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   Table of Contents - Current issue
Coverpage
March 30 2021
Volume 8 | Issue 1
Page Nos. 1-53

Online since Thursday, November 18, 2021

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ORIGINAL ARTICLES  

An artificial intelligence-based radiomics model for differential diagnosis between coronavirus disease 2019 and other viral pneumonias p. 1
Mudan Zhang, Wuchao Li, Xuntao Yin, Xianchun Zeng, Xinfeng Liu, Xiaochun Zhang, Qi Chen, Chencui Huang, Zhen Zhou, Rongpin Wang
DOI:10.4103/RID.RID_1_21  
OBJECTIVE: To set up a differential diagnosis radiomics model to identify coronavirus disease 2019 (COVID-19) and other viral pneumonias based on an artificial intelligence (AI) approach that utilizes computed tomography (CT) images. MATERIALS AND METHODS: This retrospective multi-center research involved 225 patients with COVID-19 and 265 patients with other viral pneumonias. The least absolute shrinkage and selection operator algorithm was used for the optimized features selection from 1218 radiomics features. Finally, a logistic regression (LR) classifier was applied to construct different diagnosis models. The receiver operating characteristic curve analysis was applied to evaluate the accuracy of different models. RESULTS: The patients were divided into a training set (313 of 392, 80%), an internal test set (79 of 392, 20%) and an external test set (n = 98). Thirteen features were selected to build the machine learning-based CT radiomics models. LR classifiers performed well in the training set (area under the curve [AUC] = 0.91), internal test set (AUC = 0.94), and external test set (AUC = 0.91). Delong tests suggested there was no significant difference between training and the two test sets (P > 0.05). CONCLUSION: The use of an AI-based radiomics model enables rapid discrimination of patients with COVID-19 from other viral infections, which can aid better surveillance and control during a pneumonia outbreak.
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Correlation between thorax computed tomography findings and clinical and laboratory data on patients with coronavirus disease 2019 p. 9
Ruken Ergenc, Deniz Gizem Okray, Uygar Mutlu, Ahmet Tanyeri, Merve Nizam Şahin
DOI:10.4103/RID.RID_2_21  
OBJECTIVE: We investigated the correlation between computed tomography (CT) scores, laboratory findings, and clinical symptoms in patients with coronavirus disease 2019 (COVID-19). MATERIALS AND METHODS: Clinical, laboratory, and thorax CT findings on the admission of 121 patients with COVID-19 were retrospectively evaluated. CT scores based on lobe involvement and CT patterns (i.e., ground-glass abnormalities, consolidation, and crazy-paving patterns) were estimated, and the relationship between CT score and symptomatic (e.g. fever, cough) versus asymptomatic (e.g., inflammation, coagulation, liver and kidney function) clinical laboratory findings were statistically analyzed. RESULTS: Sixty-eight of 121 patients (56%) were symptomatic; 53 (44%) were asymptomatic. The CT scores of symptomatic patients, especially those with coughing and dyspnea, were statistically higher (2 [0–9] vs. 0 [0–1]; P < 0.001). Erythrocyte sedimentation rate, C-reactive protein, ferritin, D-dimer, fibrinogen, aspartate aminotransferase, lactate dehydrogenase, glucose, prothrombin time and alanine amino transferase values were correlated with CT scores (ρ = 0.638, P < 0.001, ρ = 0.512, P < 0.001; ρ = 0.325, P = 0.001; ρ = 0.452, P < 0.001; ρ = 0.525, P < 0.001; ρ = 0.379, P < 0.001; ρ = 0.445, P < 0.001; ρ = 0.332, P < 0.001, ρ = 0.296, P = 0.003; ρ = 0.222, P = 0.015, respectively). Albumin values were negatively correlated with CT scores (ρ = −0.398, P < 0.001). CONCLUSION: CT scores may help clinicians evaluate the severity of COVID-19 pneumonia and thus help in managing the disease.
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CT quantitative analysis in patients with severe coronavirus disease 2019 and its correlation with laboratory examination results p. 17
Lan Wu, Ran Yang, Dajing Guo, Xiang Li, Chuanming Li, Wenbing Zeng, Ting Chen
DOI:10.4103/RID.RID_3_21  
OBJECTIVE: To quantitatively analyze the longitudinal changes of ground-glass opacity (GGO), consolidation and total lesion in patients infected with severe coronavirus disease 2019 (COVID-19), and its correlation with laboratory examination results. MATERIALS AND METHODS: All 76 computed tomography (CT) images and laboratory examination results from the admission to discharge of 15 patients confirmed with severe COVID-19 were reviewed, whereas the GGO volume ratio, consolidation volume ratio, and total lesion volume ratio in different stages were analyzed. The correlations of lesions volume ratio and laboratory examination results were investigated. RESULTS: Four stages were identified based on the degree of lung involvement from day 1 to day 28 after disease onset. GGO was the most common CT manifestation in the four stages. The peak of lung involvement was at around stage 2, and corresponding total lesion volume ratio, GGO volume ratio, and consolidation volume ratio were 17.48 (13.44−24.33), 12.11 (7.34−17.08), and 5.51 (2.58−8.58), respectively. Total lesion volume ratio was positively correlated with neutrophil percentage, C-reactive protein (CRP), high-sensitivity CRP (Hs-CRP), procalcitonin, lactate dehydrogenase (LD), and creatine kinase isoenzyme MB (CK-MB), but negatively correlated with lymphocyte count, lymphocyte percentage, arterial oxygen saturation, and arterial oxygen tension. Consolidation volume ratio was correlated with most above laboratory examination results except Hs-CRP, LD, and CK-MB. GGO, however, was only correlated with lymphocyte count. CONCLUSION: CT quantitative parameters could show longitudinal changes well. Total lesion volume ratio and consolidation volume ratio are well correlated with laboratory examination results, suggesting that CT quantitative parameters may be an effective tool to reflect the changes in the condition.
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The diagnostic performance of chest computed tomography scanning in the diagnosis of coronavirus disease 2019 compared to polymerase chain reaction: A retrospective study of 1240 cases from Tripoli University Hospital, Libya Highly accessed article p. 25
Nader Shalaka, Najah Gurad, Salam Alawasi, Nuha Mansour, Ala Ali, Khaled Elobidy, Mohamed Algumati, Hossam Swisi, Elham Elhshik, Ziyad Mukhtar, Alfitouri Abojamra
DOI:10.4103/RID.RID_4_21  
OBJECTIVE: The increasing prevalence of suspected cases of coronavirus disease 2019 (COVID-19) presenting to emergency departments (EDs) requires a rapid and reliable triaging tool. The diagnostic performance of chest computed tomography (CT) has yet to be validated for triaging cases in the ED. We aimed to assess the diagnostic performance of chest CT compared to GeneXpert Xpress Xpert severe acute respiratory syndrome coronavirus 2 test in rapidly diagnosing COVID-19 among patients with respiratory symptoms presenting to the ED. MATERIALS AND METHODS: This was a retrospective, single-center study at Tripoli University Hospital including cases with respiratory symptoms who underwent chest CT as well as polymerase chain reaction (PCR) testing for suspected COVID-19 between May 18 and August 18, 2020. RESULTS: A total of 1240 cases were included, among whom 570 had radiologically evident COVID-19 on chest CT (46%). Five hundred and sixty-five cases had positive PCR results (45.6%), of whom 557 had radiologically evident COVID-19 on chest CT (97.7%). The calculated accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 98%, 98.5%, 98%, 97.7%, and 98.8%, respectively, in relation to the PCR results. CONCLUSION: During the current pandemic, chest CT is a quick and reliable diagnostic tool for COVID-19 in the ED.
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Diagnostic value of ground-glass opacity in suspected coronavirus disease 2019 patients: A meta-analysis p. 31
Yanqiu Zhu, Cui Yan, Yani Duan, Leilei Tang, Junying Zhu, Xiuzhen Chen, Yunxu Dong, Weimin Liu, Wenjie Tang, Yuefei Guo, Jie Qin
DOI:10.4103/RID.RID_7_21  
OBJECTIVE: The aim of the study was to evaluate the diagnostic efficiency of ground-glass opacity (GGO) for coronavirus disease 2019 (COVID-19) in suspected patients. MATERIALS AND METHODS: In this systematic review and meta-analysis, PubMed, Embase, Cochrane Library, Scopus, Web of Science, CNKI, and Wanfang databases were searched from November 01, 2019 to November 29, 2020. Studies providing the diagnostic test accuracy of chest computed tomography (CT) and description of detailed CT features for COVID-19 were included. Data were extracted from the publications. The sensitivity, specificity, and summary receiver operating characteristic curves were pooled. Heterogeneity was detected across included studies. RESULTS: Eleven studies with 1618 cases were included. The pooled sensitivity, specificity and area under the curve were 0.74 (95% confidence interval [CI], 0.61–0.84), 0.52 (95% CI, 0.33–0.70), and 0.70 (95% CI, 0.66–0.74), respectively. There was obvious heterogeneity among included studies (P < 0.05). Differences in the study region, inclusion criteria, research quality, or research methods might have contributed to the heterogeneity. The included studies had no significant publication bias (P > 0.1). CONCLUSIONS: COVID-19 was diagnosed not only by GGO with a medium level of diagnostic accuracy but also by white blood cell counts, epidemic history, and revers transcription-polymerase chain reaction testing.
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REVIEW ARTICLES Top

