Chest lesion CT radiological features and quantitative analysis in RT-PCR turned negative and clinical symptoms resolved COVID-19 patients

Predicting malignant potential of solitary pulmonary nodules in patients with COVID-19 infection: a comprehensive analysis of CT imaging and tumor markers

OBJECTIVE

To analyze the value of combining computed tomography (CT) with serum tumor markers in the differential diagnosis of benign and malignant solitary pulmonary nodules (SPNs).

METHODS

The case data of 267 patients diagnosed with SPNs in the First Affiliated Hospital of Zhengzhou University from March 2020 to January 2023 were retrospectively analyzed. All individuals diagnosed with coronavirus disease 2019 (COVID-19) were confirmed via respiratory specimen viral nucleic acid testing. The included cases underwent CT, serum tumor marker testing and pathological examination. The diagnostic efficacy and clinical significance of CT, serum tumor marker testing and a combined test in identifying benign and malignant SPNs were analyzed using pathological histological findings as the gold standard. Finally, a nomogram mathematical model was established to predict the malignant probability of SPNs.

RESULTS

Of the 267 patients with SPNs, 91 patients were not afflicted with COVID-19, 36 exhibited malignant characteristics, whereas 55 demonstrated benign features. Conversely, within the cohort of 176 COVID-19 patients presenting with SPNs, 62 were identified as having malignant SPNs, and the remaining 114 were diagnosed with benign SPNs. CT scans revealed statistically significant differences between the benign and malignant SPNs groups in terms of CT values (P<0.001), maximum nodule diameter (P<0.001), vascular convergence sign (P<0.001), vacuole sign (P = 0.0007), air bronchogram sign (P = 0.0005), and lobulation sign (P = 0.0005). Malignant SPNs were associated with significantly higher levels of carcinoembryonic antigen (CEA) and neuron-specific enolase (NSE) compared to benign SPNs (P < 0.05), while no significant difference was found in carbohydrate antigen 125 (CA125) levels (P = 0.054 for non-COVID-19; P = 0.072 for COVID-19). The sensitivity (95.83%), specificity (95.32%), and accuracy (95.51%) of the comprehensive diagnosis combining serum tumor markers and CT were significantly higher than those of CT alone (70.45%, 79.89%, 76.78%) or serum tumor marker testing alone (56.52%, 73.71%, 67.79%) (P < 0.05). A visual nomogram predictive model for malignant pulmonary nodules was constructed.

CONCLUSION

Combining CT with testing for CEA, CA125, and NSE levels offers high diagnostic accuracy and sensitivity, enables precise differentiation between benign and malignant nodules, particularly in the context of COVID-19, thereby reducing the risk of unnecessary surgical interventions.

Admission chest CT findings and risk assessment for stroke-associated pneumonia

Introduction

Stroke-associated pneumonia (SAP) is a significant cause of morbidity and mortality after stroke. Various factors, including dysphagia and stroke severity, are closely related to SAP risk; however, the contribution of the baseline pulmonary parenchymal status to this interplay is an understudied field. Herein, we evaluated the prognostic performance of admission chest computed tomography (CT) findings in predicting SAP.

Methods

We evaluated admission chest CT images, acquired as part of a COVID-19-related institutional policy, in a consecutive series of acute ischemic stroke patients. The pulmonary opacity load at baseline was quantified using automated volumetry and visual scoring algorithms. The relationship between pulmonary opacities with risk of pneumonia within 7 days of symptom onset (i.e., SAP) was evaluated by bivariate and multivariate analyses.

Results

Twenty-three percent of patients in our cohort (n = 100) were diagnosed with SAP. Patients with SAP were more likely to have atrial fibrillation, COPD, severe neurological deficits, and dysphagia. The visual opacity score on chest CT was significantly higher among patients who developed SAP (p = 0.014), while no such relationship was observed in terms of absolute or relative opacity volume. In multivariate analyses, admission stroke severity, presence of dysphagia and a visual opacity score of ≥ 3 (OR 6.37, 95% CI 1.61–25.16; p = 0.008) remained significantly associated with SAP risk.

Conclusions

Pulmonary opacity burden, as evaluated on admission chest CT, is significantly associated with development of pneumonia within initial days of stroke. This association is independent of other well-known predisposing factors for SAP, including age, stroke severity, and presence of dysphagia.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13760-022-02043-7.

