Chest Computed Tomography Lesion Research Articles

Analysis of T-lymphocyte subsets and risk factors in children with tuberculosis

BackgroundTuberculosis (TB) is not only related to infection but also involves immune factors. This study explores the changes in T-lymphocyte subsets in children with TB who are human immunodeficiency virus (HIV)-negative and examines their relationship using chest computed tomography (CT) scans. Additionally, the study identifies risk factors for severe TB (STB) in children and establishes relevant risk prediction models. MethodsWe recruited 235 participants between 2018 and 2022, comprising 176 paediatric patients with TB who were HIV-negative and 59 age-matched children with bacterial community-acquired pneumonia (CAP). We quantitatively analysed and compared T-lymphocyte subsets between the two groups and among different types of TB infection. Both univariate and multivariate analyses of clinical and laboratory characteristics were conducted to identify independent risk factors for STB in children and to establish a risk prediction model. ResultsThe absolute counts of CD3, CD4 and CD8 T-cells in children with TB infection decreased significantly compared with bacterial CAP. The percentage of CD8 T-cells increased, whereas the percentage of CD4 T-cells did not change significantly. The absolute count of CD3, CD4 and CD8 T-cells in extrapulmonary TB (EPTB) was significantly higher than in extra-respiratory TB, with unchanged subset percentages. According to chest CT lesion classification, CD4 T-cell counts decreased significantly in S3 compared with S1 or S2, with no significant change in CD3 and CD8 T-cell counts and percentages. No significant differences were observed in lymphocyte subset counts and percentages between S1 and S2. Univariate analyses indicated that factors such as age, symptom duration, white blood cell count, platelet count, neutrophil-to-lymphocyte ratio (NLR), erythrocyte sedimentation rate, prealbumin level, albumin level, globulin level, albumin/globulin (A/G) ratio, high-sensitivity C-reactive protein (Hs-CRP) level and CD4 and CD8 T-cell counts are associated with STB. Multivariate logistic regression analysis revealed that age, Hs-CRP level, NLR, symptom duration and A/G ratio are independent risk factors for STB in children. Increased age, Hs-CRP levels and NLR, along with decreased A/G, correlate with increased susceptibility to STB. A nomogram model, based on these independent risk factors, demonstrated an area under the receiver operating characteristics curve of 0.867 (95% CI: 0.813–0.921). Internal verification confirmed the model's accuracy, with the calibration curve approaching the ideal and the Hosmer–Lemeshow goodness-of-fit test showing consistent results (χ2 = 12.212, p = 0.142). ConclusionIn paediatric patients with TB, the absolute counts of all lymphocyte subsets were considerably reduced compared with those in patients with bacterial CAP. Clinicians should consider the possibility of EPTB infection in addition to respiratory infections in children with TB who have higher CD3, CD4 and CD8 T-cell counts than the ERTB group. Furthermore, CD4 T-cell counts correlated closely with the severity of chest CT lesions. Age, symptom duration, A/G ratio, Hs-CRP level and NLR were established as independent risk factors for STB. The nomogram model, based on these factors, offers effective discrimination and calibration in predicting STB in children.

Segmentation of lung lobes and lesions in chest CT for the classification of COVID-19 severity

To precisely determine the severity of COVID-19-related pneumonia, computed tomography (CT) is an imaging modality beneficial for patient monitoring and therapy planning. Thus, we aimed to develop a deep learning-based image segmentation model to automatically assess lung lesions related to COVID-19 infection and calculate the total severity score (TSS). The entire dataset consisted of 124 COVID-19 patients acquired from Chulabhorn Hospital, divided into 28 cases without lung lesions and 96 cases with lung lesions categorized severity by radiologists regarding TSS. The model used a 3D-UNet along with DenseNet and ResNet models that had already been trained to separate the lobes of the lungs and figure out the percentage of lung involvement due to COVID-19 infection. It also used the Dice similarity coefficient (DSC) to measure TSS. Our final model, consisting of 3D-UNet integrated with DenseNet169, achieved segmentation of lung lobes and lesions with the Dice similarity coefficients of 91.52% and 76.89%, respectively. The calculated TSS values were similar to those evaluated by radiologists, with an R2 of 0.842. The correlation between the ground-truth TSS and model prediction was greater than that of the radiologist, which was 0.890 and 0.709, respectively.

