A simple chest CT score for assessing the severity of pulmonary involvement in COVID-19

Abdel-Tawab, Mohamed; Basha, Mohammad Abd Alkhalik; Mohamed, Ibrahim A.; Ibrahim, Hamdy

doi:10.1186/s43055-021-00525-x

Cited by 15 publications

(24 citation statements)

References 15 publications

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“…The similar segmented sectors of the right and left lungs have different sizes, because the right lung is larger than the left lung. For quantitative and semi-quantitative studies, these differences must be taken into account and dedicated software is required [46].…”

Section: Study Limitationsmentioning

confidence: 99%

Association of chest CT severity score with mortality of COVID-19 patients: a systematic review and meta-analysis

Zakariaee

Salmanipour

Naderi

et al. 2022

Clin Transl Imaging

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Purpose Chest computed tomography (CT) is a high-sensitivity diagnostic tool for depicting interstitial pneumonia and may lay a critical role in the evaluation of the severity and extent of pulmonary involvement. In this study, we aimed to evaluate the association of chest CT severity score (CT-SS) with the mortality of COVID-19 patients using systematic review and meta-analysis. Methods Web of Science, PubMed, Embase, Scopus, and Google Scholar were used to search for primary articles. The meta-analysis was performed using the random-effects model, and odds ratios (ORs) with 95% confidence intervals (95%CIs) were calculated as the effect sizes. Results This meta-analysis retrieved a total number of 7106 COVID-19 patients. The pooled estimate for the association of CT-SS with mortality of COVID-19 patients was calculated as 1.244 (95% CI 1.157-1.337). The pooled estimate for the association of CT-SS with an optimal cutoff and mortality of COVID-19 patients was calculated as 7.124 (95% CI 5.307-9.563). There was no publication bias in the results of included studies. Radiologist experiences and study locations were not potential sources of between-study heterogeneity (both P > 0.2). The shapes of Begg's funnel plots seemed symmetrical for studies evaluating the association of CT-SS with/without the optimal cutoffs and mortality of COVID-19 patients (Begg's test P = 0.945 and 0.356, respectively). Conclusions The results of this study point to an association between CT-SS and mortality of COVID-19 patients. The odds of mortality for COVID-19 patients could be accurately predicted using an optimal CT-SS cutoff in visual scoring of lung involvement. KeywordsCOVID-19 • Computed tomography • Mortality • CT • CT severity score • Pneumonia Abbreviations COVID-19 Coronavirus disease 2019 SARS-CoV-2 Severe acute respiratory syndrome coronavirus 2 CT-SS Chest CT severity score CT Computed tomography CI Confidence interval RT-PCR Reverse-transcriptase polymerase chain reaction OR Odds ratio HR Hazard ratio * Seyed Salman Zakariaee

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Section: Study Limitationsmentioning

confidence: 99%

Association of chest CT severity score with mortality of COVID-19 patients: a systematic review and meta-analysis

Zakariaee

Salmanipour

Naderi

et al. 2022

Clin Transl Imaging

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“…So, their corresponding lobes and segments have different sizes. Dedicated software and an experienced specialist are required to consider these differences in semiquantitative and quantitative studies [ 47 ].…”

Section: Discussionmentioning

confidence: 99%

Prognostic accuracy of visual lung damage computed tomography score for mortality prediction in patients with COVID-19 pneumonia: a systematic review and meta-analysis

Zakariaee

Naderi

Rezaee

2022

Egypt J Radiol Nucl Med

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Background Chest computed tomography (CT) findings provide great added value in characterizing the extent of disease and severity of pulmonary involvements. Chest CT severity score (CT-SS) could be considered as an appropriate prognostic factor for mortality prediction in patients with COVID-19 pneumonia. In this study, we performed a meta-analysis evaluating the prognostic accuracy of CT-SS for mortality prediction in patients with COVID-19 pneumonia. Methods A systematic search was conducted on Web of Science, PubMed, Embase, Scopus, and Google Scholar databases between December 2019 and September 2021. The meta-analysis was performed using the random-effects model, and sensitivity and specificity (with 95%CIs) of CT-SS were calculated using the study authors’ pre-specified threshold. Results Sensitivity estimates ranged from 0.32 to 1.00, and the pooled estimate of sensitivity was 0.67 [95%CI (0.59–0.75)]. Specificity estimates ranged from 0.53 to 0.95 and the pooled estimate of specificity was 0.79 [95%CI (0.74–0.84)]. Results of meta-regression analysis showed that radiologist experiences did not affect the sensitivity and specificity of CT-SS to predict mortality in COVID-19 patients (P = 0.314 and 0.283, respectively). The test for subgroup differences suggests that study location significantly modifies sensitivity and specificity of CT-SS to predict mortality in COVID-19 patients. The area under the summary receiver operator characteristic (ROC) curve was 0.8248. Conclusion Our results have shown that CT-SS has acceptable prognostic accuracy for mortality prediction in COVID-19 patients. This simple scoring method could help to improve the management of high-risk patients with COVID-19.