Performance of an Artificial Intelligence-based Application for the Detection of Plaque-based Stenosis on Monoenergetic Coronary CT Angiography: Validation by Invasive Coronary Angiography

RATIONALE AND OBJECTIVES

To explore the value of an artificial intelligence (AI)-based application for identifying plaque-specific stenosis and obstructive coronary artery disease from monoenergetic spectral reconstructions on coronary computed tomography angiography (CTA).

MATERIALS AND METHODS

This retrospective study enrolled 71 consecutive patients (52 men, 19 women; 63.3 ± 10.7 years) who underwent coronary CTA and invasive coronary angiography for diagnosing coronary artery disease. The conventional 120 kVp images and eight different virtual monoenergetic images (VMIs) (from 40 keV to 140 keV at increment of 10 keV) were reconstructed. An AI system automatically detected plaques from the conventional 120 kVp images and VMIs and calculated the degree of stenosis, which was further compared to invasive coronary angiography. The assessment was performed at a segment, vessel, and patient level.

RESULTS

Vessel and segment-based analyses showed comparable diagnostic performance between conventional CTA images and VMIs from 50 keV to 90 keV. For vessel-based analysis, the sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy of conventional CTA were 74.3% (95% CI: 64.9%-82.0%), 85.6% (95% CI: 77.0%-91.4%), 84.3% (95% CI: 75.2%-90.7%), 76.1% (95% CI: 67.1%-83.3%) and 79.8% (95% CI: 73.7%-84.9%), respectively; the average sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy values of the VMIs ranging from 50 keV to 90 keV were 71.6%, 90.7%, 87.5%, 64.1% and 81.6%, respectively. For plaque-based assessment, diagnostic performance of the average VMIs ranging from 50 keV to 100 keV showed no significant statistical difference in diagnostic accuracy compared to those of conventional CTA images in detecting calcified (91.4% vs. 93.8%, p > 0.05), noncalcified (92.6% vs. 85.2%, p > 0.05) or mixed (80.2% vs. 81.2%, p > 0.05) stenosis, although the specificity was slightly higher (53.4% vs. 40.0%, p > 0.05) in detecting stenosis caused by mixed plaques. For VMIs above 100 keV, the diagnostic accuracy dropped significantly.

CONCLUSION

Our study showed that the performance of an AI-based application employed to detect significant coronary stenosis in virtual monoenergetic reconstructions ranging from 50 keV to 90 keV was comparable to conventional 120 kVp reconstructions.

Emerging spectrum of post-COVID-19 syndrome

'Post-COVID-19 syndrome' refers to symptoms in the convalescent phase following initial COVID-19 infection. This term encompasses a wide array of presentation involving lungs, heart and the neuromuscular system. Pulmonary manifestations include post-COVID-19 fibrosis, which is akin to post acute respiratory distress syndrome fibrosis and may reflect the permanent damage to the lungs following an initial bout of infection. Cardiovascular system is often involved, and the presentation can be in terms of acute coronary syndrome, myocarditis and heart failure. Clinical manifestations are often varied and non-specific, which entails a detailed workup and a multidisciplinary approach. Post-COVID-19 syndrome adds to the overall disease morbidity and leads to a prolonged hospital stay, greater healthcare utilisation and loss of productivity marring the country's dwindling economy. Thus, it is imperative that post-COVID-19 syndrome be prevented and identified early followed by a prompt treatment.

AI-Based Quantitative CT Analysis of Temporal Changes According to Disease Severity in COVID-19 Pneumonia

Objective

To quantitatively evaluate computed tomography (CT) parameters of coronavirus disease 2019 (COVID-19) pneumonia an artificial intelligence (AI)-based software in different clinical severity groups during the disease course.

Methods

From March 11 to April 15, 2020, 51 patients (age, 18–84 years; 28 men) diagnosed and hospitalized with COVID-19 pneumonia with a total of 116 CT scans were enrolled in the study. Patients were divided into mild (n = 12), moderate (n = 31), and severe (n = 8) groups based on clinical severity. An AI-based quantitative CT analysis, including lung volume, opacity score, opacity volume, percentage of opacity, and mean lung density, was performed in initial and follow-up CTs obtained at different time points. Receiver operating characteristic analysis was performed to find the diagnostic ability of quantitative CT parameters for discriminating severe from nonsevere pneumonia.

