CT radiomics facilitates more accurate diagnosis of COVID-19 pneumonia: compared with CO-RADS

Liu, Huanhuan; Ren, Hua; Wu, Zengbin; Xu, He; Zhang, Shuhai; Li, Jinning; Hou, Lingmi; Chi, Runmin; Zheng, Hui; Chen, Yanhong; Duan, Shaofeng; Li, Huimin; Xie, Zongyu; Wang, Dengbin

doi:10.1186/s12967-020-02692-3

Cited by 46 publications

(39 citation statements)

References 30 publications

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“…Among the studies that included viral pneumonia comparisons, two studies included only influenza infections [33,34]. Other studies included influenza and other viral pneumonia infections [35][36][37][38][39]. Considering that influenza also represents a highly contagious disease with a high prevalence of adult viral pneumonias [41,42].…”

Section: Discussionmentioning

confidence: 99%

“…One observational study [30] without radiomics application in the research and two observational studies [31,32] with a repetitive patient population were excluded. After the article selection process, seven articles were used in the qualitative analysis [33][34][35][36][37][38][39], and six articles were further used in the meta-analysis. The PRISMA flowchart is provided in Figure 1.…”

Section: Literature Collectionmentioning

confidence: 99%

“…Details of the selected studies are listed in Table 1. Only the six studies were used in the meta-analysis [33][34][35][36][37][38]. Wang's study [39] was excluded due to the predictions being made on the basis of the CT slice number.…”

Section: Literature Collectionmentioning

confidence: 99%

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A Meta-Analysis of Computerized Tomography-Based Radiomics for the Diagnosis of COVID-19 and Viral Pneumonia

Kao

Lin

2021

Diagnostics

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Introduction: Coronavirus disease 2019 (COVID-19) led to a global pandemic. Although reverse transcription polymerase chain reaction (RT-PCR) of viral nucleic acid is the gold standard for COVID-19 diagnosis, its sensitivity was found to not be high enough in many reports. As radiomics-based diagnosis research has recently emerged, we aimed to use computerized tomography (CT)-based radiomics models to differentiate COVID-19 pneumonia from other viral pneumonia infections. Materials and methods: This study was performed according to the preferred reporting items for systematic review and meta-analysis diagnostic test accuracy studies (PRISMA-DTA) guidelines. The Pubmed, Cochrane, and Embase databases were searched. The pooled sensitivity and pooled specificity were calculated. A summary receiver operating characteristic (sROC) curve was constructed. The study quality was evaluated based on the radiomics quality score. Results: A total of 10,300 patients were involved in this meta-analysis. The radiomics quality score ranged from 13 to 16 (maximum score: 36). The pooled sensitivity was 0.885 (95% CI: 0.818-0.929), and the pooled specificity was 0.811 (95% CI: 0.667-0.902). The pooled AUC was 906. Conclusion: Our meta-analysis showed that CT-based radiomics feature models can successfully differentiate COVID-19 from other viral pneumonias.

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Section: Discussionmentioning

confidence: 99%

Section: Literature Collectionmentioning

confidence: 99%

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A Meta-Analysis of Computerized Tomography-Based Radiomics for the Diagnosis of COVID-19 and Viral Pneumonia

Kao

Lin

2021

Diagnostics

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“…CO-RADS 5. Based on standard CT results, CO-RADS 5 indicates a very high degree of suspicion for COVID-19 pulmonary involvement [9]. The findings associated with this category can be broken down into two groups: Mandatory features, which must be present in all cases, and confirmatory patterns of features.…”

Section: Resultsmentioning

confidence: 99%

Clinical Application of Covid-19 Reporting and Data System in Computed Tomography of Bilateral Pneumonia Diagnostic

