Computed tomography semi-automated lung volume quantification in SARS-CoV-2-related pneumonia

Impact of rapid iodine contrast agent infusion on tracheal diameter and lung volume in CT pulmonary angiography measured with deep learning-based algorithm

PURPOSE

To compare computed tomography (CT) pulmonary angiography and unenhanced CT to determine the effect of rapid iodine contrast agent infusion on tracheal diameter and lung volume.

MATERIAL AND METHODS

This retrospective study included 101 patients who underwent CT pulmonary angiography and unenhanced CT, for which the time interval between them was within 365 days. CT pulmonary angiography was scanned 20 s after starting the contrast agent injection at the end-inspiratory level. Commercial software, which was developed based on deep learning technique, was used to segment the lung, and its volume was automatically evaluated. The tracheal diameter at the thoracic inlet level was also measured. Then, the ratios for the CT pulmonary angiography to unenhanced CT of the tracheal diameter (TDPAU) and both lung volumes (BLVPAU) were calculated.

RESULTS

Tracheal diameter and both lung volumes were significantly smaller in CT pulmonary angiography (17.2 ± 2.6 mm and 3668 ± 1068 ml, respectively) than those in unenhanced CT (17.7 ± 2.5 mm and 3887 ± 1086 ml, respectively) (p < 0.001 for both). A statistically significant correlation was found between TDPAU and BLVPAU with a correlation coefficient of 0.451 (95% confidence interval, 0.280-0.594) (p < 0.001). No factor showed a significant association with TDPAU. The type of contrast agent had a significant association for BLVPAU (p = 0.042).

CONCLUSIONS

Rapid infusion of iodine contrast agent reduced the tracheal diameter and both lung volumes in CT pulmonary angiography, which was scanned at end-inspiratory level, compared with those in unenhanced CT.

Lung volume measurement using chest CT in COVID-19 patients: a cohort study in Japan

OBJECTIVE

This study aimed to investigate the utility of CT quantification of lung volume for predicting critical outcomes in COVID-19 patients.

METHODS

This retrospective cohort study included 1200 hospitalised patients with COVID-19 from 4 hospitals. Lung fields were extracted using artificial intelligence-based segmentation, and the percentage of the predicted (%pred) total lung volume (TLC (%pred)) was calculated. The incidence of critical outcomes and posthospitalisation complications was compared between patients with low and high CT lung volumes classified based on the median percentage of predicted TLCct (n=600 for each). Prognostic factors for residual lung volume loss were investigated in 208 patients with COVID-19 via a follow-up CT after 3 months.

RESULTS

The incidence of critical outcomes was higher in the low TLCct (%pred) group than in the high TLCct (%pred) group (14.2% vs 3.3%, p<0.0001). Multivariable analysis of previously reported factors (age, sex, body mass index and comorbidities) demonstrated that CT-derived lung volume was significantly associated with critical outcomes. The low TLCct (%pred) group exhibited a higher incidence of bacterial infection, heart failure, thromboembolism, liver dysfunction and renal dysfunction than the high TLCct (%pred) group. TLCct (%pred) at 3 months was similarly divided into two groups at the median (71.8%). Among patients with follow-up CT scans, lung volumes showed a recovery trend from the time of admission to 3 months but remained lower in critical cases at 3 months.

CONCLUSION

Lower CT lung volume was associated with critical outcomes, posthospitalisation complications and slower improvement of clinical conditions in COVID-19 patients.

Effects of Intravenous Infusion of Iodine Contrast Media on the Tracheal Diameter and Lung Volume Measured with Deep Learning-Based Algorithm

This study aimed to investigate the effects of intravenous injection of iodine contrast agent on the tracheal diameter and lung volume. In this retrospective study, a total of 221 patients (71.1 ± 12.4 years, 174 males) who underwent vascular dynamic CT examination including chest were included. Unenhanced, arterial phase, and delayed-phase images were scanned. The tracheal luminal diameters at the level of the thoracic inlet and both lung volumes were evaluated by a radiologist using a commercial software, which allows automatic airway and lung segmentation. The tracheal diameter and both lung volumes were compared between the unenhanced vs. arterial and delayed phase using a paired t-test. The Bonferroni correction was performed for multiple group comparisons. The tracheal diameter in the arterial phase (18.6 ± 2.4 mm) was statistically significantly smaller than those in the unenhanced CT (19.1 ± 2.5 mm) (p < 0.001). No statistically significant difference was found in the tracheal diameter between the delayed phase (19.0 ± 2.4 mm) and unenhanced CT (p = 0.077). Both lung volumes in the arterial phase were 4131 ± 1051 mL which was significantly smaller than those in the unenhanced CT (4332 ± 1076 mL) (p < 0.001). No statistically significant difference was found in both lung volumes between the delayed phase (4284 ± 1054 mL) and unenhanced CT (p = 0.068). In conclusion, intravenous infusion of iodine contrast agent transiently decreased the tracheal diameter and both lung volumes.