Results

In baseline assessment, the severe group had significantly higher opacity score, opacity volume, higher percentage of opacity, and higher mean lung density than the moderate group (all P ≤ 0.001). Through consecutive time points, the severe group had a significant decrease in lung volume (P = 0.006), a significant increase in total opacity score (P = 0.003), and percentage of opacity (P = 0.007). A significant increase in total opacity score was also observed for the mild group (P = 0.011). Residual opacities were observed in all groups. The involvement of more than 4 lobes (sensitivity, 100%; specificity, 65.26%), total opacity score greater than 4 (sensitivity, 100%; specificity, 64.21), total opacity volume greater than 337.4 mL (sensitivity, 80.95%; specificity, 84.21%), percentage of opacity greater than 11% (sensitivity, 80.95%; specificity, 88.42%), total high opacity volume greater than 10.5 mL (sensitivity, 95.24%; specificity, 66.32%), percentage of high opacity greater than 0.8% (sensitivity, 85.71%; specificity, 80.00%) and mean lung density HU greater than −705 HU (sensitivity, 57.14%; specificity, 90.53%) were related to severe pneumonia.

Conclusions

An AI-based quantitative CT analysis is an objective tool in demonstrating disease severity and can also assist the clinician in follow-up by providing information about the disease course and prognosis according to different clinical severity groups.

A prospective cohort study on radiological and physiological outcomes of recovered COVID-19 patients 6 months after discharge

Background

This study investigated patients' long-term radiological and physiological outcomes with coronavirus disease 2019 (COVID-19).

Methods

A total of 52 patients (26 men and 26 women, 32 with moderate COVID-19 and 20 with severe COVID-19, with a median age of 50.5 years) who had COVID-19 participated in this study. Follow-up thin-section chest computed tomography (CT) scans were performed at 1, 3, and 6 months after discharge. Cardiopulmonary exercise testing was performed on 37 patients 6 months after discharge. The clinical data and the chest CT findings were recorded and analyzed.

Results

The predominant chest CT patterns of abnormalities observed at 6 months after discharge were parenchymal band, interlobular septal thickening, and traction bronchiectasis. The cumulative percentage of the complete radiological resolution was 17%, 42%, 67%, and 75% at discharge and 1, 3, and 6 months after discharge, respectively. A subgroup analysis revealed that 88% of patients with moderate type and 55% of patients with severe type COVID-19 achieved complete radiological resolution at 6 months after discharge, and the difference between the 2 groups was significant (P<0.001). The following risk factors were found to be associated with an incomplete radiological resolution at 6 months after discharge: an age >50 years old, the severe type of COVID-19, a hospital stay >18 days, mechanical ventilation, steroid therapy, immunoglobin therapy, an opacity score at discharge >4, and a volume of opacity at discharge >235 mL.

Conclusions

Chest CT lesions were absorbed without any sequelae in most patients with COVID-19; however, fibrotic-like changes and cardiopulmonary insufficiency were still present in a considerable proportion of COVID-19 survivors at 6 months after discharge, especially in patients with severe type COVID-19.

The relationship between lesion density change in chest computed tomography and clinical improvement in COVID-19 patients

OBJECTIVE

To evaluate the association of changes in chest computed tomography (CT) lesion densities with clinical improvement in COVID-19 patients.

METHODS

This was a cross-sectional analysis of hospitalised COVID-19 patients who underwent repeated chest CT. Patients who improved clinically but showed radiological progression were included. Demographic data, presentation complaints and laboratory results were retrieved from the electronic database of the hospital. Lesion density that was measured in Hounsfield units was compred between admission and discharge chest CT scans.

RESULTS

Forty patients (21 males, mean age 47.4 ± 15.1 years) were included in the analysis. The median white blood cell count and C-reactive protein significantly decreased, whereas the median lymphocyte count significantly increased at discharge compared with the admission values. The mean density significantly reduced from admission to discharge.

CONCLUSION

This is the first study in the literature reporting reduction in chest CT lesion densities correlated with clinical and laboratory improvement in COVID-19 patients.

