Prediction models for respiratory outcomes in patients with COVID-19: integration of quantitative computed tomography parameters, demographics, and laboratory features.

Background: We aimed to develop integrative machine-learning models using quantitative computed tomography (CT) parameters in addition to initial clinical features to predict the respiratory outcomes of coronavirus disease 2019 (COVID-19). Methods: This was a retrospective study involving 387 patients with COVID-19. Demographic, initial laboratory, and quantitative CT findings were used to develop predictive models of respiratory outcomes. High-attenuation area (HAA) (%) and consolidation (%) were defined as quantified percentages of the area with Hounsfield units between -600 and -250 and between -100 and 0, respectively. Respiratory outcomes were defined as the development of pneumonia, hypoxia, or respiratory failure. Multivariable logistic regression and random forest models were developed for each respiratory outcome. The performance of the logistic regression model was evaluated using the area under the receiver operating characteristic curve (AUC). The accuracy of the developed models was validated by 10-fold cross-validation. Results: A total of 195 (50.4%), 85 (22.0%), and 19 (4.9%) patients developed pneumonia, hypoxia, and respiratory failure, respectively. The mean patient age was 57.8 years, and 194 (50.1%) were female. In the multivariable analysis, vaccination status and levels of lactate dehydrogenase, C-reactive protein (CRP), and fibrinogen were independent predictors of pneumonia. The presence of hypertension, levels of lactate dehydrogenase and CRP, HAA (%), and consolidation (%) were selected as independent variables to predict hypoxia. For respiratory failure, the presence of diabetes, levels of aspartate aminotransferase, and CRP, and HAA (%) were selected. The AUCs of the prediction models for pneumonia, hypoxia, and respiratory failure were 0.904, 0.890, and 0.969, respectively. Using the feature selection in the random forest model, HAA (%) was ranked as one of the top 10 features predicting pneumonia and hypoxia and was first place for respiratory failure. The accuracies of the cross-validation of the random forest models using the top 10 features for pneumonia, hypoxia, and respiratory failure were 0.872, 0.878, and 0.945, respectively. Conclusions: Our prediction models that incorporated quantitative CT parameters into clinical and laboratory variables showed good performance with high accuracy.

CT imaging findings in lung transplant recipients with COVID-19.

OBJECTIVES: Our goal was to compare the chest computed tomography (CT) imaging findings of COVID-19 in lung transplant recipients (LTR) and a group of non-transplanted controls (NTC). METHODS: This retrospective study included 51 consecutive LTR hospitalized with COVID-19 from two centers. A total of 75 NTC were included for comparison. Images were classified regarding the standardized RSNA category, main pattern of lung attenuation, and longitudinal and axial distribution. Quantitative CT (QCT) analysis was performed to evaluate percentage of high attenuation areas (%HAA, threshold -250 to -700 HU). CT scoring was used to measure severity of parenchymal abnormalities. RESULTS: The imaging findings of COVID-19 in LTR were significantly different from controls regarding the RSNA classification and pattern of lung attenuation. LTR had a significantly higher proportion of patients with an indeterminate pattern on CT (0.31 vs. 0.11, p = 0.014). The most frequent pattern of attenuation in LTR was predominantly consolidation (0.39 vs. 0.22, p = 0.144) followed by a mixed pattern of ground-glass opacities (GGO) and consolidation (0.37 vs. 0.20, adjusted p = 0.102). On the other hand, the most common pattern in NTC was GGO predominant (0.58 vs. 0.24 of LTR, p = 0.001). LTR had significantly more severe parenchymal disease measured by CT score and %HAA by QCT (0.372 ± 0.08 vs. 0.148 ± 0.06, p < 0.001). CONCLUSION: The most frequent finding of COVID-19 in LTR is a predominant pattern of consolidation. Compared to NTC, LTR more frequently demonstrated an indeterminate pattern according to the RSNA classification and more extensive lung abnormalities on QCT and semi-quantitative scoring. KEY POINTS: • The most common CT finding of COVID-19 in LTR is a predominant pattern of consolidation followed by a mixed pattern of GGO and consolidation, while controls more often have a predominant pattern of GGO. • LTR more often presents with an indeterminate pattern of COVID-19 by RSNA classification than controls; therefore, molecular testing for COVID-19 is essential for LTR presenting with lower airway infection independently of imaging findings. • LTR had more extensive disease by semi-quantitative CT score and increased percentage areas of high attenuation on QCT.