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“…Median CCS was 10 [6-15], 10 [6-15], and 10 [6][7][8][9][10][11][12][13][14][15] for the different readers a, b, and c, respectively (Figure 4). Manual estimation of the CCS took about 2 min for each CT scan.…”

Section: Semi-quantitative Scoring Of Lung Involvementmentioning

confidence: 99%

“…Chest computed tomography (CT) is regularly performed in the context of COVID pneumonia, since it has a potential role in its diagnosis and the detection of complications (1). Above all, the extent of lung involvement on chest CT is an established biomarker for the prognosis of the disease and supports clinical decision making (2)(3)(4)(5)(6)(7)(8)(9)(10). The originally established, semi-quantitative approach for assessment of lung involvement in COVID pneumonia was published by Pan et al in 2020 (11).…”

Section: Introductionmentioning

confidence: 99%

Assessment of COVID-19 lung involvement on computed tomography by deep-learning-, threshold-, and human reader-based approaches—an international, multi-center comparative study

Fervers¹,

Fervers²,

Jaiswal³

et al. 2022

Quant Imaging Med Surg

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Background: The extent of lung involvement in Coronavirus Disease 2019 (COVID-19) pneumonia, quantified on computed tomography (CT), is an established biomarker for prognosis and guides clinical decision making. The clinical standard is semi-quantitative scoring of lung involvement by an experienced reader. We aim to compare the performance of automated deep-learning-and threshold-based methods to the manual semi-quantitative lung scoring. Further, we aim to investigate an optimal threshold for quantification of involved lung in COVID pneumonia chest CT, using a multi-center dataset. Methods: In total 250 patients were included, 50 consecutive patients with RT-PCR confirmed COVID-19 from our local institutional database, and another 200 patients from four international datasets (n=50 each). Lung involvement was scored semi-quantitatively by three experienced radiologists according to the established chest CT score (CCS) ranging from 0-25. Inter-rater reliability was reported by the intraclass correlation coefficient (ICC). Deep-learning-based segmentation of ground-glass and consolidation was obtained by CT Pulmo Auto Results prototype plugin on IntelliSpace Discovery (Philips Healthcare, The Netherlands). Threshold-based segmentation of involved lung was implemented using an open-source tool for whole-lung segmentation under the presence of severe pathologies (R231CovidWeb, Hofmanninger et al., 2020) and consecutive quantitative assessment of lung attenuation. The best threshold was investigated by training and testing a linear regression of deep-learning and threshold-based results in a five-fold cross validation strategy. Results: Median CCS among 250 evaluated patients was 10 [6-15]. Inter-rater reliability of the CCS was excellent [ICC 0.97 (0.97-0.98)]. Best attenuation threshold for identification of involved lung was −522 HU.While the relationship of deep-learning-and threshold-based quantification was linear and strong (r 2 deep-learning vs. threshold =0.84), both automated quantification methods translated to the semi-quantitative CCS in a nonlinear fashion, with an increasing slope towards higher CCS (r 2 deep-learning vs. CCS = 0.80, r 2 threshold vs. CCS =0.63).

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A simple chest CT score for assessing the severity of pulmonary involvement in COVID-19

Cited by 15 publications

References 15 publications

Association of chest CT severity score with mortality of COVID-19 patients: a systematic review and meta-analysis

Association of chest CT severity score with mortality of COVID-19 patients: a systematic review and meta-analysis

Prognostic accuracy of visual lung damage computed tomography score for mortality prediction in patients with COVID-19 pneumonia: a systematic review and meta-analysis

Assessment of COVID-19 lung involvement on computed tomography by deep-learning-, threshold-, and human reader-based approaches—an international, multi-center comparative study

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