Chest CT findings in patients with coronavirus disease 2019 (COVID-19): a comprehensive review

The objective of this review was to summarize the most pertinent CT imaging findings in patients with coronavirus disease 2019 (COVID-19). A literature search retrieved eligible studies in PubMed, EMBASE, Cochrane Library and Web of Science up to June 1, 2020. A comprehensive review of publications of the Chinese Medical Association about COVID-19 was also performed. A total of 84 articles with more than 5340 participants were included and reviewed. Chest CT comprised 92.61% of abnormal CT findings overall. Compared with real-time polymerase chain reaction result, CT findings has a sensitivity of 96.14% but a low specificity of 40.48% in diagnosing COVID-19. Ground glass opacity (GGO), pure (57.31%) or mixed with consolidation (41.51%) were the most common CT features with a majority of bilateral (80.32%) and peripheral (66.21%) lung involvement. The opacity might associate with other imaging features, including air bronchogram (41.07%), vascular enlargement (54.33%), bronchial wall thickening (19.12%), crazy-paving pattern (27.55%), interlobular septal thickening (42.48%), halo sign (25.48%), reverse halo sign (12.29%), bronchiectasis (32.44%), and pulmonary fibrosis (26.22%). Other accompanying signs including pleural effusion, lymphadenopathy and pericardial effusion were rare, but pleural thickening was common. The younger or early stage patients tended to have more GGOs, while extensive/multilobar involvement with consolidation was prevalent in the older or severe population. Children with COVID-19 showed significantly lower incidences of some ancillary findings than those of adults and showed a better performance on CT during follow up. Follow-up CT showed GGO lesions gradually decreased, and the consolidation lesions first increased and then remained relatively stable at 6-13 days, and then absorbed and fibrosis increased after 14 days. Chest CT imaging is an important component in the diagnosis, staging, disease progression and follow-up of patients with COVID-19.

Quantitative CT for detecting COVID‑19 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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-021-06556-z.

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.

Longitudinal trajectories of pneumonia lesions and lymphocyte counts associated with disease severity among convalescent COVID-19 patients: a group-based multi-trajectory analysis

Background

To explore the long-term trajectories considering pneumonia volumes and lymphocyte counts with individual data in COVID-19.

Methods

A cohort of 257 convalescent COVID-19 patients (131 male and 126 females) were included. Group-based multi-trajectory modelling was applied to identify different trajectories in terms of pneumonia lesion percentage and lymphocyte counts covering the time from onset to post-discharge follow-ups. We studied the basic characteristics and disease severity associated with the trajectories.

Results

We characterised four distinct trajectory subgroups. (1) Group 1 (13.9%), pneumonia increased until a peak lesion percentage of 1.9% (IQR 0.7–4.4) before absorption. The slightly decreased lymphocyte rapidly recovered to the top half of the normal range. (2) Group 2 (44.7%), the peak lesion percentage was 7.2% (IQR 3.2–12.7). The abnormal lymphocyte count restored to normal soon. (3) Group 3 (26.0%), the peak lesion percentage reached 14.2% (IQR 8.5–19.8). The lymphocytes continuously dropped to 0.75 × 10⁹/L after one day post-onset before slowly recovering. (4) Group 4 (15.4%), the peak lesion percentage reached 41.4% (IQR 34.8–47.9), much higher than other groups. Lymphopenia was aggravated until the lymphocytes declined to 0.80 × 10⁹/L on the fourth day and slowly recovered later. Patients in the higher order groups were older and more likely to have hypertension and diabetes (all P values < 0.05), and have more severe disease.

Conclusions

Our findings provide new insights to understand the heterogeneous natural courses of COVID-19 patients and the associations of distinct trajectories with disease severity, which is essential to improve the early risk assessment, patient monitoring, and follow-up schedule.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12890-021-01592-6.

Thin-section computed tomography findings and longitudinal variations of the residual pulmonary sequelae after discharge in patients with COVID-19: a short-term follow-up study

OBJECTIVES

This study analyzed and compared CT findings and longitudinal variations after discharge between severe and non-severe coronavirus disease (COVID-19) patients who had residual pulmonary sequelae at pre-discharge.

METHODS

A total of 310 patients were included and stratified into severe and non-severe COVID-19 groups. Cross-sectional CT features across different time periods (T0: pre-discharge, T1: 1-4 weeks after discharge, T2: 5-8 weeks after discharge, T3: 9-12 weeks after discharge, T4: > 12 weeks after discharge) were compared, and the longitudinal variations of CT findings were analyzed and compared in both groups.