Quantitative Chest CT Analysis to Measure Short-Term Sequelae of COVID-19 Pneumonia: A Monocentric Prospective Study.

(1) Background: Quantitative CT analysis (QCT) has demonstrated promising results in the prognosis prediction of patients affected by COVID-19. We implemented QCT not only at diagnosis but also at short-term follow-up, pairing it with a clinical examination in search of a correlation between residual respiratory symptoms and abnormal QCT results. (2) Methods: In this prospective monocentric trial performed during the "first wave" of the Italian pandemic, i.e., from March to May 2020, we aimed to test the relationship between %deltaCL (variation of %CL-compromised lung volume) and variations of symptoms-dyspnea, cough and chest pain-at follow-up clinical assessment after hospitalization. (3) Results: 282 patients (95 females, 34%) with a median age of 60 years (IQR, 51-69) were included. We reported a correlation between changing lung abnormalities measured by QCT, and residual symptoms at short-term follow up after COVID-19 pneumonia. Independently from age, a low percentage of surviving patients (1-4%) may present residual respiratory symptoms at approximately two months after discharge. QCT was able to quantify the extent of residual lung damage underlying such symptoms, as the reduction of both %PAL (poorly aerated lung) and %CL volumes was correlated to their disappearance. (4) Conclusions QCT may be used as an objective metric for the measurement of COVID-19 sequelae.

Quantitative Computed Tomography Parameters in Coronavirus Disease 2019 Patients and Prediction of Respiratory Outcomes Using a Decision Tree.

Background: Chest computed tomography (CT) scans play an important role in the diagnosis of coronavirus disease 2019 (COVID-19). This study aimed to describe the quantitative CT parameters in COVID-19 patients according to disease severity and build decision trees for predicting respiratory outcomes using the quantitative CT parameters. Methods: Patients hospitalized for COVID-19 were classified based on the level of disease severity: (1) no pneumonia or hypoxia, (2) pneumonia without hypoxia, (3) hypoxia without respiratory failure, and (4) respiratory failure. High attenuation area (HAA) was defined as the quantified percentage of imaged lung volume with attenuation values between -600 and -250 Hounsfield units (HU). Decision tree models were built with clinical variables and initial laboratory values (model 1) and including quantitative CT parameters in addition to them (model 2). Results: A total of 387 patients were analyzed. The mean age was 57.8 years, and 50.3% were women. HAA increased as the severity of respiratory outcome increased. HAA showed a moderate correlation with lactate dehydrogenases (LDH) and C-reactive protein (CRP). In the decision tree of model 1, the CRP, fibrinogen, LDH, and gene Ct value were chosen as classifiers whereas LDH, HAA, fibrinogen, vaccination status, and neutrophil (%) were chosen in model 2. For predicting respiratory failure, the decision tree built with quantitative CT parameters showed a greater accuracy than the model without CT parameters. Conclusions: The decision tree could provide higher accuracy for predicting respiratory failure when quantitative CT parameters were considered in addition to clinical characteristics, PCR Ct value, and blood biomarkers.

Incubation period, clinical and lung CT features for early prediction of COVID-19 deterioration: development and internal verification of a risk model.

