Objectives To compare the chest computed tomography (CT) findings of coronavirus disease 2019 (COVID-19) to other non-COVID viral pneumonia. Methods MEDLINE, EMBASE, and Cochrane databases were searched through April 04, 2020, for published English language studies. Studies were eligible if they included immunocompetent patients with up to 14 days of viral pneumonia. Subjects had a respiratory tract sample test positive for COVID-19, adenovirus, influenza A, rhinovirus, parainfluenza, or respiratory syncytial virus. We only included observational studies and case series with more than ten patients. The pooled prevalence of each chest CT pattern or finding was calculated with 95% confidence intervals (95% CI).Results From 2263 studies identified, 33 were eligible for inclusion, with a total of 1911 patients (COVID-19, n = 934; non-COVID, n = 977). Frequent CT features for both COVID-19 and non-COVID viral pneumonia were a mixed pattern of groundglass opacity (GGO) and consolidation (COVID-

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Objective: To determine the CT findings of multiple cavitary lung lesions that allow the differentiation between benign and malignant etiologies. Methods: We reviewed CT scans, including patients with two or more cavitary lung lesions. We evaluated the number of cavitary lesions, their location, cavity wall thickness, and additional findings, correlating the variables with the diagnosis of a benign or malignant lesion. Results: We reviewed the chest CT scans of 102 patients, 58 (56.9%) of whom were male. The average age was 50.5 ± 18.0 years. Benign and malignant lesions were diagnosed in 74 (72.6%) and 28 (27.4%) of the patients, respectively. On the C T scans, the mean number of cavities was 3, the mean wall thickness of the largest lesions was 6.0 mm, and the mean diameter of the largest lesions was 27.0 mm. The lesions were predominantly in the upper lobes, especially on the right (in 43.1%). In our comparison of the variables studied, a diagnosis of malignancy was not found to correlate significantly with the wall thickness of the largest cavity, lymph node enlargement, emphysema, consolidation, bronchiectasis, or bronchial obstruction. The presence of centrilobular nodules correlated significantly with the absence of malignant disease (p < 0.05). In contrast, a greater number of cavities correlated significantly with malignancy (p < 0.026). Conclusions: A larger number of cavitary lung lesions and the absence of centrilobular nodules may be characteristic of a malignant etiology. However, on the basis of our evaluation of the lesions in our sample, we cannot state that wall thickness is a good indicator of a benign or malignant etiology.

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Quantitative imaging in lung cancer is a rapidly evolving modality in radiology that is changing clinical practice from a qualitative analysis of imaging features to a more dynamic, spatial, and phenotypical characterization of suspected lesions. Some quantitative parameters, such as the use of 18F-FDG PET/CT-derived standard uptake values (SUV), have already been incorporated into current practice as it provides important information for diagnosis, staging, and treatment response of patients with lung cancer. A growing body of evidence is emerging to support the use of quantitative parameters from other modalities. CT-derived volumetric assessment, CT and MRI lung perfusion scans, and diffusion-weighted MRI are some of the examples. Software-assisted technologies are the future of quantitative analyses in order to decrease intra-and inter-observer variability. In the era of "big data", widespread incorporation of radiomics (extracting quantitative information from medical images by converting them into minable high-dimensional data) will allow medical imaging to surpass its current status quo and provide more accurate histological correlations and prognostic value in lung cancer. This is a comprehensive review of some of the quantitative image methods and computer-aided systems to the diagnosis and followup of patients with lung cancer.

This study was conducted to evaluate the presence of air trapping in patients with idiopathic pulmonary fibrosis (IPF) and other interstitial lung diseases (ILDs) (non-IPF), showing the radiological pattern of usual interstitial pneumonia (UIP). Retrospectively, we included 69 consecutive patients showing the typical UIP pattern on computed tomography (CT), and 15 final diagnosis of IPF with CT pattern “inconsistent with UIP” due to extensive air trapping. Air trapping at CT was assessed qualitatively by visual analysis and quantitatively by automated-software. In the quantitative analysis, significant air trapping was defined as >6% of voxels with attenuation between −950 to −856 HU on expiratory CT (expiratory air trapping index [ATIexp]) or an expiratory to inspiratory (E/I) ratio of mean lung density >0.87. The sample comprised 51 (60.7%) cases of IPF and 33 (39.3%) cases of non-IPF ILD. Most patients did not have air trapping (E/I ratio ≤0.87, n = 53, [63.1%]; ATIexp ≤6%, n = 45, [53.6%]). Air trapping in the upper lobes was the only variable distinguishing IPF from non-IPF ILD (prevalence, 3.9% vs 33.3%, p < 0.001). In conclusion, air trapping is common in patients with ILDs showing a UIP pattern on CT, as determined by qualitative and quantitative evaluation, and should not be considered to be inconsistent with UIP. On subjective visual assessment, air trapping in the upper lobes was associated with a non-IPF diagnoses.

ObjectiveTo evaluate the quantitative computed tomography (QCT) phenotypes, airflow limitations, and exacerbation-like episodes in heavy smokers in Southern Brazil.MethodsWe enrolled 172 smokers with a smoking history ≥30 pack-years who underwent pulmonary function tests (PFTs) and CT scan for lung cancer screening. Patients were classified regarding airflow limitation (FEV1/FVC <0.7 forced expiratory volume in 1 second/forced vital capacity) and the presence of emphysema on the QCT. The QCT were analyzed in specialized software and patients were classified in two disease-predominant phenotypes: emphysema-predominant (EP) and non-emphysema-predominant (NEP). EP was determined as ≥6% of percent low-attenuation areas (LAA%) with less than -950 Hounsfield units. NEP was defined as having a total LAA% of less than 6%.ResultsMost of our patients were classified in the EP phenotype. The EP group had significantly worse predicted FEV1 (60.6 ±22.9 vs. 89.7 ±15.9, p <0.001), higher rates of airflow limitation (85.7% vs. 15%; p <0.001), and had more exacerbation-like episodes (25.8% vs. 8.3%, p <0.001) compared to the NEP group. Smoking history, ethnicity, and BMI did not differ between the groups. The total LAA% was the QCT parameter with the strongest correlation to FEV1 (r = -0.669) and FEV1/FVC (r = -0.787).ConclusionsHeavy smokers with the EP phenotype on QCT were more likely to have airflow limitation, worse predicted FEV1, and a higher rate of exacerbation-like episodes than those with the NEP phenotype. Approximately 23% of patients with no airflow limitation on PFTs were classified in EP phenotype.

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