Quantitative CT imaging features for COVID-19 evaluation: The ability to differentiate COVID-19 from non- COVID-19 (highly suspected) pneumonia patients during the epidemic period

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.

Pulmonary fibrosis as an outcome of COVID-19 pneumonia

In this study, we report three cases of persistent severe respiratory failure and radiological signs of diffuse pulmonary fibrosis in patients after COVID-19 related pneumonia. Chest CT criteria for a diagnosis of pulmonary fibrosis include the presence of traction bronchiectasis and parenchymal bands (linear opacities), as well as numerous small subpleural cystic air spaces (honeycombing). The study found that all cases of fibrosis were associated with such risk factors as male gender, old age, and multicomorbidity. Chest CT of one patient with acute respiratory distress syndrome showed radiographic characteristics of pulmonary fibrosis together with the presence of ground glass opacities (GGO) – the main CT feature of COVID-19 pneumonia. In two other patients, CT features of pulmonary fibrosis appeared in about 2 months after the hospital stay.Thus, pulmonary fibrosis is severe and rapidly progressive complication of COVID-19 pneumonia with a poor prognosis, especially in patients with multiple risk factors.

Reversibility of clinical and computed tomographic lesions mimicking pulmonary fibrosis in a young cat

Background In humans with idiopathic pulmonary fibrosis (IPF), specific thoracic computed tomographic (CT) features in the correct clinical context may be used in lieu of histologic examination. Cats develop an IPF-like condition with similar features to humans. As few cats have invasive lung biopsies, CT has appeal as a surrogate diagnostic, showing features consistent with architectural remodeling supporting “end-stage lung”. Case presentation A 1-year-old female spayed Domestic Shorthair cat presenting with progressive respiratory clinical signs and thoracic CT changes (reticular pattern, parenchymal bands, subpleural interstitial thickening, pleural fissure thickening, subpleural lines and regions of increased attenuation with traction bronchiectasis and architectural distortion) consistent with reports of IPF was given a grave prognosis for long-term survival. The cat was treated with prednisolone, fenbendazole, pradofloxacin and clindamycin. Five months later, while still receiving an anti-inflammatory dose of prednisolone, the cat was re-evaluated with owner-reported absent respiratory clinical signs. Thoracic CT demonstrated resolution of lung patterns consistent with fibrosis. Conclusions Fibrotic lung disease is irreversible. Despite this cat having compatible progressive respiratory signs and associated lung patterns on thoracic CT scan, these abnormalities resolved with non-specific therapy and time, negating the possibility of IPF. While the cause of the distinct CT lesions that ultimately resolved was not determined, infection was suspected. Experimental Toxocara cati infection shows overlapping CT features as this cat and is considered a treatable disease. Improvement of CT lesions months after experimental heartworm-associated respiratory disease in cats has been documented. Reversibility of lesions suggests inflammation rather than fibrosis was the cause of the thoracic CT lesions. This cat serves as a lesson that although thoracic CT has been advocated as a surrogate for histopathology in people with IPF, additional studies in cats are needed to integrate CT findings with signalment, other clinicopathologic features and therapeutic response before providing a diagnosis or prognosis of fibrotic lung disease.

18F-fluorodeoxyglucose Positron Emission Tomography/computed Tomography Comparison of Gastric Lymphoma and Gastric Carcinoma

ObjectiveThis study aimed to compare the characteristics of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) in gastric lymphoma (GL) and gastric carcinoma (GC).METHODSPatients with newly diagnosed GL or GC who underwent 18F-FDG PET/CT prior to treatment were included in this study. The PET/CT features of gastric wall lesions, including FDG avidity, pattern, and intensity [maximal standard uptake value (SUVmax)] were reviewed and analyzed. The correlation of SUVmax with gastric clinicopathological variables was investigated by Student’s t test, Mann-Whitney U test, chi-square test, and receiver-operating characteristic (ROC) curve analysis to determine the differential diagnostic value of SUVmax-associated parameters in GL and GC.RESULTSA total of 68 patients with GL and 117 with GC were included in this study. Abnormal gastric FDG accumulation was found in 66 (97.05%) patients with GL and 109 (93.16%) patients with GC. Majority of the GL patients presented with type I and III lesions, whereas GC patients mainly with type II and III lesions. The SUVmax [14.89(6.56，22.12) vs 4.08(5.75，10.24), P<0.001) and SUVmax/THKmax (maximal thickness) [0.72(0.50,1,10) vs 0.39(0.28,0.64), P<0.000] were both higher in patients with GL when compared with GC. The SUVmax was higher in GL patients with DLBCL than in those with MALT (17.51±10.31 vs 4.97±2.92, P =0.0010) and higher in patients with advanced Lugano stage (II1/II2/IV) than in those with stage I (18.27±10.19 vs 9.92±9.22, P = 0.002). In GC patients, the SUVmax was higher in T3+T4 than in T1+T2 [6.79(4.47,12.41) vs 4.09(2.32,6.31), P = 0.000], but there were no significant differences between TNM stage I+II and TNM stage III+IV. The ROC curve analysis suggested a better performance of SUVmax/THKmax in evaluating gastric lesions between GL and GC when compared to SUVmax alone. The sensitivity of SUVmax and SUVmax/THKmax for differential diagnosis of GL and GC were 0.917 and 0.578, respectively. However, the specificity of SUVmax/THKmax for differential diagnosis of GL and GC was higher than that of SUVmax alone (0.894 VS 0.485).CONCLUSIONThe PET/CT features differed between GL and GC, and these can improve the evaluation of PET/CT of gastric wall lesions and help differentiate GL from GC.