BACKGROUND: Most severe, critical, or mortal COVID-19 cases often had a relatively stable period before their status worsened. We developed a deterioration risk model of COVID-19 (DRM-COVID-19) to predict exacerbation risk and optimize disease management on admission. METHOD: We conducted a multicenter retrospective cohort study with 239 confirmed symptomatic COVID-19 patients. A combination of the least absolute shrinkage and selection operator (LASSO), change-in-estimate (CIE) screened out independent risk factors for the multivariate logistic regression model (DRM-COVID-19) from 44 variables, including epidemiological, demographic, clinical, and lung CT features. The compound study endpoint was progression to severe, critical, or mortal status. Additionally, the model's performance was evaluated for discrimination, accuracy, calibration, and clinical utility, through internal validation using bootstrap resampling (1000 times). We used a nomogram and a network platform for model visualization. RESULTS: In the cohort study, 62 cases reached the compound endpoint, including 42 severe, 18 critical, and two mortal cases. DRM-COVID-19 included six factors: dyspnea [odds ratio (OR) 4.89;confidence interval (95% CI) 1.53-15.80], incubation period (OR 0.83; 95% CI 0.68-0.99), number of comorbidities (OR 1.76; 95% CI 1.03-3.05), D-dimer (OR 7.05; 95% CI, 1.35-45.7), C-reactive protein (OR 1.06; 95% CI 1.02-1.1), and semi-quantitative CT score (OR 1.50; 95% CI 1.27-1.82). The model showed good fitting (Hosmer-Lemeshow goodness, X2(8) = 7.0194, P = 0.53), high discrimination (the area under the receiver operating characteristic curve, AUROC, 0.971; 95% CI, 0.949-0.992), precision (Brier score = 0.051) as well as excellent calibration and clinical benefits. The precision-recall (PR) curve showed excellent classification performance of the model (AUCPR = 0.934). We prepared a nomogram and a freely available online prediction platform ( https://deterioration-risk-model-of-covid-19.shinyapps.io/DRMapp/ ). CONCLUSION: We developed a predictive model, which includes the including incubation period along with clinical and lung CT features. The model presented satisfactory prediction and discrimination performance for COVID-19 patients who might progress from mild or moderate to severe or critical on admission, improving the clinical prognosis and optimizing the medical resources.

Prognostic value of semi-quantitative CT-based score integrated with cardiovascular risk factors during the first peak of the COVID-19 pandemic: A new score to predict poor outcome.

PURPOSE: Predicting the clinical course of COVID-19 pneumonia is of high clinical importance and may change treatment strategies. This study aimed to compare the semi-quantitative CT score (radiological score), mCHA2DS2-VASc score (clinical score), and R-mCHA2DS2-VASc score (clinical and radiological score) to predict the risk of ICU admission and mortality in COVID 19 pneumonia. METHODS: This study retrospectively evaluated 901 COVID-19 pneumonia cases with positive PCR results. The mCHA2DS2-VASc score was calculated based on clinical risk factors. CT images were evaluated, and the semi-quantitative CT scores were obtained. A new scoring method (R-mCHA2DS2-VASc score) was developed by combining these scores. The performance of the mCHA2DS2-VASc score, semi-quantitative CT score, and a combination of these scores (R-mCHA2DS2-VASc score) was evaluated using ROC analysis. RESULTS: The ROC curves of the semi-quantitative CT, mCHA2DS2-VASc, and R-mCHA2DS2-VASc scores were examined. The semi-quantitative CT, mCHA2DS2-VASc, and R-mCHA2DS2-VASc scores were significant in predicting intensive care unit (ICU) admission and mortality (p < 0.001). The R-mCHA2DS2-VASc score performed best in predicting a severe clinical course, and the cut-off value of 8 for the R-mCHA2DS2-VASc score had 83.9% sensitivity and 91.6% specificity for mortality. CONCLUSIONS: The R-mCHA2DS2-VASc score includes both clinical and radiological parameters. It is a feasible scoring method for predicting a severe clinical course at an early stage with high sensitivity and specificity values. However, prospective studies with larger sample sizes are warranted.

CT findings in survivors and non-survivors of COVID-19 and clinical usefulness of a CT scoring system.

BACKGROUND: Many patients with coronavirus disease 2019 (COVID-19) have been diagnosed with computed tomography (CT). A prognostic tool based on CT findings could be useful for predicting death from COVID-19. OBJECTIVES: To compare the chest CT findings of patients who survived COVID-19 versus those of patients who died of COVID-19 and to determine the usefulness the clinical usefulness of a CT scoring system for COVID-19. METHODS: We included 124 patients with confirmed SARS-CoV-2 infections who were hospitalized between April 1, 2020 and July 25, 2020. RESULTS: Whereas ground-glass opacities were the most common characteristic finding in survivors (75%), crazy paving was the most characteristic finding in non-survivors (65%). Atypical findings were present in 46% of patients. The chest CT score was directly proportional to mortality; a score ≥18 was the best cutoff for predicting death, yielding 70% sensitivity (95%CI: 47%-87%). CONCLUSIONS: Our results suggest that atypical lesions are more prevalent in this cohort. The chest CT score had high sensitivity for predicting hospital mortality.

Quantitative CT comparison between COVID-19 and mycoplasma pneumonia suspected as COVID-19: a longitudinal study.