COVID-19 therapy optimization by AI-driven biomechanical simulations

The COVID-19 disease causes pneumonia in many patients that in the most serious cases evolves into the Acute Distress Respiratory Syndrome (ARDS), requiring assisted ventilation and intensive care. In this context, identification of patients at high risk of developing ARDS is a key point for early clinical management, better clinical outcome and optimization in using the limited resources available in the intensive care units. We propose an AI-based prognostic system that makes predictions of oxygen exchange with arterial blood by using as input lung Computed Tomography (CT), the air flux in lungs obtained from biomechanical simulations and Arterial Blood Gas (ABG) analysis. We developed and investigated the feasibility of this system on a small clinical database of proven COVID-19 cases where the initial CT and various ABG reports were available for each patient. We studied the time evolution of the ABG parameters and found correlation with the morphological information extracted from CT scans and disease outcome. Promising results of a preliminary version of the prognostic algorithm are presented. The ability to predict the evolution of patients' respiratory efficiency would be of crucial importance for disease management.

Feasibility of a New Lung Ultrasound Protocol to Determine the Extent of Lung Injury in COVID-19 Pneumonia

BACKGROUND

Lung ultrasound (LUS) scanning is useful to diagnose and assess the severity of pulmonary lesions during COVID-19-related ARDS (CoARDS). A conventional LUS score is proposed to measure the loss of aeration during CoARDS. However, this score was validated during the pre-COVID-19 era in patients with ARDS in the ICU and does not consider the differences with CoARDS. An alternative LUS method is based on grading the percentage of extension of the typical signs of COVID-19 pneumonia on the lung surface (LUSext).

RESEARCH QUESTION

Is LUSext feasible in patients with COVID-19 at the onset of disease, and does it correlate with the volumetric measure of severity of COVID-19 pneumonia lesions at CT scan (CTvol)?

STUDY DESIGN AND METHODS

This observational study enrolled a convenience sampling of patients in the ED with confirmed COVID-19 whose condition demonstrated pneumonia at bedside LUS and CT scan. LUSext was visually quantified. All CT scan studies were analyzed retrospectively by a specifically designed software to calculate the CTvol. The correlation between LUSext and CTvol, and the correlations of each score with Pao2/Fio2 ratio were calculated.

RESULTS

We analyzed data from 179 patients. Feasibility of LUSext was 100%. Time to perform LUS scan was 5 ± 1.5 mins. LUSext and CTvol were correlated positively (R = 0.67; P < .0001). Both LUSext and CTvol showed negative correlation with Pao2/Fio2 ratio (R = -0.66 and R = -0.54; P < .0001, respectively).

INTERPRETATION

LUSext is a valid measure of the severity of the lesions when compared with the CT scan. Not only are LUSext and CTvol correlated, but they also have similar inverse correlation with the severity of respiratory failure. LUSext is a practical and simple bedside measure of the severity of pneumonia in CoARDS, whose clinical and prognostic impact need to be investigated further.

Well-Aerated Lung and Mean Lung Density Quantified by CT at Discharge to Predict Pulmonary Diffusion Function 5 Months after COVID-19

