Baseline Chest Computed Tomography Research Articles

Early temporal changes on CT chest in patients with COVID-19 pneumonia

Introduction: Computed tomography (CT) scan of the chest helps in the evaluation, prognostication, and follow-up of patients with Corona virus disease-19 (COVID-19) pneumonia.Objectives: To study the early temporal evolution of findings on CT chest in patients with COVID-19 pneumonia at 6 weeks follow up.Material and methods: A retrospective analysis was performed on 40 patients with confirmed COVID-19 pneumonia who underwent baseline chest CT scans followed by a repeat scan at six weeks. Patients were quantitatively assessed using an objective CT severity score and qualitatively assessed using imaging parameters like consolidation, ground glass opacities (GGOs), parenchymal bands, septal thickening, nodules and crazy paving pattern.Results: Mean age of our study population was 58.5 ± 10.2 years with 80% being males. The baseline CT scan revealed a total median CT severity score interquartile range (IQR) of 16/25 (range: 14-20) while the follow-up scan after six weeks revealed a median score (IQR) of 14/25 (range: 8-19). This difference was statistically significant (p=0.04). The difference in CT severity scores of bilateral upper lobes was statistically significant (p=0.04 on right side and p=0.009 on left side); however, the difference in scores of bilateral lower lobes was not statistically significant (p>0.05). The common imaging patterns on baseline imaging were GGO (97.5%), con-solidation (85%), septal thickening (85%), parenchymal bands (60%) and nodules (32.5%), which showed an interval resolution in the follow-up CT scan.Conclusion: Most of the patients with COVID-19 pneumonia showed interval resolution of lung findings seen on short-term follow-up CT scan.

Exploring bone density analysis on routine CT scans as a tool for opportunistic osteoporosis screening

The primary aim of this study was to evaluate computed tomography (CT)-based bone density analysis at the level of thoracic vertebra 12 (Th12) as a screening method for decreased bone density in patients admitted to the intensive care unit (ICU). Interobserver variability was analyzed. Secondary aims were to assess the prevalence of CT-based low bone density upon ICU admission in a cohort of COVID-19 patients and to assess the potential effect of long-term ICU stay on bone density in these patients. Retrospective single-center cohort study. ICU of the Leiden University Medical Center (LUMC), the Netherlands. Patients admitted to the ICU of the LUMC between March 1st, 2020 and February 1st, 2022 with a diagnosis of COVID-19, and a length of ICU stay of ≥ 21 days. In the included patients both baseline chest CT scans (obtained upon ICU admission) and follow-up chest CT scans (obtained ≥ 21 days after ICU admission) were available for analysis. A total of 118 CT scans in 38 patients were analyzed. There was a good interobserver variability, with an overall mean absolute difference (between measurements of three observers) of 9.7 Hounsfield Units (HU) and an intraclass correlation coefficient (ICC) of 0.93 (95% CI 0.88–0.96). The effect of intravenous contrast administration on bone density measurements was small (+ 7.5 HU (95% CI 3.4–11.5 HU)) higher in contrast enhanced CT images compared to non contrast enhanced CT images). Thirty-seven percent of patients had a bone density < 140 HU, suggestive of osteoporosis. No significant difference was found between bone density upon ICU admission and bone density at follow-up (≥ 21 days after ICU admission). Vertebral CT-based bone density analysis using routine CT scans is an easily applicable method to identify ICU patients with decreased bone density, which could enable enrollment in osteoporosis prevention programs. A high prevalence of low bone density was found in our cohort of ICU patients. There were no changes observed in bone density between baseline and follow-up measurements.

Time-dependent CT score-based model for identifying severe/critical COVID-19 at a fever clinic after the emergence of Omicron variant

