scholarly journals Initial CT features of COVID-19 predicting clinical category

Author(s):  
Li Fan ◽  
Wenqing Le ◽  
Qin Zou ◽  
Xiuxiu Zhou ◽  
Yun Wang ◽  
...  
1997 ◽  
Vol 37 (4) ◽  
pp. 673 ◽  
Author(s):  
Kyung Sook Kim ◽  
Moon Gyu Lee ◽  
Young Chul Won ◽  
Eun Hye Lee ◽  
Han Na Noh ◽  
...  

1997 ◽  
Vol 36 (4) ◽  
pp. 651
Author(s):  
T H Kim ◽  
K Y Lee ◽  
K H Shin ◽  
M H Jung ◽  
C M Park ◽  
...  

2000 ◽  
Vol 42 (5) ◽  
pp. 805
Author(s):  
Jung Kyung Yun ◽  
Jun Sik Lee ◽  
Mee Eun Kim ◽  
Hae Wook Pyun ◽  
Il Gi Lee ◽  
...  

2015 ◽  
Vol 36 (12) ◽  
pp. 1385
Author(s):  
Yuan YUAN ◽  
Sheng-nan REN ◽  
Xiao-yu GUO ◽  
Xiao-long MA ◽  
Jian-ping LU

2020 ◽  
Vol 9 (8) ◽  
pp. 2425
Author(s):  
Wei-Hsin Yuan ◽  
Anna Fen-Yau Li ◽  
Shu-Yi Yu ◽  
Ying-Yuan Chen ◽  
Chia-Hung Wu ◽  
...  

Background: Benign immunoglobulin G4 (IgG4)-related orbital disease (IgG4-ROD)—characterized as tumors mimicking malignant orbital lymphoma (OL)—responds well to steroids, instead of chemotherapy, radiotherapy and/or surgery of OL. The objective of this study was to report the differences in computed tomography (CT) features and- serum IgG4 levels of IgG4-ROD and OL. Methods: This study retrieved records for patients with OL and IgG4-ROD from a pathology database during an eight-year-and-five-month period. We assessed the differences between 16 OL patients with 27 lesions and nine IgG4-ROD patients with 20 lesions according to prebiopsy CT features of lesions and prebiopsy serum IgG4 levels and immunoglobulin G (IgG) levels This study also established the receiver-operating curves (ROC) of precontrast and postcontrast CT Hounsfield unit scales (CTHU), serum IgG4 levels, serum IgG levels and their ratios. Results: Significantly related to IgG4-ROD (all p < 0.05) were the presence of lesions with regular borders, presence of multiple lesions—involving both lacrimal glands on CT scans—higher median values of postcontrast CTHU, postcontrast CTHU/precontrast CTHU ratios, serum IgG4 levels and serum IgG4/IgG level ratios. Compared to postcontrast CTHU, serum IgG4 levels had a larger area under the ROC curve (0.847 [95% confidence interval (CI): 0.674–1.000, p = 0.005] vs. 0.766 [95% CI: 0.615–0.917, p = 0.002]), higher sensitivity (0.889 [95% CI: 0.518–0.997] vs. 0.75 [95% CI: 0.509–0.913]), higher specificity (0.813 [95% CI: 0.544–0.960] vs. 0.778 [95% CI: 0.578–0.914]) and a higher cutoff value (≥132.5 mg/dL [milligrams per deciliter] vs. ≥89.5). Conclusions: IgG4-ROD showed distinct CT features and elevated serum IgG4 (≥132.5 mg/dL), which could help distinguish IgG4-ROD from OL.


Author(s):  
Faeze Salahshour ◽  
Mohammad-Mehdi Mehrabinejad ◽  
Mohssen Nassiri Toosi ◽  
Masoumeh Gity ◽  
Hossein Ghanaati ◽  
...  

Author(s):  
Eva Prado ◽  
Elena M. Chamorro ◽  
Alejandro Marín ◽  
Carlos G. Fuentes ◽  
Zhao Chen Zhou

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xiaohua Ban ◽  
Xinping Shen ◽  
Huijun Hu ◽  
Rong Zhang ◽  
Chuanmiao Xie ◽  
...  

Abstract Background To determine the predictive CT imaging features for diagnosis in patients with primary pulmonary mucoepidermoid carcinomas (PMECs). Materials and methods CT imaging features of 37 patients with primary PMECs, 76 with squamous cell carcinomas (SCCs) and 78 with adenocarcinomas were retrospectively reviewed. The difference of CT features among the PMECs, SCCs and adenocarcinomas was analyzed using univariate analysis, followed by multinomial logistic regression and receiver operating characteristic (ROC) curve analysis. Results CT imaging features including tumor size, location, margin, shape, necrosis and degree of enhancement were significant different among the PMECs, SCCs and adenocarcinomas, as determined by univariate analysis (P < 0.05). Only lesion location, shape, margin and degree of enhancement remained independent factors in multinomial logistic regression analysis. ROC curve analysis showed that the area under curve of the obtained multinomial logistic regression model was 0.805 (95%CI: 0.704–0.906). Conclusion The prediction model derived from location, margin, shape and degree of enhancement can be used for preoperative diagnosis of PMECs.


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