Chest computed tomography findings of COVID-19 pneumonia: pictorial essay with literature review

Cellina, Michaela; Orsi, Marcello; Pittino, Carlo Valenti; Toluian, Tahereh; Oliva, Giancarlo

doi:10.1007/s11604-020-01010-7

Cited by 40 publications

(32 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Chest-radiographs can be normal in early or mild disease. In this context, Chest CT is strongly recommended in suspected cases for both initial evaluation and follow-up [4,7]. A wide variety of CT findings have been reported in COVID-19 patients in different studies [8,9].…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Chest CT Findings after 4 Months from the Onset of COVID-19 Pneumonia: A Case Series

Urciuoli¹,

Guerriero

2020

Diagnostics

View full text Add to dashboard Cite

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Although the reference standard for SARS-CoV-2 diagnosis is real-time reverse transcription polymerase chain reaction (RT-PCR), computed tomography (CT) is recommended for both initial evaluation and follow-up. There is a growing body of published evidence about CT evolution during the course of COVID-19 pneumonia. Here, we report six confirmed cases of COVID-19 patients who underwent unenhanced chest CT on admission and after 4 months from the onset of symptoms. Chest-CT at first admission showed the typical CT features of COVID-19. Interestingly, the follow-up CT revealed the persistence of lung abnormalities in five cases even if all the patients were completely asymptomatic. Further studies are needed for a comprehensive understanding of the disease progression and the resulting late imaging modifications.

show abstract

Section: Discussionmentioning

confidence: 99%

“…Although the reference standard for SARS-CoV-2 diagnosis is real-time reverse transcription polymerase chain reaction (RT-PCR), computed tomography (CT) is recommended for both initial evaluation and follow-up [3]. CT findings of COVID-19 pneumonia vary with time and different temporal stages have been described [4].…”

Section: Introductionmentioning

confidence: 99%

Chest CT Findings after 4 Months from the Onset of COVID-19 Pneumonia: A Case Series

Urciuoli¹,

Guerriero

2020

Diagnostics

View full text Add to dashboard Cite

show abstract

“…Before lung lobe and lesion segmentation, the images were resampled to voxel size 1 × 1 × 1 mm 3 , and a Gaussian filter was applied for denoising. Then, a fully automatic segmentation of three-dimensional lung lobes and lesions based on deep learning algorithms was performed.…”

Section: Lobe and Lesion Segmentationmentioning

confidence: 99%

“…Chest computed tomography (CT) can intuitively demonstrate the lung lesions and its manifestations of COVID-19 pneumonia have been reported in many studies [2][3][4]. Chest CT exams are useful in supplementary diagnosis of RT-PCR tests [5][6][7], evaluating disease stages [2,3,8,9] and severity [10][11][12]. Recently, deep learning techniques have been widely used in the detection and segmentation of COVID-19 lesions in chest CT images [13][14][15][16].…”

mentioning

confidence: 99%

A model based on CT radiomic features for predicting RT-PCR becoming negative in coronavirus disease 2019 (COVID-19) patients