OBJECTIVE: The purpose of this study was to compare imaging features between COVID-19 and mycoplasma pneumonia (MP). MATERIALS AND METHODS: The data of patients with mild COVID-19 and MP who underwent chest computed tomography (CT) examination from February 1, 2020 to April 17, 2020 were retrospectively analyzed. The Pneumonia-CT-LKM-PP model based on a deep learning algorithm was used to automatically quantify the number, volume, and involved lobes of pulmonary lesions, and longitudinal changes in quantitative parameters were assessed in three CT follow-ups. RESULTS: A total of 10 patients with mild COVID-19 and 13 patients with MP were included in this study. There was no difference in lymphocyte counts at baseline between the two groups (1.43 ± 0.45 vs. 1.44 ± 0.50, p = 0.279). C-reactive protein levels were significantly higher in MP group than in COVID-19 group (p < 0.05). The number, volume, and involved lobes of pulmonary lesions reached a peak in 7-14 days in the COVID-19 group, but there was no peak or declining trend over time in the MP group (p < 0.05). CONCLUSION: Based on the longitudinal changes of quantitative CT, pulmonary lesions peaked at 7-14 days in patients with COVID-19, and this may be useful to distinguish COVID-19 from MP and evaluate curative effects and prognosis.

[CT findings in survivors and non-survivors of COVID-19 and clinical usefulness of a CT scoring system]. / Hallazgos tomográficos entre sobrevivientes y no-sobrevivientes con COVID-19 y utilidad clínica de una puntuación de tomografía torácica.

Background: Many patients with coronavirus disease 2019 (COVID-19) have been diagnosed with computed tomography (CT). A prognostic tool based on CT findings could be useful for predicting death from COVID-19. Objectives: To compare the chest CT findings of patients who survived COVID-19 versus those of patients who died of COVID-19 and to determine the usefulness the clinical usefulness of a CT scoring system for COVID-19. Methods: We included 124 patients with confirmed SARS-CoV-2 infections who were hospitalized between April 1, 2020 and July 25, 2020. Results: Whereas ground-glass opacities were the most common characteristic finding in survivors (75%), crazy paving was the most characteristic finding in non-survivors (65%). Atypical findings were present in 46% of patients. The chest CT score was directly proportional to mortality; a score ≥ 18 was the best cutoff for predicting death, yielding 70% sensitivity (95%CI: 47%-87%). Conclusions: Our results suggest that atypical lesions are more prevalent in this cohort. The chest CT score had high sensitivity for predicting hospital mortality.

The Differences and Changes of Semi-Quantitative and Quantitative CT Features of Coronavirus Disease 2019 Pneumonia in Patients With or Without Smoking History.

Objective: To assess CT features of COVID-19 patients with different smoking status using quantitative and semi-quantitative technologies and to investigate changes of CT features in different disease states between the two groups. Methods: 30 COVID-19 patients with current smoking status (29 men, 1 woman) admitted in our database were enrolled as smoking group and 56 COVID-19 patients without smoking history (24 men, 32 women) admitted during the same period were enrolled as a control group. Twenty-seven smoking cases and 55 control cases reached recovery standard and were discharged. Initial and follow-up CT during hospitalization and follow-up CT after discharge were acquired. Thirty quantitative features, including the ratio of infection volume and visual-assessed interstitial changes score including total score, score of ground glass opacity, consolidation, septal thickening, reticulation and honeycombing sign, were analyzed. Results: Initial CT images of the smoking group showed higher scores of septal thickening [4.5 (0-5) vs. 0 (0-4), p = 0.001] and reticulation [0 (0-5.25) vs 0 (0-0), p = 0.001] as well as higher total score [7 (5-12.25) vs. 6 (5-7), p = 0.008] with statistical significance than in the control group. The score of reticulation was higher in the smoking group than in the control group when discharged [0.89 (0-0) vs. 0.09 (0-0), p = 0.02]. The score of septal thickening tended to be higher in the smoking group than the control group [4 (0-4) vs. 0 (0-4), p = 0.007] after being discharged. Quantitative CT features including infection ratio of whole lung and left lung as well as infection ratio within HU (-750, -300) and within HU (-300, 49) were higher in the control group of initial CT with statistical differences. The infection ratio of whole lung and left lung, infection ratio within HU (-750), and within HU (-750, -300) were higher in the control group with statistical differences when discharged. This trend turned adverse after discharge and the values of quantitative features were generally higher in the smoking group than in the control group without statistical differences. Conclusions: Patients with a history of smoking presented more severe interstitial manifestations and more residual lesion after being discharged. More support should be given for COVID-19 patients with a smoking history during hospitalization and after discharge.