Rationale and objectivesThe computed tomography (CT) score has been used to evaluate the severity of COVID-19 during the pandemic; however, most studies have overlooked the impact of infection duration on the CT score. This study aimed to determine the optimal cutoff CT score value for identifying severe/critical COVID-19 during different stages of infection and to construct corresponding predictive models using radiological characteristics and clinical factors. Materials and methodsThis retrospective study collected consecutive baseline chest CT images of confirmed COVID-19 patients from a fever clinic at a tertiary referral hospital from November 28, 2022, to January 8, 2023. Cohorts were divided into three subcohorts according to the time interval from symptom onset to CT examination at the hospital: early phase (0–3 days), intermediate phase (4–7 days), and late phase (8–14 days). The binary endpoints were mild/moderate and severe/critical infection. The CT scores and qualitative CT features were manually evaluated. A logistic regression analysis was performed on the CT score as determined by a visual assessment to predict severe/critical infection. Receiver operating characteristic analysis was performed and the area under the curve (AUC) was calculated. The optimal cutoff value was determined by maximizing the Youden index in each subcohort. A radiology score and integrated models were then constructed by combining the qualitative CT features and clinical features, respectively, using multivariate logistic regression with stepwise elimination. ResultsA total of 962 patients (aged, 61.7 ± 19.6 years; 490 men) were included; 179 (18.6%) were classified as severe/critical COVID-19, while 344 (35.8%) had a typical Radiological Society of North America (RSNA) COVID-19 appearance. The AUCs of the CT score models reached 0.91 (95% confidence interval (CI) 0.88–0.94), 0.82 (95% CI 0.76–0.87), and 0.83 (95% CI 0.77–0.89) during the early, intermediate, and late phases, respectively. The best cutoff values of the CT scores during each phase were 1.5, 4.5, and 5.5. The predictive accuracies associated with the time-dependent cutoff values reached 88% (vs.78%), 73% (vs. 63%), and 87% (vs. 57%), which were greater than those associated with universal cutoff value (all P < 0.001). The radiology score models reached AUCs of 0.96 (95% CI 0.94–0.98), 0.90 (95% CI 0.87–0.94), and 0.89 (95% CI 0.84–0.94) during the early, intermediate, and late phases, respectively. The integrated models including demographic and clinical risk factors greatly enhanced the AUC during the intermediate and late phases compared with the values obtained with the radiology score models; however, an improvement in accuracy was not observed. ConclusionThe time interval between symptom onset and CT examination should be tracked to determine the cutoff value for the CT score for identifying severe/critical COVID-19. The radiology score combining qualitative CT features and the CT score complements clinical factors for identifying severe/critical COVID-19 patients and facilitates timely hierarchical diagnoses and treatment.

Evaluation of retrieval accuracy and visual similarity in content-based image retrieval of chest CT for obstructive lung disease

The aim of our study was to assess the performance of content-based image retrieval (CBIR) for similar chest computed tomography (CT) in obstructive lung disease. This retrospective study included patients with obstructive lung disease who underwent volumetric chest CT scans. The CBIR database included 600 chest CT scans from 541 patients. To assess the system performance, follow-up chest CT scans of 50 patients were evaluated as query cases, which showed the stability of the CT findings between baseline and follow-up chest CT, as confirmed by thoracic radiologists. The CBIR system retrieved the top five similar CT scans for each query case from the database by quantifying and comparing emphysema extent and size, airway wall thickness, and peripheral pulmonary vasculatures in descending order from the database. The rates of retrieval of the same pairs of query CT scans in the top 1–5 retrievals were assessed. Two expert chest radiologists evaluated the visual similarities between the query and retrieved CT scans using a five-point scale grading system. The rates of retrieving the same pairs of query CTs were 60.0% (30/50) and 68.0% (34/50) for top-three and top-five retrievals. Radiologists rated 64.8% (95% confidence interval 58.8–70.4) of the retrieved CT scans with a visual similarity score of four or five and at least one case scored five points in 74% (74/100) of all query cases. The proposed CBIR system for obstructive lung disease integrating quantitative CT measures demonstrated potential for retrieving chest CT scans with similar imaging phenotypes. Further refinement and validation in this field would be valuable.

The relationship of fat and muscle measurements with emphysema and bronchial wall thickening in smokers.

Differences in body composition in patients with COPD may have important prognostic value and may provide opportunities for patient-specific management. We investigated the relation of thoracic fat and muscle with computed tomography (CT)-measured emphysema and bronchial wall thickening. Low-dose baseline chest CT scans from 1031 male lung cancer screening participants from one site were quantified for emphysema, bronchial wall thickening, subcutaneous fat, visceral fat and skeletal muscle. Body composition measurements were performed by segmenting the first slice above the aortic arch using Hounsfield unit thresholds with region growing and manual corrections. COPD presence and severity were evaluated with pre-bronchodilator spirometry testing. Participants had a median age of 61.5 years (58.6-65.6, 25th-75th percentile) and median number of 38.0 pack-years (28.0-49.5); 549 (53.2%) were current smokers. Overall, 396 (38.4%) had COPD (256 Global Initiative for Chronic Obstructive Lung Disease (GOLD) 1, 140 GOLD 2-3). Participants with COPD had less subcutaneous fat, visceral fat and skeletal muscle (p<0.001 for all). With increasing GOLD stages, subcutaneous (p=0.005) and visceral fat values (p=0.004) were higher, and skeletal muscle was lower (p=0.004). With increasing severity of CT-derived emphysema, subcutaneous fat, visceral fat and skeletal muscle values were lower (p<0.001 for all). With increasing CT-derived bronchial wall thickness, subcutaneous and visceral fat values were higher (p<0.001 for both), without difference in skeletal muscle. All statistical relationships remained when adjusted for age, pack-years and smoking status. COPD presence and emphysema severity are associated with smaller amounts of thoracic fat and muscle, whereas bronchial wall thickening is associated with fat accumulation.