Cai

Gao

et al. 2020

BMC Med Imaging

View full text Add to dashboard Cite

Background Coronavirus disease 2019 (COVID-19) has emerged as a global pandemic. According to the diagnosis and treatment guidelines of China, negative reverse transcription-polymerase chain reaction (RT-PCR) is the key criterion for discharging COVID-19 patients. However, repeated RT-PCR tests lead to medical waste and prolonged hospital stays for COVID-19 patients during the recovery period. Our purpose is to assess a model based on chest computed tomography (CT) radiomic features and clinical characteristics to predict RT-PCR negativity during clinical treatment. Methods From February 10 to March 10, 2020, 203 mild COVID-19 patients in Fangcang Shelter Hospital were retrospectively included (training: n = 141; testing: n = 62), and clinical characteristics were collected. Lung abnormalities on chest CT images were segmented with a deep learning algorithm. CT quantitative features and radiomic features were automatically extracted. Clinical characteristics and CT quantitative features were compared between RT-PCR-negative and RT-PCR-positive groups. Univariate logistic regression and Spearman correlation analyses identified the strongest features associated with RT-PCR negativity, and a multivariate logistic regression model was established. The diagnostic performance was evaluated for both cohorts. Results The RT-PCR-negative group had a longer time interval from symptom onset to CT exams than the RT-PCR-positive group (median 23 vs. 16 days, p < 0.001). There was no significant difference in the other clinical characteristics or CT quantitative features. In addition to the time interval from symptom onset to CT exams, nine CT radiomic features were selected for the model. ROC curve analysis revealed AUCs of 0.811 and 0.812 for differentiating the RT-PCR-negative group, with sensitivity/specificity of 0.765/0.625 and 0.784/0.600 in the training and testing datasets, respectively. Conclusion The model combining CT radiomic features and clinical data helped predict RT-PCR negativity during clinical treatment, indicating the proper time for RT-PCR retesting.

show abstract

“…Before lung lobe and lesion segmentation, the images were resampled to voxel size 1 × 1 × 1 mm 3 , and a Gaussian lter was applied for denoising. Then, a fully automatic segmentation of three-dimensional lung lobes and lesions based on deep learning algorithms was performed.…”

Section: Lobe and Lesion Segmentationmentioning

confidence: 99%

A model based on CT radiomic features for predicting RT-PCR becoming negative in coronavirus disease 2019 (COVID-19) patients

Cai

Gao

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: Coronavirus disease 2019 (COVID-19) has emerged as a global pandemic. According to the diagnosis and treatment guidelines of China, negative reverse transcription-polymerase chain reaction (RT-PCR) is the key criterion for discharging COVID-19 patients. However, repeated RT-PCR tests lead to medical waste and prolonged hospital stays for COVID-19 patients during the recovery period. Our purpose is to assess a model based on chest computed tomography (CT) radiomic features and clinical characteristics to predict RT-PCR negativity during clinical treatment. Methods: From February 10 to March 10, 2020, 203 mild COVID-19 patients in Fangcang Shelter Hospital were retrospectively included (training: n=141; testing: n=62), and clinical characteristics were collected. Lung abnormalities on chest CT images were segmented with a deep learning algorithm. CT quantitative features and radiomic features were automatically extracted. Clinical characteristics and CT quantitative features were compared between RT-PCR-negative and RT-PCR-positive groups. Univariate logistic regression and Spearman correlation analyses identified the strongest features associated with RT-PCR negativity, and a multivariate logistic regression model was established. The diagnostic performance was evaluated for both cohorts. Results: The RT-PCR-negative group had a longer time interval from symptom onset to CT exams than the RT-PCR-positive group (median 23 vs. 16 days, p<0.001). There was no significant difference in the other clinical characteristics or CT quantitative features. In addition to the time interval from symptom onset to CT exams, nine CT radiomic features were selected for the model. ROC curve analysis revealed AUCs of 0.811 and 0.812 for differentiating the RT-PCR-negative group, with sensitivity/specificity of 0.765/0.625 and 0.784/0.600 in the training and testing datasets, respectively. Conclusion: The model combining CT radiomic features and clinical data helped predict RT-PCR negativity during clinical treatment, indicating the proper time for RT-PCR retesting.

show abstract

Chest computed tomography findings of COVID-19 pneumonia: pictorial essay with literature review

Cited by 40 publications

References 47 publications

Chest CT Findings after 4 Months from the Onset of COVID-19 Pneumonia: A Case Series

Chest CT Findings after 4 Months from the Onset of COVID-19 Pneumonia: A Case Series

A model based on CT radiomic features for predicting RT-PCR becoming negative in coronavirus disease 2019 (COVID-19) patients

A model based on CT radiomic features for predicting RT-PCR becoming negative in coronavirus disease 2019 (COVID-19) patients

Contact Info

Product

Resources

About