Computed tomography findings in patients with pulmonary tuberculosis and diabetes at an infectious disease hospital in China: a retrospective cross-sectional study

BackgroundThis study aimed to investigate the relationship between active pulmonary tuberculosis (TB) and type 2 diabetes mellitus (T2DM) by analysing the clinical features and computed tomography (CT) findings of patients with active pulmonary TB and comorbid T2DM (TB-DM) in the LiangShan Yi regions.MethodsWe collected data from 154 hospitalised patients with TB-DM initially confirmed at an infectious disease hospital in the Liangshan Yi Autonomous Prefecture between 1 and 2019, and 31 December 2021. These were matched by sex and age ± 3 years to 145 hospitalised patients with initially confirmed pulmonary TB without comorbid T2DM (TB-NDM) over the same period. The clinical characteristics of the two groups were analysed separately. Three group-blinded radiologists independently analysed the CT findings and classified them into mild-to-moderate and severe groups. Severe chest CT lesion refers to a lesion that is less diffused or moderately dense and either exceeds the total volume of one lung, a high-density fused lesion greater than one-third of the volume of one lung, or a cavitary lesion with a maximum diameter ≥ 4 cm.ResultsNo significant differences were observed in the presentation of clinical features. Regarding the severity of chest CT manifestation, patients with TB-DM had significantly more severe TB than those with TB-NDM (89.61% vs. 68.97%, P < 0.0001). Regarding CT findings, patients with TB-DM had higher proportions of consolidation (79.22% vs. 52.41%, P < 0.0001), cavitary lesions (85.06% vs. 59.31%, P < 0.0001), bronchiectasis (71.43% vs. 31.03%, P < 0.0001), exudative lesions (88.96% vs. 68.28%, P < 0.0001), and fibrous lesions (93.51% vs. 68.97%, P < 0.0001) than patients with TB-NDM. In conclusion, patients with TB-DM have more severe pulmonary TB CT findings than those without. There were no significant differences in the distribution of lesions in the lung lobes between TB-DM and TB-NDM patients.ConclusionsAmong patients hospitalised with pulmonary TB, those with T2DM had more severe findings on chest CT than those without T2DM. However, the clinical presentation was not significantly different.

Long-term respiratory follow-up of ICU hospitalized COVID-19 patients: Prospective cohort study.

Coronavirus disease (COVID-19) survivors exhibit multisystemic alterations after hospitalization. Little is known about long-term imaging and pulmonary function of hospitalized patients intensive care unit (ICU) who survive COVID-19. We aimed to investigate long-term consequences of COVID-19 on the respiratory system of patients discharged from hospital ICU and identify risk factors associated with chest computed tomography (CT) lesion severity. A prospective cohort study of COVID-19 patients admitted to a tertiary hospital ICU in Brazil (March-August/2020), and followed-up six-twelve months after hospital admission. Initial assessment included: modified Medical Research Council dyspnea scale, SpO2 evaluation, forced vital capacity, and chest X-Ray. Patients with alterations in at least one of these examinations were eligible for CT and pulmonary function tests (PFTs) approximately 16 months after hospital admission. Primary outcome: CT lesion severity (fibrotic-like or non-fibrotic-like). Baseline clinical variables were used to build a machine learning model (ML) to predict the severity of CT lesion. In total, 326 patients (72%) were eligible for CT and PFTs. COVID-19 CT lesions were identified in 81.8% of patients, and half of them showed mild restrictive lung impairment and impaired lung diffusion capacity. Patients with COVID-19 CT findings were stratified into two categories of lesion severity: non-fibrotic-like (50.8%-ground-glass opacities/reticulations) and fibrotic-like (49.2%-traction bronchiectasis/architectural distortion). No association between CT feature severity and altered lung diffusion or functional restrictive/obstructive patterns was found. The ML detected that male sex, ICU and invasive mechanic ventilation (IMV) period, tracheostomy and vasoactive drug need during hospitalization were predictors of CT lesion severity(sensitivity,0.78±0.02;specificity,0.79±0.01;F1-score,0.78±0.02;positive predictive rate,0.78±0.02; accuracy,0.78±0.02; and area under the curve,0.83±0.01). ICU hospitalization due to COVID-19 led to respiratory system alterations six-twelve months after hospital admission. Male sex and critical disease acute phase, characterized by a longer ICU and IMV period, and need for tracheostomy and vasoactive drugs, were risk factors for severe CT lesions six-twelve months after hospital admission.