Radiomics-based Machine Learning Methods for Volume Doubling Time Prediction of Pulmonary Ground-glass Nodules With Baseline Chest Computed Tomography.

Reliable prediction of volume doubling time (VDT) is essential for the personalized management of pulmonary ground-glass nodules (GGNs). We aimed to determine the optimal VDT prediction method by comparing different machine learning methods only based on the baseline chest computed tomography (CT) images. Seven classical machine learning methods were evaluated in terms of their stability and performance for VDT prediction. The VDT, calculated by the preoperative and baseline CT, was divided into 2 groups with a cutoff value of 400 days. A total of 90 GGNs from 3 hospitals constituted the training set, and 86 GGNs from the fourth hospital served as the external validation set. The training set was used for feature selection and model training, and the validation set was used to evaluate the predictive performance of the model independently. The eXtreme Gradient Boosting showed the highest predictive performance (accuracy: 0.890±0.128 and area under the ROC curve (AUC): 0.896±0.134), followed by the neural network (NNet) (accuracy: 0.865±0.103 and AUC: 0.886±0.097). While regarding stability, the NNet showed the highest robustness against data perturbation (relative SDs [%] of mean AUC: 10.9%). Therefore, the NNet was chosen as the final model, achieving high accuracy of 0.756 in the external validation set. The NNet is a promising machine learning method to predict the VDT of GGNs, which would assist in the personalized follow-up and treatment strategies for GGNs reducing unnecessary follow-up and radiation dose.

Predicting omicron pneumonia severity and outcome: a single-center study in Hangzhou, China.

In December 2022, there was a large Omicron epidemic in Hangzhou, China. Many people were diagnosed with Omicron pneumonia with variable symptom severity and outcome. Computed tomography (CT) imaging has been proven to be an important tool for COVID-19 pneumonia screening and quantification. We hypothesized that CT-based machine learning algorithms can predict disease severity and outcome in Omicron pneumonia, and we compared its performance with the pneumonia severity index (PSI)-related clinical and biological features. Our study included 238 patients with the Omicron variant who have been admitted to our hospital in China from 15 December 2022 to 16 January 2023 (the first wave after the dynamic zero-COVID strategy stopped). All patients had a positive real-time polymerase chain reaction (PCR) or lateral flow antigen test for SARS-CoV-2 after vaccination and no previous SARS-CoV-2 infections. We recorded patient baseline information pertaining to demographics, comorbid conditions, vital signs, and available laboratory data. All CT images were processed with a commercial artificial intelligence (AI) algorithm to obtain the volume and percentage of consolidation and infiltration related to Omicron pneumonia. The support vector machine (SVM) model was used to predict the disease severity and outcome. The receiver operating characteristic (ROC) area under the curve (AUC) of the machine learning classifier using PSI-related features was 0.85 (accuracy = 87.40%, p < 0.001) for predicting severity while that using CT-based features was only 0.70 (accuracy = 76.47%, p = 0.014). If combined, the AUC was not increased, showing 0.84 (accuracy = 84.03%, p < 0.001). Trained on outcome prediction, the classifier reached the AUC of 0.85 using PSI-related features (accuracy = 85.29%, p < 0.001), which was higher than using CT-based features (AUC = 0.67, accuracy = 75.21%, p < 0.001). If combined, the integrated model showed a slightly higher AUC of 0.86 (accuracy = 86.13%, p < 0.001). Oxygen saturation, IL-6, and CT infiltration showed great importance in both predicting severity and outcome. Our study provided a comprehensive analysis and comparison between baseline chest CT and clinical assessment in disease severity and outcome prediction in Omicron pneumonia. The predictive model accurately predicts the severity and outcome of Omicron infection. Oxygen saturation, IL-6, and infiltration in chest CT were found to be important biomarkers. This approach has the potential to provide frontline physicians with an objective tool to manage Omicron patients more effectively in time-sensitive, stressful, and potentially resource-constrained environments.