Quantitative CT for detecting COVID­19 pneumonia in suspected cases.

BACKGROUND: Corona Virus Disease 2019 (COVID-19) is currently a worldwide pandemic and has a huge impact on public health and socio-economic development. The purpose of this study is to explore the diagnostic value of the quantitative computed tomography (CT) method by using different threshold segmentation techniques to distinguish between patients with or without COVID-19 pneumonia. METHODS: A total of 47 patients with suspected COVID-19 were retrospectively analyzed, including nine patients with positive real-time fluorescence reverse transcription polymerase chain reaction (RT-PCR) test (confirmed case group) and 38 patients with negative RT-PCR test (excluded case group). An improved 3D convolutional neural network (VB-Net) was used to automatically extract lung lesions. Eight different threshold segmentation methods were used to define the ground glass opacity (GGO) and consolidation. The receiver operating characteristic (ROC) curves were used to compare the performance of various parameters with different thresholds for diagnosing COVID-19 pneumonia. RESULTS: The volume of GGO (VOGGO) and GGO percentage in the whole lung (GGOPITWL) were the most effective values for diagnosing COVID-19 at a threshold of - 300 HU, with areas under the curve (AUCs) of 0.769 and 0.769, sensitivity of 66.67 and 66.67%, specificity of 94.74 and 86.84%. Compared with VOGGO or GGOPITWL at a threshold of - 300 Hounsfield units (HU), the consolidation percentage in the whole lung (CPITWL) with thresholds at - 400 HU, - 350 HU, and - 250 HU were statistically different. There were statistical differences in the infection volume and percentage of the whole lung, right lung, and lobes between the two groups. VOGGO, GGOPITWL, and volume of consolidation (VOC) were also statistically different at the threshold of - 300 HU. CONCLUSIONS: Quantitative CT provides an image quantification method for the auxiliary diagnosis of COVID-19 and is expected to assist in confirming patients with COVID-19 pneumonia in suspected cases.

Automated AI-Driven CT Quantification of Lung Disease Predicts Adverse Outcomes in Patients Hospitalized for COVID-19 Pneumonia.

The purpose of our work was to assess the independent and incremental value of AI-derived quantitative determination of lung lesions extent on initial CT scan for the prediction of clinical deterioration or death in patients hospitalized with COVID-19 pneumonia. 323 consecutive patients (mean age 65 ± 15 years, 192 men), with laboratory-confirmed COVID-19 and an abnormal chest CT scan, were admitted to the hospital between March and December 2020. The extent of consolidation and all lung opacities were quantified on an initial CT scan using a 3D automatic AI-based software. The outcome was known for all these patients. 85 (26.3%) patients died or experienced clinical deterioration, defined as intensive care unit admission. In multivariate regression based on clinical, biological and CT parameters, the extent of all opacities, and extent of consolidation were independent predictors of adverse outcomes, as were diabetes, heart disease, C-reactive protein, and neutrophils/lymphocytes ratio. The association of CT-derived measures with clinical and biological parameters significantly improved the risk prediction (p = 0.049). Automated quantification of lung disease at CT in COVID-19 pneumonia is useful to predict clinical deterioration or in-hospital death. Its combination with clinical and biological data improves risk prediction.

Quantitative assessment of lung involvement on chest CT at admission: Impact on hypoxia and outcome in COVID-19 patients.

