Quantitative analysis of chest computed tomography of COVID-19 pneumonia using a software widely used in Japan
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ABSTRACT:
SUBMITTER: Suzuki M
PROVIDER: S-EPMC10593239 | biostudies-literature | 2023 Jan
REPOSITORIES: biostudies-literature
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Project description:Available information on chest Computed Tomography (CT) findings of the 2019 novel coronavirus disease (COVID-19) is constantly evolving. Ground glass opacities and consolidation with bilateral and peripheral distribution were reported as the most common CT findings, but also less typical features could be identified. All radiologists should be aware of the imaging spectrum of the COVID-19 pneumonia and imaging changes in the course of the disease. Our aim is to display the chest CT findings at first assessment and follow-up through a pictorial essay, to help in the recognition of these features for an accurate diagnosis.
| S-EPMC7315402 | biostudies-literature
Project description:BackgroundComputed tomography (CT) imaging and artificial intelligence (AI)-based analyses have aided in the diagnosis and prediction of the severity of COVID-19. However, the potential of AI-based CT quantification of pneumonia in assessing patients with COVID-19 has not yet been fully explored. This study aimed to investigate the potential of AI-based CT quantification of COVID-19 pneumonia to predict the critical outcomes and clinical characteristics of patients with residual lung lesions.MethodsThis retrospective cohort study included 1,200 hospitalized patients with COVID-19 from four hospitals. The incidence of critical outcomes (requiring the support of high-flow oxygen or invasive mechanical ventilation or death) and complications during hospitalization (bacterial infection, renal failure, heart failure, thromboembolism, and liver dysfunction) was compared between the groups of pneumonia with high/low-percentage lung lesions, based on AI-based CT quantification. Additionally, 198 patients underwent CT scans 3 months after admission to analyze prognostic factors for residual lung lesions.ResultsThe pneumonia group with a high percentage of lung lesions (N = 400) had a higher incidence of critical outcomes and complications during hospitalization than the low percentage group (N = 800). Multivariable analysis demonstrated that AI-based CT quantification of pneumonia was independently associated with critical outcomes (adjusted odds ratio [aOR] 10.5, 95% confidence interval [CI] 5.59-19.7), as well as with oxygen requirement (aOR 6.35, 95% CI 4.60-8.76), IMV requirement (aOR 7.73, 95% CI 2.52-23.7), and mortality rate (aOR 6.46, 95% CI 1.87-22.3). Among patients with follow-up CT scans (N = 198), the multivariable analysis revealed that the pneumonia group with a high percentage of lung lesions on admission (aOR 4.74, 95% CI 2.36-9.52), older age (aOR 2.53, 95% CI 1.16-5.51), female sex (aOR 2.41, 95% CI 1.13-5.11), and medical history of hypertension (aOR 2.22, 95% CI 1.09-4.50) independently predicted persistent residual lung lesions.ConclusionsAI-based CT quantification of pneumonia provides valuable information beyond qualitative evaluation by physicians, enabling the prediction of critical outcomes and residual lung lesions in patients with COVID-19.
| S-EPMC10552312 | biostudies-literature
Project description:ObjectiveThe aim of our study was to assess the effect of a short-term treatment with low-moderate corticosteroid (CS) doses by both a quantitative and qualitative assessment of chest HRCT of COVID-19 pneumonia.MethodsCORTICOVID is a single-center, cross-sectional, retrospective study involving severe/critical COVID-19 patients with mild/moderate ARDS. Lung total severity score was obtained according to Chung and colleagues. Moreover, the relative percentages of lung total severity score by ground glass opacities, consolidations, crazy paving, and linear bands were computed. Chest HRCT scores, P/F ratio, and laboratory parameters were evaluated before (pre-CS) and 7-10 days after (post-CS) methylprednisolone of 0.5-0.8 mg/kg/day.FindingsA total of 34 severe/critical COVID-19 patients were included in the study, of which 17 received Standard of Care (SoC) and 17 CS therapy in add-on. CS treatment disclosed a significant decrease in HRCT total severity score [median = 6 (IQR: 5-7.5) versus 10 (IQR: 9-13) in SoC, p < 0.001], as well in single consolidations [median = 0.33 (IQR: 0-0.92) versus 6.73 (IQR: 2.49-8.03) in SoC, p < 0.001] and crazy paving scores [mean = 0.19 (SD = 0.53) versus 1.79 (SD = 2.71) in SoC, p = 0.010], along with a significant increase in linear bands [mean = 2.56 (SD = 1.65) versus 0.97 (SD = 1.30) in SoC, p = 0.006]. GGO score instead did not significantly differ at the end of treatment between the two groups. Most post-CS GGO, however, derived from previous consolidations and crazy paving [median = 1.5 (0.35-3.81) versus 2 (1.25-3.8) pre-CS; p = 0.579], while pre-CS GGO significantly decreased after methylprednisolone therapy [median = 0.66 (0.05-1.33) versus 1.5 (0.35-3.81) pre-CS; p = 0.004]. CS therapy further determined a significant improvement in P/F levels [median P/F = 310 (IQR: 235.5-370) versus 136 (IQR: 98.5-211.75) in SoC; p < 0.001], and a significant increase in white blood cells, lymphocytes, and neutrophils absolute values.ConclusionThe improvement of all chest HRCT findings further supports the role of CS adjunctive therapy in severe/critical COVID-19 pneumonia.