Background: The aim of this study was to explore the predictive values of quantitative CT indices of the total lung and lung lobe tissue at discharge for the pulmonary diffusion function of coronavirus disease 2019 (COVID-19) patients at 5 months after symptom onset. Methods: A total of 90 patients with moderate and severe COVID-19 underwent CT scans at discharge, and pulmonary function tests (PFTs) were performed 5 months after symptom onset. The differences in quantitative CT and PFT results between Group 1 (patients with abnormal diffusion function) and Group 2 (patients with normal diffusion function) were compared by the chi-square test, Fisher’s exact test or Mann−Whitney U test. Univariate analysis, stepwise linear regression and logistic regression were used to determine the predictors of diffusion function in convalescent patients. Results: A total of 37.80% (34/90) of patients presented diffusion dysfunction at 5 months after symptom onset. The mean lung density (MLD) of the total lung tissue in Group 1 was higher than that in Group 2, and the percentage of the well-aerated lung (WAL) tissue volume (WAL%) of Group 1 was lower than that of Group 2 (all p < 0.05). Multiple stepwise linear regression identified only WAL and WAL% of the left upper lobe (LUL) as parameters that positively correlated with the percent of the predicted value of diffusion capacity of the lungs for carbon monoxide (WAL: p = 0.002; WAL%: p = 0.004), and multiple stepwise logistic regression identified MLD and MLDLUL as independent predictors of diffusion dysfunction (MLD: OR (95%CI): 1.011 (1.001, 1.02), p = 0.035; MLDLUL: OR (95%CI): 1.016 (1.004, 1.027), p = 0.008). Conclusion: At five months after symptom onset, more than one-third of moderate and severe COVID-19 patients presented with diffusion dysfunction. The well-aerated lung and mean lung density quantified by CT at discharge could be predictors of diffusion function in convalesce.

Artificial Intelligence in Lung Cancer Imaging: Unfolding the Future

Lung cancer is one of the malignancies with higher morbidity and mortality. Imaging plays an essential role in each phase of lung cancer management, from detection to assessment of response to treatment. The development of imaging-based artificial intelligence (AI) models has the potential to play a key role in early detection and customized treatment planning. Computer-aided detection of lung nodules in screening programs has revolutionized the early detection of the disease. Moreover, the possibility to use AI approaches to identify patients at risk of developing lung cancer during their life can help a more targeted screening program. The combination of imaging features and clinical and laboratory data through AI models is giving promising results in the prediction of patients’ outcomes, response to specific therapies, and risk for toxic reaction development. In this review, we provide an overview of the main imaging AI-based tools in lung cancer imaging, including automated lesion detection, characterization, segmentation, prediction of outcome, and treatment response to provide radiologists and clinicians with the foundation for these applications in a clinical scenario.

Rapid artificial intelligence solutions in a pandemic-The COVID-19-20 Lung CT Lesion Segmentation Challenge

Artificial intelligence (AI) methods for the automatic detection and quantification of COVID-19 lesions in chest computed tomography (CT) might play an important role in the monitoring and management of the disease. We organized an international challenge and competition for the development and comparison of AI algorithms for this task, which we supported with public data and state-of-the-art benchmark methods. Board Certified Radiologists annotated 295 public images from two sources (A and B) for algorithms training (n=199, source A), validation (n=50, source A) and testing (n=23, source A; n=23, source B). There were 1,096 registered teams of which 225 and 98 completed the validation and testing phases, respectively. The challenge showed that AI models could be rapidly designed by diverse teams with the potential to measure disease or facilitate timely and patient-specific interventions. This paper provides an overview and the major outcomes of the COVID-19 Lung CT Lesion Segmentation Challenge — 2020.

Different Lung Parenchyma Quantification Using Dissimilar Segmentation Software: A Multi-Center Study for COVID-19 Patients

Background: Chest Computed Tomography (CT) imaging has played a central role in the diagnosis of interstitial pneumonia in patients affected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and can be used to obtain the extent of lung involvement in COVID-19 pneumonia patients either qualitatively, via visual inspection, or quantitatively, via AI-based software. This study aims to compare the qualitative/quantitative pathological lung extension data on COVID-19 patients. Secondly, the quantitative data obtained were compared to verify their concordance since they were derived from three different lung segmentation software. Methods: This double-center study includes a total of 120 COVID-19 patients (60 from each center) with positive reverse-transcription polymerase chain reaction (RT-PCR) who underwent a chest CT scan from November 2020 to February 2021. CT scans were analyzed retrospectively and independently in each center. Specifically, CT images were examined manually by two different and experienced radiologists for each center, providing the qualitative extent score of lung involvement, whereas the quantitative analysis was performed by one trained radiographer for each center using three different software: 3DSlicer, CT Lung Density Analysis, and CT Pulmo 3D. Results: The agreement between radiologists for visual estimation of pneumonia at CT can be defined as good (ICC 0.79, 95% CI 0.73–0.84). The statistical tests show that 3DSlicer overestimates the measures assessed; however, ICC index returns a value of 0.92 (CI 0.90–0.94), indicating excellent reliability within the three software employed. ICC was also performed between each single software and the median of the visual score provided by the radiologists. This statistical analysis underlines that the best agreement is between 3D Slicer “LungCTAnalyzer” and the median of the visual score (0.75 with a CI 0.67–82 and with a median value of 22% of disease extension for the software and 25% for the visual values). Conclusions: This study provides for the first time a direct comparison between the actual gold standard, which is represented by the qualitative information described by radiologists, and novel quantitative AI-based techniques, here represented by three different commonly used lung segmentation software, underlying the importance of these specific values that in the future could be implemented as consistent prognostic and clinical course parameters.