Extension of Lung Damage at Chest Computed Tomography in Severely Ill COVID-19 Patients Treated with Interleukin-6 Receptor Blockers Correlates with Inflammatory Cytokines Production and Prognosis.

Elevated inflammatory markers are associated with severe coronavirus disease 2019 (COVID-19), and some patients benefit from Interleukin (IL)-6 pathway inhibitors. Different chest computed tomography (CT) scoring systems have shown a prognostic value in COVID-19, but not specifically in anti-IL-6-treated patients at high risk of respiratory failure. We aimed to explore the relationship between baseline CT findings and inflammatory conditions and to evaluate the prognostic value of chest CT scores and laboratory findings in COVID-19 patients specifically treated with anti-IL-6. Baseline CT lung involvement was assessed in 51 hospitalized COVID-19 patients naive to glucocorticoids and other immunosuppressants using four CT scoring systems. CT data were correlated with systemic inflammation and 30-day prognosis after anti-IL-6 treatment. All the considered CT scores showed a negative correlation with pulmonary function and a positive one with C-reactive protein (CRP), IL-6, IL-8, and Tumor Necrosis Factor α (TNF-α) serum levels. All the performed scores were prognostic factors, but the disease extension assessed by the six-lung-zone CT score (S24) was the only independently associated with intensive care unit (ICU) admission (p = 0.04). In conclusion, CT involvement correlates with laboratory inflammation markers and is an independent prognostic factor in COVID-19 patients representing a further tool to implement prognostic stratification in hospitalized patients.

Migratory Pneumonia in Prolonged SARS-CoV-2 Infection in Patients Treated With B-cell Depletion Therapies for B-cell Lymphoma.

To report the clinical and radiological characteristics of patients with underlying B-cell lymphoma and coronavirus disease 2019 (COVID-19) showing migratory airspace opacities on serial chest computed tomography (CT) with persistent COVID-19 symptoms. From January 2020 to June 2022, of the 56 patients with underlying hematologic malignancy who had undergone chest CT more than once at our hospital after acquiring COVID-19, seven adult patients (5 female; age range, 37-71 years; median age, 45 years) who showed migratory airspace opacities on chest CT were selected for the analysis of clinical and CT features. All patients had been diagnosed with B-cell lymphoma (three diffuse large B-cell lymphoma and four follicular lymphoma) and had received B-cell depleting chemotherapy, including rituximab, within three months prior to COVID-19 diagnosis. The patients underwent a median of 3 CT scans during the follow-up period (median 124 days). All patients showed multifocal patchy peripheral ground glass opacities (GGOs) with basal predominance in the baseline CTs. In all patients, follow-up CTs demonstrated clearing of previous airspace opacities with the development of new peripheral and peribronchial GGO and consolidation in different locations. Throughout the follow-up period, all patients demonstrated prolonged COVID-19 symptoms accompanied by positive polymerase chain reaction results from nasopharyngeal swabs, with cycle threshold values of less than 25. COVID-19 patients with B-cell lymphoma who had received B-cell depleting therapy and are experiencing prolonged SARS-CoV-2 infection and persistent symptoms may demonstrate migratory airspace opacities on serial CT, which could be interpreted as ongoing COVID-19 pneumonia.

Interstitial Lung Abnormalities in Patients With Locally Advanced Esophageal Cancer: Prevalence, Risk Factors, and Clinical Implications.

Interstitial lung abnormalities (ILAs) represent nondependent abnormalities on chest computed tomography (CT) indicating lung parenchymal damages due to inflammation and fibrosis. Interstitial lung abnormalities have been studied as a predictor of clinical outcome in lung cancer, but not in other thoracic malignancies. The present study investigated the prevalence of ILA in patients with esophageal cancer and identified risk factors and clinical implications of ILA in these patients. The study included 208 patients with locally advanced esophageal cancer (median age, 65.6 years; 166 males, 42 females). Interstitial lung abnormality was scored on baseline CT scans before treatment using a 3-point scale (0 = no evidence of ILA, 1 = equivocal for ILA, 2 = ILA). Clinical characteristics and overall survival were compared in patients with ILA (score 2) and others. An ILA was present in 14 of 208 patients (7%) with esophageal cancer on pretreatment chest CT. Patients with ILA were significantly older (median age, 69 vs 65, respectively; P = 0.011), had a higher number of pack-years of smoking ( P = 0.02), and more commonly had T4 stage disease ( P = 0.026) than patients with ILA score of 1 or 0. Interstitial lung abnormality on baseline scan was associated with a lack of surgical resection after chemoradiotherapy (7/14, 50% vs 39/194, 20% respectively; P = 0.016). Interstitial lung abnormality was not associated with overall survival (log-rank P = 0.75, Cox P = 0.613). An ILA was present in 7% of esophageal cancer patients, which is similar to the prevalence in general population and in smokers. Interstitial lung abnormality was strongly associated with a lack of surgical resection after chemoradiotherapy, indicating an implication of ILA in treatment selection in these patients, which can be further studied in larger cohorts.