| S-EPMC8477677 | biostudies-literature
Project description:PurposeThe purpose of our study was to determine the usability of lung ultrasonography (LUS) in the diagnosis of COVID-19, and to match the morphological features of lesions detected on computed tomography (CT) with the findings observed on LUS.MethodsSixty patients with COVID-19 were included in this prospective study. Patients were examined by radiology and anesthesia clinic specialists for a visual CT score. A LUS 12-zone ultrasonography protocol was applied by the investigator blinded to the CT and PCR test results. The characteristics of abnormal findings and the relationship of lesions to the pleura and the distance to the pleura were investigated.ResultsForty-five males and 25 females evaluated within the scope of the study had an average age of 61.2 ± 15.3 years. The total CT score was calculated as 14.3 ± 5.3, and the LUS score was found to be 19.9 ± 7.6. There was a statistically significant positive correlation between the measured LUS and CT scores (r = 0.857, p < 0.001). The mean distance of these lesions to the pleura was 5.2 ± 1.76 cm. LUS findings in 51 areas corresponded to non-pleural lesions on CT. There was a negative correlation between the measured distance to the pleura and the LUS scores (p < 0.001, r = - 0.708).ConclusionThe results of this study showed that the correlation between CT and LUS findings may be used in the diagnosis of COVID-19 pneumonia, although there are some limitations. ClinicalTrials.gov identifier: NCT04719234.
| S-EPMC7908963 | biostudies-literature
Project description:Despite extraordinary international efforts to dampen the spread and understand the mechanisms behind SARS-CoV-2 infections, accessible predictive biomarkers directly applicable in the clinic are yet to be discovered. Recent studies have revealed that diverse types of assays bear limited predictive power for COVID-19 outcomes. Here, we harness the predictive power of chest computed tomography (CT) in combination with plasma cytokines using a machine learning and k-fold cross-validation approach for predicting death during hospitalization and maximum severity degree in COVID-19 patients. Patients (n = 152) from the Mount Sinai Health System in New York with plasma cytokine assessment and a chest CT within five days from admission were included. Demographics, clinical, and laboratory variables, including plasma cytokines (IL-6, IL-8, and TNF-α), were collected from the electronic medical record. We found that CT quantitative alone was better at predicting severity (AUC 0.81) than death (AUC 0.70), while cytokine measurements alone better-predicted death (AUC 0.70) compared to severity (AUC 0.66). When combined, chest CT and plasma cytokines were good predictors of death (AUC 0.78) and maximum severity (AUC 0.82). Finally, we provide a simple scoring system (nomogram) using plasma IL-6, IL-8, TNF-α, ground-glass opacities (GGO) to aerated lung ratio and age as new metrics that may be used to monitor patients upon hospitalization and help physicians make critical decisions and considerations for patients at high risk of death for COVID-19.
| S-EPMC9356575 | biostudies-literature
Project description:Background: The diagnosis of osteoporosis is still one of the most critical topics for orthopedic surgeons worldwide. One research direction is to use existing clinical imaging data for accurate measurements of bone mineral density (BMD) without additional radiation. Methods: A novel phantom-less quantitative computed tomography (PL-QCT) system was developed to measure BMD and diagnose osteoporosis, as our previous study reported. Compared with traditional phantom-less QCT, this tool can conduct an automatic selection of body tissues and complete the BMD calibration with high efficacy and precision. The function has great advantages in big data screening and thus expands the scope of use of this novel PL-QCT. In this study, we utilized lung cancer or COVID-19 screening low-dose computed tomography (LDCT) of 649 patients for BMD calibration by the novel PL-QCT, and we made the BMD changes with age based on this PL-QCT. Results: The results show that the novel PL-QCT can predict osteoporosis with relatively high accuracy and precision using LDCT, and the AUC values range from 0.68 to 0.88 with DXA results as diagnosis reference. The relationship between PL-QCT BMD with age is close to the real trend population (from ∼160 mg/cc in less than 30 years old to ∼70 mg/cc in greater than 80 years old for both female and male groups). Additionally, the calculation results of Pearson's r-values for correlation between CT values with BMD in different CT devices were 0.85-0.99. Conclusion: To our knowledge, it is the first time for automatic PL-QCT to evaluate the performance against dual-energy X-ray absorptiometry (DXA) in LDCT images. The results indicate that it may be a promising tool for individuals screened for low-dose chest computed tomography.