Multiorgan Involvement in SARS-CoV-2 Infection: The Role of the Radiologist from Head to Toe

Radiology plays a crucial role for the diagnosis and management of COVID-19 patients during the different stages of the disease, allowing for early detection of manifestations and complications of COVID-19 in the different organs. Lungs are the most common organs involved by SARS-CoV-2 and chest computed tomography (CT) represents a reliable imaging-based tool in acute, subacute, and chronic settings for diagnosis, prognosis, and management of lung disease and the evaluation of acute and chronic complications. Cardiac involvement can be evaluated by using cardiac computed tomography angiography (CCTA), considered as the best choice to solve the differential diagnosis between the most common cardiac conditions: acute coronary syndrome, myocarditis, and cardiac dysrhythmia. By using compressive ultrasound it's possible to study the peripheral arteries and veins and to exclude the deep vein thrombosis, directly linked to the onset of pulmonary embolism. Moreover, CT and especially MRI can help to evaluate the gastrointestinal involvement and assess hepatic function, pancreas involvement, and exclude causes of lymphocytopenia, thrombocytopenia, and leukopenia, typical of COVID-19 patients. Finally, radiology plays a crucial role in the early identification of renal damage in COVID-19 patients, by using both CT and US. This narrative review aims to provide a comprehensive radiological analysis of commonly involved organs in patients with COVID-19 disease.

Newly developed artificial intelligence algorithm for COVID-19 pneumonia: utility of quantitative CT texture analysis for prediction of favipiravir treatment effect

Purpose

Using CT findings from a prospective, randomized, open-label multicenter trial of favipiravir treatment of COVID-19 patients, the purpose of this study was to compare the utility of machine learning (ML)-based algorithm with that of CT-determined disease severity score and time from disease onset to CT (i.e., time until CT) in this setting.

Materials and methods

From March to May 2020, 32 COVID-19 patients underwent initial chest CT before enrollment were evaluated in this study. Eighteen patients were randomized to start favipiravir on day 1 (early treatment group), and 14 patients on day 6 of study participation (late treatment group). In this study, percentages of ground-glass opacity (GGO), reticulation, consolidation, emphysema, honeycomb, and nodular lesion volumes were calculated as quantitative indexes by means of the software, while CT-determined disease severity was also visually scored. Next, univariate and stepwise regression analyses were performed to determine relationships between quantitative indexes and time until CT. Moreover, patient outcomes determined as viral clearance in the first 6 days and duration of fever were compared for those who started therapy within 4, 5, or 6 days as time until CT and those who started later by means of the Kaplan–Meier method followed by Wilcoxon’s signed-rank test.

Results

% GGO and % consolidation showed significant correlations with time until CT (p < 0.05), and stepwise regression analyses identified both indexes as significant descriptors for time until CT (p < 0.05). When divided all patients between time until CT of 4 days and that of more than 4 days, accuracy of the combined quantitative method (87.5%) was significantly higher than that of the CT disease severity score (62.5%, p = 0.008).

Conclusion

ML-based CT texture analysis is equally or more useful for predicting time until CT for favipiravir treatment on COVID-19 patients than CT disease severity score.

Comparison of semiquantitative chest CT scoring systems to estimate severity in coronavirus disease 2019 (COVID-19) pneumonia

OBJECTIVES

To compare the clinical usefulness among three different semiquantitative computed tomography (CT) severity scoring systems for coronavirus disease 2019 (COVID-19) pneumonia.

METHODS

Two radiologists independently reviewed chest CT images in 108 patients to rate three CT scoring systems (total CT score [TSS], chest CT score [CCTS], and CT severity score [CTSS]). We made a minor modification to CTSS. Quantitative dense area ratio (QDAR: the ratio of lung involvement to lung parenchyma) was calculated using the U-net model. Clinical severity at admission was classified as severe (n = 14) or mild (n = 94). Interobserver agreement, interpretation time, and degree of correlation with clinical severity as well as QDAR were evaluated.