Kulnickaitė¹,

Dobrovaitė²,

Grigaitė³

et al. 2021

Health Sciences

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Background: the 2019 coronavirus disease pandemic (COVID-19) has spread at an astonishing speed across the world, causing major morbidity and mortality. Computed tomography (CT) examination plays an important role in crisis areas in the diagnosis of COVID-19. COVID-19 Reporting and Data System (CO-RADS) has a five-point scale of suspicion for COVID-19 pneumonia in chest CT picture which standardizes the evaluation scheme and simplifies reporting. Aim: to summarise and present the role of COVID-19 Reporting and Data System in computed tomography of bilateral pneumonia diagnostic. Materials and methods: recently published studies were reviewed to evaluate COVID-19 Reporting and Data System scale as effective tool to detect COVID-19 pneumonia on chest CT scans. Databases from the subscription list of Lithuanian University of Health Sciences were selected: Medline (PubMed), SpringerLink and ScienceDirect. Results: chest CT features, as bilateral involvement, subpleural or peripherally distributed GGO, consolidation, reticulation, crazy paving pattern, air bronchogram signs, intralobular septal thickening, pulmonary vascular enlargement, are considered to be characteristic manifestations of COVID-19 infection. Studies show that Dutch Radiological Society presented CO-RADS scale sensitivity and specificity may vary from 61-88% and 66,4-98%, respectively. Conclusion: chest CT scan has a high sensitivity for COVID-19 diagnosis and could reduce false negative results obtained from RT-PCR tests. Furthermore, a standardized reporting system could increase clarification, minimize reporting variability and help radiologists recognize the results they observe, especially, for less experienced specialists.

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“…Huang et al [ 11 ] analyzed 154 viral pneumonia patients (including 89 cases of COVID-19 and 65 cases of influenza pneumonia) to establish a CT-based radiomics model, whose results showed radiomics model had a satisfactory performance in distinguishing COVID-19 and influenza pneumonia. Liu et al [ 12 ] reported CT-based radiomics model could facilitate a rapid and accurate detection in differentiation of COVID-19 and Non-COVID-19 pneumonia. Fang et al [ 13 ] summarized 136 patients with COVID-19 and developed a CT-based radiomics model for discriminating COVID-19 and other types of viral pneumonia, which showed a good performance for predicting COVID-19 pneumonia.…”

Section: Discussionmentioning

confidence: 99%

A novel CT-based radiomics in the distinction of severity of coronavirus disease 2019 (COVID-19) pneumonia

et al. 2021

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Background Convenient and precise assessment of the severity in coronavirus disease 2019 (COVID-19) contributes to the timely patient treatment and prognosis improvement. We aimed to evaluate the ability of CT-based radiomics nomogram in discriminating the severity of patients with COVID-19 Pneumonia. Methods A total of 150 patients (training cohort n = 105; test cohort n = 45) with COVID-19 confirmed by reverse transcription polymerase chain reaction (RT-PCR) test were enrolled. Two feature selection methods, Max-Relevance and Min-Redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO), were used to extract features from CT images and construct model. A total of 30 radiomic features were finally retained. Rad-score was calculated by summing the selected features weighted by their coefficients. The radiomics nomogram incorporating clinical-radiological features was eventually constructed by multivariate regression analysis. Nomogram, calibration, and decision-curve analysis were all assessed. Results In both cohorts, 40 patients with COVID-19 pneumonia were severe and 110 patients were non-severe. By combining the 30 radiomic features extracted from CT images, the radiomics signature showed high discrimination between severe and non-severe patients in the training set [Area Under the Curve (AUC), 0.857; 95% confidence interval (CI), 0.775–0.918] and the test set (AUC, 0.867; 95% CI, 0.732–949). The final combined model that integrated age, comorbidity, CT scores, number of lesions, ground glass opacity (GGO) with consolidation, and radiomics signature, improved the AUC to 0.952 in the training cohort and 0.98 in the test cohort. The nomogram based on the combined model similarly exhibited excellent discrimination performance in both training and test cohorts. Conclusions The developed model based on a radiomics signature derived from CT images can be a reliable marker for discriminating the severity of COVID-19 pneumonia.

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CT radiomics facilitates more accurate diagnosis of COVID-19 pneumonia: compared with CO-RADS

Cited by 46 publications

References 30 publications

A Meta-Analysis of Computerized Tomography-Based Radiomics for the Diagnosis of COVID-19 and Viral Pneumonia

A Meta-Analysis of Computerized Tomography-Based Radiomics for the Diagnosis of COVID-19 and Viral Pneumonia

Clinical Application of Covid-19 Reporting and Data System in Computed Tomography of Bilateral Pneumonia Diagnostic

A novel CT-based radiomics in the distinction of severity of coronavirus disease 2019 (COVID-19) pneumonia

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