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 (, source A), validation (, source A) and testing (, source A; , 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.

Long-term chest CT follow-up in COVID-19 Survivors: 102-361 days after onset.

BackgroundThe aim of this study was to evaluate long-term longitudinal changes in chest computed tomography (CT) findings in coronavirus disease 2019 (COVID-19) survivors and their correlations with dyspnea after discharge.MethodsA total of 337 COVID-19 survivors who underwent CT scan during hospitalization and between 102 and 361 days after onset were retrospectively included. Subjective CT findings, lesion volume, therapeutic measures and laboratory parameters were collected. The severity of the survivors' dyspnea was determined by follow-up questionnaire. The evolution of the CT findings from the peak period to discharge and throughout follow-up and the abilities of CT findings and clinical parameters to predict survival with and without dyspnea were analyzed.ResultsNinety-one COVID-19 survivors still had dyspnea at follow-up. The age, comorbidity score, duration of hospital stays, receipt of hormone administration, receipt of immunoglobulin injections, intensive care unit (ICU) admission, receipt of mechanical ventilation, laboratory parameters, clinical classifications and parameters associated with lesion volume of the survivors with dyspnea were significantly different from those of survivors without dyspnea. Among the clinical parameters and CT parameters used to identify dyspnea, parameters associated with lesion volume showed the largest area under the curve (AUC) values, with lesion volume at discharge showing the largest AUC (0.820). Lesion volume decreased gradually from the peak period to discharge and through follow-up, with a notable decrease observed after discharge. Absorption of lesions continued 6 months after discharge.ConclusionsAmong the clinical parameters and subjective CT findings, CT findings associated with lesion volume were the best predictors of post-discharge dyspnea in COVID-19 survivors.

Serial quantitative chest computed tomography imaging as prognosticators of coronavirus disease 2019 pneumonia

In 2019, a large outbreak of a novel coronavirus disease (COVID-19) occurred in China. The purpose of this study is to quantitatively analyze the evolution of chest computed tomography (CT) imaging features in COVID-19. Nine patients with positive real-time reverse-transcriptase polymerase chain reaction results were included in this study. Totally 19 CT scans were analyzed. Lesion density, lesion volume, and lesion load were higher in the severe group than in the mild group. A significantly positive correlation was noted between major laboratory prognosticators with lesion volume and load. Lesion load at the first week of disease was significantly higher in severe group (p = 0.03). Our study revealed that several CT features were significantly different between severely and mildly infected forms of COVID-19 pneumonia. The CT lesion load value at the first week of infection may be applied as an outcome predictor of the disease.

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

ObjectiveTo evaluate the association of changes in chest computed tomography (CT) lesion densities with clinical improvement in COVID‐19 patients.MethodsThis 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.ResultsForty 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.ConclusionThis is the first study in the literature reporting reduction in chest CT lesion densities correlated with clinical and laboratory improvement in COVID‐19 patients.