Lung Cancer Screening With Low-dose Chest Computed Tomography: Experience From Radon-contaminated Regions in Kazakhstan.

ObjectivesThe aim of this study was to present the baseline results of a pilot project conducted to evaluate the effectiveness of lung cancer screening using low-dose chest computed tomography (CT) in regions with excessive radon levels in the Republic of Kazakhstan.MethodsIn total, 3671 participants were screened by low-dose chest CT. Current, former, and never-smokers who resided in regions with elevated levels of radon in drinking water sources and indoor air, aged between 40 and 75 with no history of any cancer, and weighing less than 140 kg were included in the study. All lung nodules were categorized according to the American College of Radiology Lung Imaging Reporting and Data System (Lung-RADS 1.0).ResultsOverall, 614 (16.7%) participants had positive baseline CT findings (Lung-RADS categories 3 and 4). Seventy-four cancers were detected, yielding an overall cancer detection rate of 2.0%, with 10.8% (8/74) stage I and a predominance of stage III (59.4%; 44/74). Women never-smokers and men current smokers had the highest cancer detection rates, at 2.9% (12/412) and 6.1% (12/196), respectively. Compared to never-smokers, higher odds ratios (ORs) of lung cancer detection were found in smokers (OR,2.48; 95% confidence interval [CI], 1.52 to 4.05, p<0.001) and former smokers (OR, 2.32; 95% CI, 1.06 to 5.06, p=0.003). The most common histologic type of cancer was adenocarcinoma (58.1%).ConclusionsImplementation of low-dose CT screening for lung cancer in regions with elevated radon levels is an effective method for both smokers and never-smokers.

Prognostic findings for ICU admission in patients with COVID-19 pneumonia: baseline and follow-up chest CT and the added value of artificial intelligence.

Infection with SARS-CoV-2 has dominated discussion and caused global healthcare and economic crisis over the past 18 months. Coronavirus disease 19 (COVID-19) causes mild-to-moderate symptoms in most individuals. However, rapid deterioration to severe disease with or without acute respiratory distress syndrome (ARDS) can occur within 1–2 weeks from the onset of symptoms in a proportion of patients. Early identification by risk stratifying such patients who are at risk of severe complications of COVID-19 is of great clinical importance. Computed tomography (CT) is widely available and offers the potential for fast triage, robust, rapid, and minimally invasive diagnosis: Ground glass opacities (GGO), crazy-paving pattern (GGO with superimposed septal thickening), and consolidation are the most common chest CT findings in COVID pneumonia. There is growing interest in the prognostic value of baseline chest CT since an early risk stratification of patients with COVID-19 would allow for better resource allocation and could help improve outcomes. Recent studies have demonstrated the utility of baseline chest CT to predict intensive care unit (ICU) admission in patients with COVID-19. Furthermore, developments and progress integrating artificial intelligence (AI) with computer-aided design (CAD) software for diagnostic imaging allow for objective, unbiased, and rapid assessment of CT images.

Significance of too-small-to-characterize (TSTC) pulmonary nodules in known extrapulmonary solid tumors: A retrospective observational study