BACKGROUND: The aim of this study was to quantify COVID-19 pneumonia features using CT performed at time of admission to emergency department in order to predict patients' hypoxia during the hospitalization and outcome. METHODS: Consecutive chest CT performed in the emergency department between March 1st and April 7th 2020 for COVID-19 pneumonia were analyzed. The three features of pneumonia (GGO, semi-consolidation and consolidation) and the percentage of well-aerated lung were quantified using a HU threshold based software. ROC curves identified the optimal cut-off values of CT parameters to predict hypoxia worsening and hospital discharge. Multiple Cox proportional hazards regression was used to analyze the capability of CT quantitative features, demographic and clinical variables to predict the time to hospital discharge. RESULTS: Seventy-seven patients (median age 56-years-old, 51 men) with COVID-19 pneumonia at CT were enrolled. The quantitative features of COVID-19 pneumonia were not associated to age, sex and time-from-symptoms onset, whereas higher number of comorbidities was correlated to lower well-aerated parenchyma ratio (rho = -0.234, p = 0.04) and increased semi-consolidation ratio (rho = -0.303, p = 0.008). Well-aerated lung (≤57%), semi-consolidation (≥17%) and consolidation (≥9%) predicted worst hypoxemia during hospitalization, with moderate areas under curves (AUC 0.76, 0.75, 0.77, respectively). Multiple Cox regression identified younger age (p < 0.01), female sex (p < 0.001), longer time-from-symptoms onset (p = 0.049), semi-consolidation ≤17% (p < 0.01) and consolidation ≤13% (p = 0.03) as independent predictors of shorter time to hospital discharge. CONCLUSION: Quantification of pneumonia features on admitting chest CT predicted hypoxia worsening during hospitalization and time to hospital discharge in COVID-19 patients.

Changes of quantitative CT-based airway wall dimensions in patients with COVID-19 during early recovery.

BACKGROUND: As the coronavirus disease 19 (COVID-19) pandemic evolves, the need for recognizing the structural pulmonary changes of the disease during early convalescence has emerged. Most studies focus on parenchymal destruction of the disease; but little is known about whether the disease affects the airway. This study was conducted to investigate the changes in airway dimensions and explore the associated factors during early convalescence in patients with COVID-19. METHODS: We retrospectively analyzed quantitative computed tomography (CT)-based airway measures of 69 patients with COVID-19 from 5 February to 17 March 2020, and 32 non-COVID-19 participants from 1 January 2018 to 31 December 2019 from Guangzhou, China. The well-established measures of wall area fraction and the square root of the wall area of a hypothetical bronchus with an inner perimeter of 10 mm, were used to describe airway wall dimensions. We described the characteristics of the dimensions and inner area of airways in 66 patients with COVID-19 at the initial and convalescent stages of the disease, and compared them with the non-COVID-19 group. Linear regression models were constructed to investigate the association of airway dimensions with duration of hospitalization or disease severity after recovery. Partial correlation coefficients were calculated to investigate whether inflammatory markers were related to airway dimensions. RESULTS: Among 66 patients with COVID-19, airway dimensions were greater during disease initiation than early convalescence, which was significantly greater than in non-COVID-19 participants. No significant difference was found between the patients with COVID-19 at the initial stage and the non-COVID-19 controls regarding the first to eighth generations of the inner area. In adjusted regression models, duration of hospitalization was negatively associated with wall area fraction of the first to the sixth generation of airways. No significant associations exist between airway dimensions and disease severity, or airway dimensions with inflammatory markers. CONCLUSIONS: Airway dimensions in patients with COVID-19 during disease initiation are greater than those in non-COVID-19 participants. Such structural airway changes continue to remain significantly greater during early convalescence. No evidence shows that disease severity or inflammatory markers are associated with airway dimensions, implying that the primary lesion attacked by COVID-19 might not be the airways.

Temporal changes of quantitative CT findings from 102 patients with COVID-19 in Wuhan, China: A longitudinal study.

BACKGROUND: Computed tomography (CT) imaging combined with artificial intelligence is important in the diagnosis and prognosis of lung diseases. OBJECTIVE: This study aimed to investigate temporal changes of quantitative CT findings in patients with COVID-19 in three clinic types, including moderate, severe, and non-survivors, and to predict severe cases in the early stage from the results. METHODS: One hundred and two patients with confirmed COVID-19 were included in this study. Based on the time interval between onset of symptoms and the CT scan, four stages were defined in this study: Stage-1 (0 ∼7 days); Stage-2 (8 ∼ 14 days); Stage-3 (15 ∼ 21days); Stage-4 (> 21 days). Eight parameters, the infection volume and percentage of the whole lung in four different Hounsfield (HU) ranges, ((-, -750), [-750, -300), [-300, 50) and [50, +)), were calculated and compared between different groups. RESULTS: The infection volume and percentage of four HU ranges peaked in Stage-2. The highest proportion of HU [-750, 50) was found in the infected regions in non-survivors among three groups. CONCLUSIONS: The findings indicate rapid deterioration in the first week since the onset of symptoms in non-survivors. Higher proportion of HU [-750, 50) in the lesion area might be a potential bio-marker for poor prognosis in patients with COVID-19.

