Computed tomography and pathology evaluation of lung ground‑glass opacity

Yu, Hui; Liu, Shihe; Zhang, Chuanyu; Li, Shaoke; Ren, Jianan; Zhang, Jingli; Xu, Wenjian

doi:10.3892/etm.2018.6886

“…Ground-glass opacity (GGO) refers to hazy areas with mildly increased density in lungs, but without obscuration of bronchial and vascular margins, which generally is due to destruction of alveolar walls and lling of the cavity with serous, cells and hemorrhage as well as local interstitial brosis. (7). Consolidation opacity is caused by any pathologic process that diffuse alveolar injury in both lungs with brous mucinous exudate.…”

Section: Discussionmentioning

confidence: 99%

Prediction of COVID-19 prognosis by heterogeneity analysis based on chest CT scans

Yang¹,

Zhang²,

Zou³

et al. 2020

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This study aimed to estimate clinical outcome in individual COVID-19 patient by using histogram heterogeneity analysis based on CT opacities. 57 COVID-19 cases’ medical records were retrospectively reviewed from a designated hospital in Wuhan, China. Two characteristic lung abnormity opacities, ground-glass opacity (GGO) and consolidation opacity (CLO) were drawn on CT images to identify the heterogeneity by using quantitative histogram analysis. The parameters (mean, mode, kurtosis, skewness) derived from histograms evaluate the accuracy of clinical classification and outcome prediction. Nomograms were built to predict the risk of death and median length of hospital stays (LOS), respectively. The most highly frequency of lung abnormalities was GGO mixed with CLO in both survival population (26 in 42, 61.9%) and died population (10 in 15, 66.7%). The best performance heterogeneity parameters to discriminate severe type from mild/moderate counterparts were as following: GGO_skewness: specificity=66.67%, sensitivity=78.12%, AUC=0.706; CLO_mean: specificity=70.00%, sensitivity=76.92%, AUC=0.746. Nomogram based on histogram parameters can predict the individual risk of death and the prolonged median LOS of COVID-19 patients. C-indexes were 0.763 and 0.888 for risk of death and prolonged median LOS, respectively. The histogram analysis method based on GGO and CLO has the ability for individual risk prediction in COVID-19 patients.

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“…GGO and CLO are the typical lung abnormalities on CT imaging of COVID-19, and are highly heterogeneity at histology and cellular level. GGO lesions can be caused by numerous pathology changes, which generally present as incomplete lling of the alveolar cavity with cells and liquids (such as edema and hemorrhage), or lung interstitial thickening due to in ammation, edema (7). Consolidation opacity is caused from any pathologic process that lls the alveoli with uid, pus, blood, cells or other substances resulting in lobar, diffuse or multifocal ill-de ned opacities.…”

Section: Discussionmentioning

confidence: 99%

Prediction of COVID-19 prognosis by heterogeneity analysis based on chest CT scans

Yang

¹

,

Zhang²,

Zou

³

et al. 2020

Preprint

0

View full text Add to dashboard Cite

This study aimed to estimate clinical outcome in individual COVID-19 patient by using histogram heterogeneity analysis based on CT opacities. 57 COVID-19 cases’ medical records were retrospectively reviewed from a designated hospital in Wuhan, China. Two characteristic lung abnormity opacities, ground-glass opacity (GGO) and consolidation opacity (CLO) were drawn on CT images to identify the heterogeneity by using quantitative histogram analysis. The parameters (mean, mode, kurtosis, skewness) derived from histograms evaluate the accuracy of clinical classification and outcome prediction. Nomograms were built to predict the risk of death and median length of hospital stays (LOS), respectively. The most highly frequency of lung abnormalities was GGO mixed with CLO in both survival population (26 in 42, 61.9%) and died population (10 in 15, 66.7%). The best performance heterogeneity parameters to discriminate severe type from mild/moderate counterparts were as following: GGO_skewness: specificity=66.67%, sensitivity=78.12%, AUC=0.706; CLO_mean: specificity=70.00%, sensitivity=76.92%, AUC=0.746. Nomogram based on histogram parameters can predict the individual risk of death and the prolonged median LOS of COVID-19 patients. C-indexes were 0.763 and 0.888 for risk of death and prolonged median LOS, respectively. The histogram analysis method based on GGO and CLO has the ability for individual risk prediction in COVID-19 patients.

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“…Although GGO can be seen in various pathologies, its pattern and distribution along with the clinical picture can favor one diagnosis over the other. The typical findings in patients with negative RT-PCR results can be attribute to other pathologies mimicking the typical CT appearance; such as influenza pneumonia, organizing pneumonia like in drug toxicity and connective tissue disease (20). Recently, new publications aim to differentiate Influenza A from COVID-19 pneumonia by identifying specific CT imaging features (21,22).…”

Section: Discussionmentioning

confidence: 99%

Chest CT features of COVID-19 in the region of Abu Dhabi, UAE- A single institute study

Saeed

¹

,

Helali

²

,

Almazrouei

³

et al. 2020

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OBJECTIVEOur aim is to investigate high resolution CT features of COVID-19 infection in Abu Dhabi, UAE, and to compare the diagnostic performance of CT scan with RT-PCR test.METHODSData of consecutive patients who were suspected to have COVID-19 infection and presented to our hospital, was collected from March 2, 2020, until April 12, 2020. All patients underwent RT-PCR test; out of which 53.8% had chest CT scan done. Using RT-PCR as a standard reference, the sensitivity and specify of CT scan was calculated. We also analyzed the most common imaging findings in patients with positive RT-PCR results.RESULTSThe typical HRCT findings were seen in 50 scans (65.8%) out of total positive ones; 44 (77.2%) with positive RT-PCR results and 6 (31.6%) with negative results. The peripheral disease distribution was seen in 86%, multilobe involvement in 70%, bilateral in 82%, and posterior in 82% of the 50 scans.The ground glass opacities were seen in 50/74 (89.3%) of positive RT-PCR group. The recognized GGO patterns in these scans were: rounded 50%, linear 38%, and crazy-paving 24%.Using RT-PCR as a standard of reference, chest HRCT scan revealed sensitivity of 68.8% and specificity of 70%.CONCLUSIONThe commonest HRCT findings in patients with COVID-19 pneumonia were peripheral, posterior, bilateral, multilobe rounded ground glass opacities.

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Computed tomography and pathology evaluation of lung ground‑glass opacity

Cited by 12 publications

References 15 publications

Prediction of COVID-19 prognosis by heterogeneity analysis based on chest CT scans

Prediction of COVID-19 prognosis by heterogeneity analysis based on chest CT scans

Prediction of COVID-19 prognosis by heterogeneity analysis based on chest CT scans

Chest CT features of COVID-19 in the region of Abu Dhabi, UAE- A single institute study

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