| S-EPMC9273929 | biostudies-literature
Project description: Not available
| S-EPMC6549062 | biostudies-literature
Project description:Presently, coronavirus disease-19 (COVID-19) is spreading worldwide without an effective treatment method. For COVID-19, which is often asymptomatic, it is essential to adopt a method that does not cause aggravation, as well as a method to prevent infection. Whether aggravation can be predicted by analyzing the extent of lung damage on chest computed tomography (CT) scans was examined. The extent of lung damage on pre-intubation chest CT scans of 277 patients with COVID-19 was assessed. It was observed that aggravation occurred when the CT scan showed extensive damage associated with ground-glass opacification and/or consolidation (p < 0.0001). The extent of lung damage was similar across the upper, middle, and lower fields. Furthermore, upon comparing the extent of lung damage based on the number of days after onset, a significant difference was found between the severe pneumonia group (SPG) with intubation or those who died and non-severe pneumonia group (NSPG) ≥3 days after onset, with aggravation observed when ≥14.5% of the lungs exhibited damage at 3–5 days (sensitivity: 88.2%, specificity: 72.4%) and when ≥20.1% of the lungs exhibited damage at 6–8 days (sensitivity: 88.2%, specificity: 69.4%). Patients with aggravation suddenly developed hypoxemia after 7 days from the onset; however, chest CT scans obtained in the paucisymptomatic phase without hypoxemia indicated that subsequent aggravation could be predicted based on the degree of lung damage. Furthermore, in subjects aged ≥65 years, a significant difference between the SPG and NSPG was observed in the extent of lung damage early beginning from 3 days after onset, and it was found that the degree of lung damage could serve as a predictor of aggravation. Therefore, to predict and improve prognosis through rapid and appropriate management, evaluating patients with factors indicating poor prognosis using chest CT is essential.
| S-EPMC9632883 | biostudies-literature
Project description:ObjectiveQuantitative chest computed tomography (qCT) methods are new tools that objectively measure parenchymal abnormalities and vascular features on CT images in patients with interstitial lung disease (ILD). We aimed to investigate whether the qCT measures are predictors of 5-year mortality in patients with systemic sclerosis (SSc).MethodsPatients diagnosed with SSc were retrospectively selected from 2011 to 2022. Patients should have had volumetric high-resolution CTs (HRCTs) and pulmonary function tests (PFTs) performed at baseline and at 24 months of follow-up. The following parameters were evaluated in HRCTs using Computer-Aided Lung Informatics for Pathology Evaluation and Rating (CALIPER): ground glass opacities, reticular pattern, honeycombing, and pulmonary vascular volume. Factors associated with death were evaluated by Kaplan‒Meier survival curves and multivariate analysis models. Semiquantitative analysis of the HRCTs images was also performed.ResultsSeventy-one patients were included (mean age, 54.2 years). Eleven patients (15.49%) died during the follow-up, and all patients had ILD. As shown by Kaplan‒Meier curves, survival was worse among patients with an ILD extent (ground glass opacities + reticular pattern + honeycombing) ≥ 6.32%, a reticular pattern ≥ 1.41% and a forced vital capacity (FVC) < 70% at baseline. The independent predictors of mortality by multivariate analysis were a higher reticular pattern (Exp 2.70, 95%CI 1.26-5.82) on qCT at baseline, younger age (Exp 0.906, 95%CI 0.826-0.995), and absolute FVC decline ≥ 5% at follow-up (Exp 15.01, 95%CI 1.90-118.5), but not baseline FVC. Patients with extensive disease (>20% extension) by semiquantitative analysis according to Goh's staging system had higher disease extension on qCT at baseline and follow-up.ConclusionThis study showed that the reticular pattern assessed by baseline qCT may be a useful tool in the clinical practice for assessing lung damage and predicting mortality in SSc.
| S-EPMC11432915 | biostudies-literature
Project description:Chest computed tomography (CT) is effective for assessing the severity of coronavirus disease 2019 (COVID-19). However, the clinical factors reflecting the disease progression of COVID-19 pneumonia on chest CT and predicting a subsequent exacerbation remain controversial. We conducted a retrospective cohort study of 450 COVID-19 patients. We used an automated image processing tool to quantify the COVID-19 pneumonia lesion extent on chest CT at admission. The factors associated with the lesion extent were estimated by a multiple regression analysis. After adjusting for background factors by propensity score matching, we conducted a multivariate Cox proportional hazards analysis to identify factors associated with severe disease after admission. The multiple regression analysis identified, body-mass index (BMI), lactate dehydrogenase (LDH), C-reactive protein (CRP), and albumin as continuous variables associated with the lesion extent on chest CT. The standardized partial regression coefficients for them were 1.76, 2.42, 1.54, and 0.71. The multivariate Cox proportional hazards analysis identified LDH (hazard ratio, 1.003; 95% confidence interval, 1.001-1.005) as a factor independently associated with the development of severe COVID-19 pneumonia. Increased serum LDH at admission may be useful in real-world clinical practice for the simple screening of COVID-19 patients at high risk of developing subsequent severe disease.
| S-EPMC9848045 | biostudies-literature