Visual and Quantitative Assessment of COVID-19 Pneumonia on Chest CT: The Relationship with Disease Severity and Clinical Findings.

BACKGROUND: Lungs are the primary organ involved in COVID-19, and the severity of pneumonia in COVID-19 patients is an important cause of morbidity and mortality. AIM: We aimed to evaluate the pneumonia severity through the visual and quantitative assessment on chest computed tomography (CT) in patients with coronavirus disease 2019 (COVID-19) and compare the CT findings with clinical and laboratory findings. METHODS: We retrospectively evaluated adult COVID-19 patients who underwent chest CT along with theirclinical scores, laboratory findings, and length of hospital stay. Two independent radiologists visually evaluated the pneumonia severity on chest CT (VSQS). Quantitative CT (QCT) assessment was performed using a free DICOM viewer, and the percentage of the well-aerated lung (%WAL), high-attenuation areas (%HAA) at different threshold values, and mean lung attenuation (MLA) values were calculated. The relationship between CT scores and the clinical, laboratory data, and the length of hospital stay were evaluated in this cross-sectional study. The student's t-test and chi-square test were used to analyze the differences between the variables. The Pearson correlation test analyzed the correlation between the variables. The diagnostic performance of the variables was assessed using the receiver operating characteristic (ROC) analysis. RESULTS: The VSQS and QCT scores were significantly correlated with procalcitonin, d-dimer, ferritin, and C-reactive protein levels. Both VSQ and QCT scores were significantly correlated with the disease severity (p < 0.001). Among the QCT parameters, the %HAA-600 value showed the best correlation with the VSQS (r = 730, p < 0.001). VSQS and QCT scores had high sensitivity and specificity in distinguishing disease severity and predicting prolonged hospitalization. CONCLUSION: The VSQS and QCT scores can help manage the COVID-19 and predict the duration of the hospitalization.

Role of computed tomography in predicting critical disease in patients with covid-19 pneumonia: A retrospective study using a semiautomatic quantitative method.

BACKGROUND: So far, only a few studies evaluated the correlation between CT features and clinical outcome in patients with COVID-19 pneumonia. PURPOSE: To evaluate CT ability in differentiating critically ill patients requiring invasive ventilation from patients with less severe disease. METHODS: We retrospectively collected data from patients admitted to our institution for COVID-19 pneumonia between March 5th-24th. Patients were considered critically ill or non-critically ill, depending on the need for mechanical ventilation. CT images from both groups were analyzed for the assessment of qualitative features and disease extension, using a quantitative semiautomatic method. We evaluated the differences between the two groups for clinical, laboratory and CT data. Analyses were conducted on a per-protocol basis. RESULTS: 189 patients were analyzed. PaO2/FIO2 ratio and oxygen saturation (SaO2) were decreased in critically ill patients. At CT, mixed pattern (ground glass opacities (GGO) and consolidation) and GGO alone were more frequent respectively in critically ill and in non-critically ill patients (p < 0.05). Lung volume involvement was significantly higher in critically ill patients (38.5 % vs. 5.8 %, p < 0.05). A cut-off of 23.0 % of lung involvement showed 96 % sensitivity and 96 % specificity in distinguishing critically ill patients from patients with less severe disease. The fraction of involved lung was related to lactate dehydrogenase (LDH) levels, PaO2/FIO2 ratio and SaO2 (p < 0.05). CONCLUSION: Lung disease extension, assessed using quantitative CT, has a significant relationship with clinical severity and may predict the need for invasive ventilation in patients with COVID-19.

Assessment of the Severity of Coronavirus Disease: Quantitative Computed Tomography Parameters versus Semiquantitative Visual Score.