RESULTS

Interobserver agreement was excellent (intraclass correlation coefficient: 0.952-0.970, p < 0.001). Mean interpretation time was significantly longer in CTSS (48.9-80.0 s) than in TSS (25.7-41.7 s, p < 0.001) and CCTS (27.7-39.5 s, p < 0.001). Area under the curve for differentiating clinical severity at admission was 0.855-0.842 in TSS, 0.853-0.850 in CCTS, and 0.853-0.836 in CTSS. All scoring systems correlated with QDAR in the order of CCTS (ρ = 0.443-0.448), TSS (ρ = 0.435-0.437), and CTSS (ρ = 0.415-0.426).

CONCLUSIONS

All semiquantitative scoring systems demonstrated substantial diagnostic performance for clinical severity at admission with excellent interobserver agreement. Interpretation time was significantly shorter in TSS and CCTS than in CTSS. The correlation between the scoring system and QDAR was highest in CCTS, followed by TSS and CTSS. CCTS appeared to be the most appropriate CT scoring system for clinical practice.

KEY POINTS

• Three semiquantitative scoring systems demonstrate substantial accuracy (area under the curve: 0.836-0.855) for diagnosing clinical severity at admission and (area under the curve: 0.786-0.802) for risk of developing critical illness. • Total CT score (TSS) and chest CT score (CCTS) were considered to be more appropriate in terms of clinical usefulness as compared with CT severity score (CTSS), given the shorter interpretation time in TSS and CCTS, and the lowest correlation with quantitative dense area ratio in CTSS. • CCTS is assumed to distinguish subtle from mild lung involvement better than TSS by adopting a 5% threshold in scoring the degree of severity.

Total Lung and Lobar Quantitative Assessment Based on Paired Inspiratory-Expiratory Chest CT in Healthy Adults: Correlation with Pulmonary Ventilatory Function

Objective: To provide the quantitative volumetric data of the total lung and lobes in inspiration and expiration from healthy adults, and to explore the value of paired inspiratory–expiratory chest CT scan in pulmonary ventilatory function and further explore the influence of each lobe on ventilation. Methods: A total of 65 adults (29 males and 36 females) with normal clinical pulmonary function test (PFT) and paired inspiratory–expiratory chest CT scan were retrospectively enrolled. The inspiratory and expiratory volumetric indexes of the total lung (TL) and 5 lobes (left upper lobe [LUL], left lower lobe [LLL], right upper lobe [RUL], right middle lobe [RML], and right lower lobe [RLL]) were obtained by Philips IntelliSpace Portal image postprocessing workstation, including inspiratory lung volume (LV_in), expiratory lung volume (LV_ex), volume change (∆LV), and well-aerated lung volume (WAL, lung tissue with CT threshold between −950 and −750 HU in inspiratory scan). Spearman correlation analysis was used to explore the correlation between CT quantitative indexes of the total lung and ventilatory function indexes (including total lung capacity [TLC], residual volume [RV], and force vital capacity [FVC]). Multiple stepwise regression analysis was used to explore the influence of each lobe on ventilation. Results: At end-inspiratory phase, the LV_in-TL was 4664.6 (4282.7, 5916.2) mL, the WAL_TL was 4173 (3639.6, 5250.9) mL; both showed excellent correlation with TLC (LV_in-TL: r = 0.890, p < 0.001; WAL_TL: r = 0.879, p < 0.001). From multiple linear regression analysis with lobar CT indexes as variables, the LV_in and WAL of these two lobes, LLL and RUL, showed a significant relationship with TLC. At end-expiratory phase, the LV_ex-TL was 2325.2 (1969.7, 2722.5) mL with good correlation with RV (r = 0.811, p < 0.001), of which the LV_ex of RUL and RML had a significant relationship with RV. For the volumetric change within breathing, the ∆LV_TL was 2485.6 (2169.8, 3078.1) mL with good correlation with FVC (r = 0.719, p < 0.001), moreover, WAL_TL showed a better correlation with FVC (r = 0.817, p < 0.001) than that of ∆LV_TL. Likewise, there was also a strong association between ∆LV, WAL of these two lobes (LLL and RUL), and FVC. Conclusions: The quantitative indexes derived from paired inspiratory–expiratory chest CT could reflect the clinical pulmonary ventilatory function, LLL, and RUL give greater impact on ventilation. Thus, the pulmonary functional evaluation needs to be more precise and not limited to the total lung level.

