL-23/IL-17 axes are the main therapeutic targets in spondyloarthritis. Despite the clinical efficacy of blocking either pathway, monotherapy does not induce remission in all patients and its effect on new bone formation remains unclear. We aimed to study the effect of TNF and IL-17A dual inhibition on clinical disease and structural damage using the HLA-B27/human β2-microglobulin transgenic rat model of SpA. Immunized rats were randomized according to arthritis severity, 1 week after arthritis incidence reached 50%, to be treated twice weekly for a period of 5 weeks with either a dual blockade therapy of an anti-TNF antibody and an anti-IL-17A antibody, a single therapy of either antibody, or PBS as vehicle control. Treatment-blinded observers assessed inflammation and structural damage clinically, histologically and by micro-CT imaging. Both single therapies as well as TNF and IL-17A dual blockade therapy reduced clinical spondylitis and peripheral arthritis effectively and similarly. Clinical improvement was confirmed for all treatments by a reduction of histological inflammation and pannus formation (p < 0.05) at the caudal spine. All treatments showed an improvement of structural changes at the axial and peripheral joints on micro-CT imaging, with a significant decrease for roughness (p < 0.05), which reflects both erosion and new bone formation, at the level of the caudal spine. The effect of dual blockade therapy on new bone formation was more prominent at the axial than the peripheral level. Collectively, our study showed that dual blockade therapy significantly reduces inflammation and structural changes, including new bone formation. However, we could not confirm a more pronounced effect of dual inhibition compared to single inhibition.

Ethan Schonfeld ◽  
Madeleine de Lotbiniere-Bassett ◽  
Tatiana Jansen ◽  
Diana Anthony ◽  
Anand Veeravagu

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