RESULTS

The cumulative absorption rate of fibrosis-like findings in the severe and non-severe groups at T4 was 24.3% (17/70) and 52.0% (53/102), respectively. In both groups, ground-glass opacity (GGO) with consolidation showed a clear decreasing trend at T1, after which they maintained similar lower levels. The GGO in the severe group showed an increasing trend first at T1 and then decreasing at T4; however, the incidence decreased gradually in the non-severe group. Most fibrosis-like findings showed a tendency to decrease rapidly and then remained stable. Bronchial dilatation in the severe group persisted at an intermediate level.

CONCLUSIONS

After discharge, the characteristics and changing trends of pulmonary sequelae caused by COVID-19 were significantly different between the two groups. Pulmonary sequelae were more serious and recovery was slower in patients with severe/critical disease than in patients with moderate disease. A portion of the fibrosis-like findings were completely absorbed in patients with moderate and severe/critical diseases.

KEY POINTS

• Lung sequelae were more serious and recovery was slower in severe/critical COVID-19 patients. • Complete absorption of fibrosis-like findings after a short-term follow-up was observed in at least 17/70 (24.3%) of COVID-19 patients with severe/critical disease and 53/102 (52.0%) of COVID-19 patients with moderate disease. • The most common fibrosis-like findings was a parenchymal band; irregular interface was a nonspecific sign of COVID-19, and the percentage of bronchial dilatation in patients with severe/critical disease remained at a relatively stable medium level (range, 31.6 to 47.8%) at all stages.

Progression to fibrosing diffuse alveolar damage in a series of 30 minimally invasive autopsies with COVID-19 pneumonia in Wuhan, China

AIMS

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), infection has been deemed as a global pandemic by the World Health Organisation. While diffuse alveolar damage (DAD) is recognised to be the primary manifestation of COVID-19 pneumonia, there has been little emphasis on the progression to the fibrosing phase of DAD. This topic is of great interest, due to growing concerns regarding the potential long-term complications in prolonged survivors.

METHODS AND RESULTS

Here we report a detailed histopathological study of 30 autopsy cases with COVID-19 virus infection, based on minimally invasive autopsies performed between February and March, 2020. The mean age was 69 years, with 20 (67%) males and 10 (33%) females and frequent (70.0%) underlying comorbidities. The duration of illness ranged from 16 to 82 (median = 42) days. Histologically, the most common manifestation was diffuse alveolar damage (DAD) in 28 (93.3%) cases which showed predominantly acute (32%), organising (25%) and/or fibrosing (43%) patterns. Patients with fibrosing DAD were one decade younger (P = 0.034) and they had a longer duration of illness (P = 0.033), hospitalisation (P = 0.037) and mechanical ventilation (P = 0.014) compared to those with acute DAD. Patients with organising DAD had a longer duration of illness (P = 0.032) and hospitalisation (P = 0.023) compared to those with acute DAD.

CONCLUSIONS

COVID-19 pneumonia patients who develop DAD can progress to the fibrosing pattern. While we observed fibrosing DAD in fatal cases, whether or not surviving patients are at risk for developing pulmonary fibrosis and the frequency of this complication will require further clinical and radiological follow-up studies.

Role of Machine Learning Techniques to Tackle the COVID-19 Crisis: Systematic Review

BACKGROUND

SARS-CoV-2, the novel coronavirus responsible for COVID-19, has caused havoc worldwide, with patients presenting a spectrum of complications that have pushed health care experts to explore new technological solutions and treatment plans. Artificial Intelligence (AI)-based technologies have played a substantial role in solving complex problems, and several organizations have been swift to adopt and customize these technologies in response to the challenges posed by the COVID-19 pandemic.

OBJECTIVE

The objective of this study was to conduct a systematic review of the literature on the role of AI as a comprehensive and decisive technology to fight the COVID-19 crisis in the fields of epidemiology, diagnosis, and disease progression.

METHODS

A systematic search of PubMed, Web of Science, and CINAHL databases was performed according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines to identify all potentially relevant studies published and made available online between December 1, 2019, and June 27, 2020. The search syntax was built using keywords specific to COVID-19 and AI.