The Applications of Artificial Intelligence in Chest Imaging of COVID-19 Patients: A Literature Review

Diagnostic imaging is regarded as fundamental in the clinical work-up of patients with a suspected or confirmed COVID-19 infection. Recent progress has been made in diagnostic imaging with the integration of artificial intelligence (AI) and machine learning (ML) algorisms leading to an increase in the accuracy of exam interpretation and to the extraction of prognostic information useful in the decision-making process. Considering the ever expanding imaging data generated amid this pandemic, COVID-19 has catalyzed the rapid expansion in the application of AI to combat disease. In this context, many recent studies have explored the role of AI in each of the presumed applications for COVID-19 infection chest imaging, suggesting that implementing AI applications for chest imaging can be a great asset for fast and precise disease screening, identification and characterization. However, various biases should be overcome in the development of further ML-based algorithms to give them sufficient robustness and reproducibility for their integration into clinical practice. As a result, in this literature review, we will focus on the application of AI in chest imaging, in particular, deep learning, radiomics and advanced imaging as quantitative CT.

Rapid Artificial Intelligence Solutions in a Pandemic - The COVID-19-20 Lung CT Lesion Segmentation Challenge

Artificial intelligence (AI) methods for the automatic detection and quantification of COVID-19 lesions in chest computed tomography (CT) might play an important role in the monitoring and management of the disease. We organized an international challenge and competition for the development and comparison of AI algorithms for this task, which we supported with public data and state-of-the-art benchmark methods. Board Certified Radiologists annotated 295 public images from two sources (A and B) for algorithms training (n=199, source A), validation (n=50, source A) and testing (n=23, source A; n=23, source B). There were 1,096 registered teams of which 225 and 98 completed the validation and testing phases, respectively. The challenge showed that AI models could be rapidly designed by diverse teams with the potential to measure disease or facilitate timely and patient-specific interventions. This paper provides an overview and the major outcomes of the COVID-19 Lung CT Lesion Segmentation Challenge - 2020.

Pulmonary function and COVID-19

In people recovering from COVID-19, there is concern regarding potential long-term pulmonary sequelae and associated impairment of functional capacity. Data published thus far indicate that spirometric indices appear to be generally well preserved, but that a defect in diffusing capacity (DLco) is a prevalent abnormality identified on follow-up lung function; present in 20-30% of those with mild to moderate disease and 60% in those with severe disease. Reductions in total lung capacity were commonly reported. Functional capacity is also often impaired, with data now starting to emerge detailing walk test and cardiopulmonary exercise test outcome at follow-up. In this review, we evaluate the published evidence in this area, to summarise the impact of COVID-19 infection on pulmonary function and relate this to the clinico-radiological findings and disease severity.

Prognostic value of CT integrated with clinical and laboratory data during the first peak of the COVID-19 pandemic in Northern Italy: A nomogram to predict unfavorable outcome

Purpose

To evaluate the prognostic role of chest computed tomography (CT), alone or in combination with clinical and laboratory parameters, in COVID-19 patients during the first peak of the pandemic.

Methods

A retrospective single-center study of 301 COVID-19 patients referred to our Emergency Department (ED) from February 25 to March 29, 2020. At presentation, patients underwent chest CT and clinical and laboratory examinations. Outcomes included discharge from the ED after improvement/recovery (positive outcome), or admission to the intensive care unit or death (poor prognosis). A visual quantitative analysis was formed using two scores: the Pulmonary Involvement (PI) score based on the extension of lung involvement, and the Pulmonary Consolidation (PC) score based on lung consolidation. The prognostic value of CT alone or integrated with other parameters was studied by logistic regression and ROC analysis.

Results

The impact of the CT PI score [≥15 vs. ≤ 6] on predicting poor prognosis (OR 5.71 95 % CI 1.93−16.92, P = 0.002) was demonstrated; no significant association was found for the PC score. Chest CT had a prognostic role considering the PI score alone (AUC 0.722) and when evaluated with demographic characteristics, comorbidities, and laboratory data (AUC 0.841). We, therefore, developed a nomogram as an easy tool for immediate clinical application.

Conclusions

Visual analysis of CT gives useful information to physicians for prognostic evaluations, even in conditions of COVID-19 emergency. The predictive value is increased by evaluating CT in combination with clinical and laboratory data.