Glycemic status affects the severity of coronavirus disease 2019 in patients with diabetes mellitus: an observational study of CT radiological manifestations using an artificial intelligence algorithm

AimsIncreasing evidence suggests that poor glycemic control in diabetic individuals is associated with poor coronavirus disease 2019 (COVID-19) pneumonia outcomes and influences chest computed tomography (CT) manifestations. This study aimed to explore the impact of diabetes mellitus (DM) and glycemic control on chest CT manifestations, acquired using an artificial intelligence (AI)-based quantitative evaluation system, and COVID-19 disease severity and to investigate the association between CT lesions and clinical outcome.MethodsA total of 126 patients with COVID-19 were enrolled in this retrospective study. According to their clinical history of DM and glycosylated hemoglobin (HbA1c) level, the patients were divided into 3 groups: the non-DM group (Group 1); the well-controlled blood glucose (BG) group, with HbA1c < 7% (Group 2); and the poorly controlled BG group, with HbA1c ≥ 7% (Group 3). The chest CT images were analyzed with an AI-based quantitative evaluation system. Three main quantitative CT features representing the percentage of total lung lesion volume (PLV), percentage of ground-glass opacity volume (PGV) and percentage of consolidation volume (PCV) in bilateral lung fields were used to evaluate the severity of pneumonia lesions.ResultsPatients in Group 3 had the highest percentage of severe or critical illness, with 12 (32%) cases, followed by 6 (11%) and 7 (23%) cases in Groups 1 and 2, respectively (p = 0.042). The composite endpoints, including death or using mechanical ventilation or admission to the intensive care unit (ICU), were 3 (5%), 5 (16%) and 10 (26%) in Groups 1, 2 and 3, respectively (p = 0.013). The PLV, PGV and PCV in bilateral lung fields were significantly different among the three groups (all p < 0.001): the median PLVs were 12.5% (Group 3), 3.8% (Group 2) and 2.4% (Group 1); the median PGVs were 10.2% (Group 3), 3.6% (Group 2) and 1.9% (Group 1); and the median PCVs were 1.8% (Group 3), 0.3% (Group 2) and 0.1% (Group 1). In the linear regression analyses, which were adjusted for age, sex, BMI, and comorbidities, HbA1c remained positively associated with PLV (β = 0.401, p < 0.001), PGV (β = 0.364, p = 0.001) and PCV (β = 0.472, p < 0.001); this relationship was also observed between fasting blood glucose (FBG) and the three CT quantitative parameters. In the logistic regression analyses, PLV [OR 1.067 (1.032, 1.103)], PGV [OR 1.076 (1.034, 1.120)] and PCV [OR 1.280 (1.110, 1.476)] levels were independent predictors of the composite endpoints, as well as the areas under the ROC (AUCs) for PLV [AUC 0.796 (0.691, 0.900)], PGV [AUC 0.783 (0.678, 0.889)] and PCV [AUC 0.816 (0.722, 0.911)]; the ORs were still significant for CT lesions after adjusting for age, sex and poorly controlled diabetes.ConclusionsIncreased blood glucose level was correlated with the severity of lung involvement, as evidenced by certain chest CT parameters, and clinical prognosis in diabetic COVID-19 patients. There was a positive correlation between blood glucose level (both HbA1c and FBG) on admission and lung lesions. Moreover, the CT lesion severity by AI quantitative analysis was correlated with clinical outcomes.

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

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. 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. 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). 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.

Hemoperfusion in patients with severe COVID-19 respiratory failure, lifesaving or not?

Background:The new coronavirus outbreak quickly filled hospital beds and stunned the world. Intensive care is required for 5% of patients, and the mortality rate for critically ill patients is 49%. The “cytokine storm” is considered as the main cause of pathogenesis for coronavirus disease-19 (COVID-19)-related respiratory failure, hemoperfusion may be a modality for treatment of disease.Materials and Methods:Thirty-seven an patients with positive real-time polymerase chain reaction for SARStions2 in an upper respiratory tract sample or typical chest computed tomography lesion were eligible for this case–control study. Patients meeting the criteria for hemoperfusion including clinical and laboratory indices, were evaluated for outcomes such as hospitalization length and mortality. Patients were divided into three groups, i.e., patients who received hemoperfusion without a need for mechanical ventilation (MV), patients who received hemoperfusion before MV, and patients who received hemoperfusion after MV.Results:Among 37 patients with COVID-19 respiratory failure, 32% were female with a mean age of 55.54 (standard deviation 14.1) years. There was no statistically significant difference between the three groups in terms of length of hospital stay and intensive care unit (ICU) stay (P-tayns: 0.593 and 0.243, respectively, confidence interval [CI]: 95%). Heart rate, respiratory rate, PaO2/FIO2, high-sensitivity C-reactive protein, and ferritin significantly improved after the application of hemoperfusion in all groups (P < 0.05, CI: 95%).Conclusion:It seems that applying hemoperfusion in the inflammatory phase of the disease, especially before the intubation, reduce the need for MV. However, hemoperfusion does not have any impacts on the duration of hospital and ICU stay.