OBJECTIVE: To compare the accuracies of quantitative computed tomography (CT) parameters and semiquantitative visual score in evaluating clinical classification of severity of coronavirus disease (COVID-19). MATERIALS AND METHODS: We retrospectively enrolled 187 patients with COVID-19 treated at Tongji Hospital of Tongji Medical College from February 15, 2020, to February 29, 2020. Demographic data, imaging characteristics, and clinical data were collected, and based on the clinical classification of severity, patients were divided into groups 1 (mild) and 2 (severe/critical). A semiquantitative visual score was used to estimate the lesion extent. A three-dimensional slicer was used to precisely quantify the volume and CT value of the lung and lesions. Correlation coefficients of the quantitative CT parameters, semiquantitative visual score, and clinical classification were calculated using Spearman's correlation. A receiver operating characteristic curve was used to compare the accuracies of quantitative and semi-quantitative methods. RESULTS: There were 59 patients in group 1 and 128 patients in group 2. The mean age and sex distribution of the two groups were not significantly different. The lesions were primarily located in the subpleural area. Compared to group 1, group 2 had larger values for all volume-dependent parameters (p < 0.001). The percentage of lesions had the strongest correlation with disease severity with a correlation coefficient of 0.495. In comparison, the correlation coefficient of semiquantitative score was 0.349. To classify the severity of COVID-19, area under the curve of the percentage of lesions was the highest (0.807; 95% confidence interval, 0.744-0.861: p < 0.001) and that of the quantitative CT parameters was significantly higher than that of the semiquantitative visual score (p = 0.001). CONCLUSION: The classification accuracy of quantitative CT parameters was significantly superior to that of semiquantitative visual score in terms of evaluating the severity of COVID-19.

Assessment of Small Pulmonary Blood Vessels in COVID-19 Patients Using HRCT.

RATIONALE AND OBJECTIVES: Mounting evidence supports the role of pulmonary hemodynamic alternations in the pathogenesis of COVID-19. Previous studies have demonstrated that changes in pulmonary blood volumes measured on computed tomography (CT) are associated with histopathological markers of pulmonary vascular pruning, suggesting that quantitative CT analysis may eventually be useful in the assessment pulmonary vascular dysfunction more broadly. MATERIALS AND METHODS: Building upon previous work, automated quantitative CT measures of small blood vessel volume and pulmonary vascular density were developed. Scans from 103 COVID-19 patients and 107 healthy volunteers were analyzed and their results compared, with comparisons made both on lobar and global levels. RESULTS: Compared to healthy volunteers, COVID-19 patients showed significant reduction in BV5 (pulmonary blood volume contained in blood vessels of <5 mm2) expressed as BV5/(total pulmonary blood volume; p < 0.0001), and significant increases in BV5-10 and BV 10 (pulmonary blood volumes contained in vessels between 5 and 10 mm2 and above 10 mm2, respectively, p < 0.0001). These changes were consistent across lobes. CONCLUSION: COVID-19 patients display striking anomalies in the distribution of blood volume within the pulmonary vascular tree, consistent with increased pulmonary vasculature resistance in the pulmonary vessels below the resolution of CT.

Quantitative analysis of chest CT imaging findings with the risk of ARDS in COVID-19 patients: a preliminary study.

BACKGROUND: The coronavirus disease 2019 (COVID-19) has rapidly become a pandemic worldwide. The value of chest computed tomography (CT) is debatable during the treatment of COVID-19 patients. Compared with traditional chest X-ray radiography, quantitative CT may supply more information, but its value on COVID-19 patients was still not proven. METHODS: An automatic quantitative analysis model based on a deep network called VB-Net for infection region segmentation was developed. A quantitative analysis was performed for patients diagnosed as severe COVID 19. The quantitative assessment included volume and density among the infectious area. The primary clinical outcome was the existence of acute respiratory distress syndrome (ARDS). A univariable and multivariable logistic analysis was done to explore the relationship between the quantitative results and ARDS existence. RESULTS: The VB-Ne model was sensitive and stable for pulmonary lesion segmentation, and quantitative analysis indicated that the total volume and average density of the lung lesions were not related to ARDS. However, lesions with specific density changes showed some influence on the risk of ARDS. The proportion of lesion density from -549 to -450 Hounsfield unit (HU) was associated with increased risk of ARDS, while the density was ranging from -149 to -50 HU was related to a lowered risk of ARDS. CONCLUSIONS: The automatic quantitative model based on VB-Ne can supply useful information for ARDS risk stratification in COVID-19 patients during treatment.