Background: Increased use of chest computed tomography (CT) scanning and greater awareness among general radiologists has led to a rise in lung nodule identification. Nodules less than 1 cm in diameter are considered small, and the morphology of such nodules is difficult to characterize.Objectives: The aim of our study was to determine the significance of pulmonary nodules that are too-small-to-characterize on CT scan in patients with extrapulmonary solid tumors and to determine the characteristics that help in distinguishing malignant from benign pulmonary nodules.Materials and Methods: We conducted a retrospective observational study in the Department of Radiodiagnosis of the Tata Memorial Hospital, Mumbai, India, and included patients with non-pulmonary solid malignancies who had obtained baseline CT/positron emission tomography (PET) scans between January 2010 and December 2014. Demographic information, site of the primary tumor, and histological diagnosis were recorded. Baseline and follow-up CT images were read by two radiologists. The significance of associated risk factors was analyzed using Pearson's Chi-square test. A multivariable logistic regression model was used.Results: Our study included 200 patients and 334 nodules, of which 148 (44.3%) nodules were found to be benign and 127 (38%) were malignant. The remaining 59 (17.7%) nodules in 32 patients were indeterminate. Most nodules located at a distance of less than 10 mm from the pleura were benign, whereas more than 2/3rd of the nodules more than 10 mm away from the pleura were malignant (n = 60/94, 63.8% P = 0.0001). The mean size of the 148 benign nodules (5.3 mm) was smaller than that of the 127 malignant nodules (9.2 mm). Nearly 93% of malignant nodules were solid (P = 0.0001). About 83% of malignant nodules were round, while 7 of 10 linear-shaped nodules were either benign or indeterminate (P = 0.006). Among the nodules associated with poorly differentiated primary neoplasms, 63.6% were malignant, 25% were benign, and 11.4% were indeterminate (P = 0.005).Conclusions: Too-small-to-characterize pulmonary nodules detected on CT are a common clinical problem and are more likely to be benign or indeterminate even in patients with known extrapulmonary neoplasms. Using nodule size, distance from the pleura, and morphological pointers, radiologists can help clinicians decide whether there is a need for biopsy or a short-interval follow-up. We have proposed a Pulmonary Metastases Cancer Imaging and Reporting Data System (PM-CI-RADS).

Adipose tissue biomarkers and type 2 diabetes incidence in normoglycemic participants in the MESArthritis Ancillary Study: A cohort study.

Given the central role of skeletal muscles in glucose homeostasis, deposition of adipose depots beneath the fascia of muscles (versus subcutaneous adipose tissue [SAT]) may precede insulin resistance and type 2 diabetes (T2D) incidence. This study was aimed to investigate the associations between computed tomography (CT)-derived biomarkers for adipose tissue and T2D incidence in normoglycemic adults. This study was a population-based multiethnic retrospective cohort of 1,744 participants in the Multi-Ethnic Study of Atherosclerosis (MESA) with normoglycemia (baseline fasting plasma glucose [FPG] less than 100 mg/dL) from 6 United States of America communities. Participants were followed from April 2010 and January 2012 to December 2017, for a median of 7 years. The intermuscular adipose tissue (IMAT) and SAT areas were measured in baseline chest CT exams and were corrected by height squared (SAT and IMAT indices) using a predefined measurement protocol. T2D incidence, as the main outcome, was based on follow-up FPG, review of hospital records, or self-reported physician diagnoses. Participants' mean age was 69 ± 9 years at baseline, and 977 (56.0%) were women. Over a median of 7 years, 103 (5.9%) participants were diagnosed with T2D, and 147 (8.4%) participants died. The IMAT index (hazard ratio [HR]: 1.27 [95% confidence interval [CI]: 1.15-1.41] per 1-standard deviation [SD] increment) and the SAT index (HR: 1.43 [95% CI: 1.16-1.77] per 1-SD increment) at baseline were associated with T2D incidence over the follow-up. The associations of the IMAT and SAT indices with T2D incidence were attenuated after adjustment for body mass index (BMI) and waist circumference, with HRs of 1.23 (95% CI: 1.09-1.38) and 1.29 (95% CI: 0.96-1.74) per 1-SD increment, respectively. The limitations of this study include unmeasured residual confounders and one-time measurement of adipose tissue biomarkers. In this study, we observed an association between IMAT at baseline and T2D incidence over the follow-up. This study suggests the potential role of intermuscular adipose depots in the pathophysiology of T2D. ClinicalTrials.gov NCT00005487.

Long-Term Imaging Follow-Up in DIPNECH: Multicenter Experience.