Mechanism and computed tomography features of liver injury caused by coronavirus disease 2019: Current status p. 42
Fulin Lu, Jing Ou, Rui Li, Bangguo Tan, Xiaoming Zhang, Tianwu Chen, Hongjun Li
DOI:10.4103/RID.RID_5_21  
Liver injury is found in some patients with coronavirus disease-2019 (COVID-19). Both the clinical treatment efficacy and the patient's prognosis are affected by the severity of liver injury. In addition, in some cases, liver injury may occur in the absence of respiratory symptoms. To date, liver injury diagnosed based on laboratory findings and abdominal computed tomography (CT) has been reported in COVID-19 patients. The aim of this review was to summarize the mechanism of liver injury caused by COVID-19 and describe the CT features of COVID-19-induced liver damage.
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Lesion types, pathogenesis, pathological manifestations, and imaging findings of cardiovascular complications induced by coronavirus disease 2019: Current status p. 45
Feiran Yu, Qimin Zhou, Dexin Yu
DOI:10.4103/RID.RID_6_21  
The coronavirus disease 2019 (COVID-19) has formed a worldwide pandemic trend. Despite the virus usually invades lungs and presents with various respiratory symptoms, it can also affect the cardiac function in multiple ways and result in high mortality. Various possible mechanisms have been proposed to explain these manifestations at present, including cytokine storm and direct invasion of the virus. There are a series of feasible schemes in clinical work to reduce the incidence of complications now, but the layered management of hospitalized patients, the early prevention, and the early detection of complications seem to be more important. Cardiac imaging examinations (such as computed tomography coronary angiography, magnetic resonance imaging multi-parameter scan, and enhanced scan, etc.) are very essential in these aspects. However, radiological data of the cardiac complications are not comprehensive enough in accessing the prognosis due to the limitation of examination. This paper summarized the imaging findings of cardiac complications of COVID-19, providing the possible morphological basis or hypothesis for cardiac multimode imaging by analyzing the pathological manifestations retrospectively.
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