Correlation Between Quantitative Assessment of Chest Computed Tomography (CT) Imaging and Prognosis of COVID-19 Patients.

BackgroundThe aim of our work was to evaluate the correlation between the quantitative parameters of the peak lesion to 25% improvement time (PIT25) and the prognosis of new coronavirus pneumonia (COVID-19) patients by analyzing the changes of chest CT imaging.Material/MethodsThis retrospective analysis included 68 patients with COVID-19 in the Fifth People’s Hospital of Suzhou City. Three radiologists performed a blind evaluation of 4 chest CT images that included the initial scans, the lesion peak, the lesion decreased to 25% of the peak, and the final scan. The score of chest CT lesion, the imaging characteristics of the lesion, the time of the appearance of symptoms related to the CT examination, quantitative assessment of PIT25, and the absorption of the lesion in last CT image were analyzed. Patients were divided into an obvious absorption group and a non-obvious absorption group according to the reduction of the lesion area by greater than 50% or less than 50%.ResultsIn the peak time, the most common images of CT were ground-glass opacities (94.1%), consolidation (85.3%) and reticulation (88.2%), multifocal (97.1%), center and subpleural (54.4%), subpleural distribution (45.6%), and pleural thickening (79.4%). The PIT25 with the prognosis (r=0.53, p=0.00) was significantly relevant. PIT25 was 4.3±0.7 days for the obvious absorption group and 6.8±1.4 days for the non-obvious absorption group.ConclusionsThe features of CT image are specific at the peak. The quantitative parameter PIT25 could be used to predict the prognosis of the patients with COVID-19, as COVID-19 patients with a shorter PIT25 have a better prognosis and vice versa.

A valuable computed tomography-based new diagnostic tool for severe chest lesions in active pulmonary tuberculosis: combined application of influencing factors

The objective of the present investigation was to explore the influencing factors and value of computed tomography (CT) for diagnosing severe chest lesions in active pulmonary tuberculosis (APTB). This retrospective investigation included 463 patients diagnosed with APTB. Relevant clinical features were collected. Patients were assigned to mild/moderate group or advanced group depending on the lesion severity on chest CT, severe chest CT lesion refers to the moderately dense or less diffuse lesion that exceeds the total volume of one lung, or the dense fusion lesion greater than one third of the volume of one lung, or the lesion with cavity diameter ≥4 cm. Independent risk factors for severe lesions were determined by univariate and multivariate logistic regression analyses, and the diagnostic efficiency of the risk factors was assessed by receiver operating characteristic curve (ROC). Chest CT demonstrated that there were 285 (61.56%) cases with severe lesions; multivariate Logistic regression analysis showed dust exposure [odds ratio (OR) = 4.108, 95% confidence interval (CI) 2.416–6.986], patient classification (OR = 1.792, 95% CI 1.067–3.012), age (OR = 1.018, 95% CI 1.005–1.030), and albumin-globulin ratio (OR = 0.179, 95% CI 0.084–0.383) to be independently correlated with severe lesions on chest CT. ROC curve analysis showed the cutoff values of age, albumin-globulin ratio and combined score to be 39 years, 0.918 and −0.085, respectively. The predictive value of combined score area under the curve 0.753 (95% CI 0.708–0.798) was higher than that of any single factor. The combined score of these four factors further improved the predictive efficacy.