Diffuse pulmonary neuroendocrine cell hyperplasia (DIPNECH) is a rare pre-invasive disease whose pathophysiology remains unclear. We aimed to assess long-term evolution in imaging of DIPNECH, in order to propose follow-up recommendations. Patients with histologically confirmed DIPNECH from four centers, evaluated between 2001 and 2020, were enrolled if they had at least two available chest computed tomography (CT) exams performed at least 24 months apart. CT exams were analyzed for the presence and the evolution of DIPNECH-related CT findings. Twenty-seven patients, mostly of female gender (n = 25/27; 93%) were included. Longitudinal follow-up over a median 63-month duration (IQR: 31–80 months) demonstrated an increase in the size of lung nodules in 19 patients (19/27, 70%) and the occurrence of metastatic spread in three patients (3/27, 11%). The metastatic spread was limited to mediastinal lymph nodes in one patient, whereas the other two patients had both lymph node and distant metastases. The mean time interval between baseline CT scan and metastatic spread was 70 months (14, 74 and 123 months). Therefore, long-term annual imaging follow-up of DIPNECH might be appropriate to encompass the heterogeneous longitudinal behavior of this disease.

Computed Tomography-based Lung Residual Volume and Mortality of Patients With Coronavirus Disease-19 (COVID-19).

To assess the effect of computed tomography (CT)-based residual lung volume (RLV) on mortality of patients with coronavirus disease 2019 (COVID-19). A single-center, retrospective study of a prospectively maintained database was performed. In total, 138 patients with COVID-19 were enrolled. Baseline chest CT scan was performed in all patients. CT-based automated and semi-automated lung segmentation was performed using the Alma Medical workstation to calculate normal lung volume, lung opacities volume, total lung volume, and RLV. The primary end point of the study was mortality. Univariate and multivariate analyses were performed to determine independent predictors of mortality. Overall, 84 men (61%) and 54 women (39%) with a mean age of 47.3 years (±14.3 y) were included in the study. Overall mortality rate was 21% (29 patients) at a median time of 7 days (interquartile range, 4 to 11 d). Univariate analysis demonstrated that age, hypertension, and diabetes were associated with death (P<0.01). Similarly, patients who died had lower normal lung volume and RLV than patients who survived (P<0.01). Multivariate analysis demonstrated that low RLV was the only independent predictor of death (odds ratio, 1.042; 95% confidence interval, 10.2-10.65). Furthermore, receiver operating characteristic curve analysis demonstrated that a RLV ≤64% significantly increased the risk of death (odds ratio, 4.8; 95% confidence interval, 1.9-11.7). Overall mortality of patients with COVID-19 may reach 21%. Univariate and multivariate analyses demonstrated that reduced RLV was the principal independent predictor of death. Furthermore, RLV ≤64% is associated with a 4-fold increase on the risk of death.

Value of Follow-Up Chest Computed Tomography in the Surveillance of Patients with Hepatocellular Carcinoma.

PurposeTo evaluate the value of follow-up chest CT in the surveillance of HCC patients.BackgroundImaging guidelines for the surveillance of hepatocellular carcinoma (HCC) patients recommend multiple follow-up computed tomography (CT) examinations of the chest, abdomen, and pelvis. Imaging studies are a major driver of rising healthcare costs. The appropriate use of imaging studies must be evaluated to provide valued health care.MethodsWe reviewed the radiology reports of baseline and follow-up chest, abdominal, and pelvic CT examinations of HCC patients. We categorized the incidence of malignancy in the chest and abdomen for the baseline and follow-up examinations. We also categorized the follow-up examinations as showing improved disease, stable disease, or disease progression. We correlated any progression of disease in the chest with progression of disease in the abdomen. We determined the extent to which disease progression in the chest occurred alongside that in the abdomen. Descriptive statistical analysis was carried out using R (version 3.5.2, R Development Core Team).ResultsOf the 226 patients included in our study, only 7 (3%) had disease progression in the chest without corresponding disease progression in the abdomen and pelvis on follow-up CT. Only 1.8% of patients with disease progression in the chest had a negative CT chest at baseline.ConclusionFollow-up chest CT has limited benefit in the surveillance of HCC patients, especially those with negative baseline chest CT findings.

Correlation between Chest Computed Tomography Scan Findings and Mortality of COVID-19 Cases; a Cross sectional Study.