RESULTS

The search strategy resulted in 419 articles published and made available online during the aforementioned period. Of these, 130 publications were selected for further analyses. These publications were classified into 3 themes based on AI applications employed to combat the COVID-19 crisis: Computational Epidemiology, Early Detection and Diagnosis, and Disease Progression. Of the 130 studies, 71 (54.6%) focused on predicting the COVID-19 outbreak, the impact of containment policies, and potential drug discoveries, which were classified under the Computational Epidemiology theme. Next, 40 of 130 (30.8%) studies that applied AI techniques to detect COVID-19 by using patients' radiological images or laboratory test results were classified under the Early Detection and Diagnosis theme. Finally, 19 of the 130 studies (14.6%) that focused on predicting disease progression, outcomes (ie, recovery and mortality), length of hospital stay, and number of days spent in the intensive care unit for patients with COVID-19 were classified under the Disease Progression theme.

CONCLUSIONS

In this systematic review, we assembled studies in the current COVID-19 literature that utilized AI-based methods to provide insights into different COVID-19 themes. Our findings highlight important variables, data types, and available COVID-19 resources that can assist in facilitating clinical and translational research.

Optimization of imaging parameters in chest CT for COVID-19 patients: an experimental phantom study

Background

With the global outbreak of coronavirus disease 2019 (COVID-19), chest computed tomography (CT) is vital for diagnosis and follow-up. The increasing contribution of CT to the population-collected dose has become a topic of interest. Radiation dose optimization for chest CT of COVID-19 patients is of importance in clinical practice. The present study aimed to investigate the factors affecting the detection of ground-glass nodules and exudative lesions in chest CT among COVID-19 patients and to find an appropriate combination of imaging parameters that optimize detection while effectively reducing the radiation dose.

Methods

The anthropomorphic thorax phantom, with 9 spherical nodules of different diameters and CT values of -800, -630, and 100 HU, was used to simulate the lesions of COVID-19 patients. Four custom-simulated lesions of porcine fat and ethanol were also scanned at 3 tube potentials (120, 100, and 80 kV) and corresponding milliampere-seconds (mAs) (ranging from 10 to 100). Separate scans were performed at pitches of 0.6, 0.8, 1.0, 1.15, and 1.49, and at collimations of 10, 20, 40, and 80 mm at 80 kV and 100 mAs. CT values and standard deviations of simulated nodules and lesions were measured, and radiation dose quantity (volume CT dose index; CTDIvol) was collected. Contrast-to-noise ratio (CNR) and figure of merit (FOM) were calculated. All images were subjectively evaluated by 2 radiologists to determine whether the nodules were detectable and if the overall image quality met diagnostic requirements.

Results

All simulated lesions, except -800 HU nodules, were detected at all scanning conditions. At a fixed voltage of 120 or 100 kV, with increasing mAs, image noise tended to decrease, and the CNR tended to increase (F=9.694 and P=0.033 for 120 kV; F=9.028 and P=0.034 for 100 kV). The FOM trend was the same as that of CNR (F=2.768 and P=0.174 for 120 kV; F=1.915 and P=0.255 for 100 kV). At 80 kV, the CNRs and FOMs had no significant change with increasing mAs (F=4.522 and P=0.114 for CNRs; F=1.212 and P=0.351 for FOMs). For the 4 nodules of -800 and -630 HU, CNRs had no statistical differences at each of the 5 pitches (F=0.673, P=0.476). The CNRs and FOMs at each of the 4 collimations had no statistical differences (F=2.509 and P=0.125 for CNRs; F=1.485 and P=0.309 for FOMs) for each nodule. CNRs and subjective evaluation scores increased with increasing parameter values for each imaging iteration. The CNRs of 4 -800 HU nodules in the qualified images at the thresholds of scanning parameters of 120 kV/20 mAs, 100 kV/40 mAs, and 80 kV/80 mAs, had statistical differences (P=0.038), but the FOMs had no statistical differences (P=0.085). Under the 3 threshold conditions, the CNRs and FOMs of the 4 nodules were highest at 100 kV and 40 mAs (1.6 mGy CTDIvol).

Conclusions

For chest CT among COVID-19 patients, it is recommended that 100 kV/40 mAs is used for average patients; the radiation dose can be reduced to 1.6 mGy with qualified images to detect ground-glass nodules and exudation lesions.