Predicting the outcomes of COVID-19 cases using different clinical, laboratory, and imaging parameters is one of the most interesting fields of research in this regard. This study aimed to evaluate the correlation between chest computed tomography (CT) scan findings and outcomes of COVID-19 cases. This cross sectional study was carried out on confirmed COVID-19 cases with clinical manifestations and chest CT scan findings based on Iran's National Guidelines for defining COVID-19. Baseline and chest CT scan characteristics of patients were investigated and their correlation with mortality was analyzed and reported using SPSS 21.0. 380 patients with the mean age of 53.62 ± 16.66 years were evaluated (66.1% male). The most frequent chest CT scan abnormalities were in peripheral (86.6%) and peribronchovascular interstitium (34.6%), with ground glass pattern (54.1%), and round (53.6%) or linear (46.7%) shape. There was a significant correlation between shape of abnormalities (p = 0.003), CT scan Severity Score (CTSS) (p <0.0001), and pulmonary artery CT diameter (p = 0. 01) with mortality. The mean CTSS of non-survived cases was significantly higher (13.68 ± 4.59 versus 8.72 ± 4.42; <0.0001). The area under the receiver operating characteristic (ROC) curve of CTSS in predicting the patients' mortality was 0.800 (95% CI: 0.716-0.884). The best cut off point of chest CTSS in this regard was 12 with 75.82% (95% CI: 56.07%-88.98%) sensitivity and 75.78% (95% CI: 70.88%-80.10%) specificity. The mean main pulmonary artery diameter in patients with CTSS ≥ 12 was higher than cases with CTSS < 12 (27.89 ± 3.73 vs 26.24 ± 3.14 mm; p < 0.0001). Based on the results of the present study it seems that there is a significant correlation between chest CT scan characteristics and mortality of COVID-19 cases. Patients with lower CTSS, lower pulmonary artery CT diameter, and round shape opacity had lower mortality.

Baseline Chest Computed Tomography as Standard of Care in High-Risk Hematology Patients.

Baseline chest computed tomography (BCT) in high-risk hematology patients allows for the early diagnosis of invasive pulmonary aspergillosis (IPA). The distribution of BCT implementation in hematology departments and impact on outcome is unknown. A web-based questionnaire was designed. International scientific bodies were invited. The estimated numbers of annually treated hematology patients, chest imaging timepoints and techniques, IPA rates, and follow-up imaging were assessed. In total, 142 physicians from 43 countries participated. The specialties included infectious diseases (n = 69; 49%), hematology (n = 68; 48%), and others (n = 41; 29%). BCT was performed in 57% (n = 54) of 92 hospitals. Upon the diagnosis of malignancy or admission, 48% and 24% performed BCT, respectively, and X-ray was performed in 48% and 69%, respectively. BCT was more often used in hematopoietic cell transplantation and in relapsed acute leukemia. European centers performed BCT in 59% and non-European centers in 53%. Median estimated IPA rate was 8% and did not differ between BCT (9%; IQR 5–15%) and non-BCT centers (7%; IQR 5–10%) (p = 0.69). Follow-up computed tomography (CT) for IPA was performed in 98% (n = 90) of centers. In high-risk hematology patients, baseline CT is becoming a standard-of-care. Chest X-ray, while inferior, is still widely used. Randomized, controlled trials are needed to investigate the impact of BCT on patient outcome.

Early markers of cystic fibrosis structural lung disease: follow-up of the ACFBAL cohort.

Little is known about early predictors of later cystic fibrosis (CF) structural lung disease. This study examined early predictors of progressive structural lung abnormalities in children who completed the Australasian CF Bronchoalveolar Lavage (ACFBAL) clinical trial at age 5-years and participated in an observational follow-up study (CF-FAB).Eight Australian and New Zealand CF centres participated in CF-FAB and provided follow-up chest computed-tomography (CT) scans for children who had completed the ACFBAL study with baseline scans at age 5-years. CT scans were annotated using PRAGMA-CF scoring. Ordinal regression analysis and linear regression were used to investigate associations between PRAGMA-CF (Perth-Rotterdam Annotated Grid Morphometric Analysis for CF) outcomes at follow-up and variables measured during the ACFBAL study.99 out of 157 ACFBAL children (mean±sd age 13±1.5 years) participated in the CF-FAB study. The probability of bronchiectasis at follow-up increased with airway disease severity on the baseline CT scan. In multiple regression (retaining factors at p<0.05) the extent of bronchiectasis at follow-up was associated with baseline atelectasis (OR 7.2, 95% CI 2.4-22; p≤ 0.001), bronchoalveolar lavage (BAL) log2 interleukin (IL)-8 (OR 1.2, 95% CI 1.05-1.5; p=0.010) and body mass index z-score (OR 0.49, 95% CI 0.24-1.00; p=0.05) at age 5 years. Percentage trapped air at follow-up was associated with BAL log2 IL-8 (coefficient 1.3, 95% CI 0.57-2.1; p<0.001) at age 5 years.The extent of airway disease, atelectasis, airway inflammation and poor nutritional status in early childhood are risk factors for progressive structural lung disease in adolescence.