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Although the original data on systemic oxidative stress in COVID-19 patients have recently started to emerge, we are still far from a complete profile of changes in patients’ redox homeostasis. We aimed to assess the extent of oxidative damage of proteins, lipids and DNA during the course of acute disease, as well as their association with CT pulmonary patterns. In order to obtain more insight into the origin of the systemic oxidative stress, the observed parameters were correlated with inflammatory biomarkers and biomarkers of multiorgan impairment. In this prospective study, we included 58 patients admitted between July and October 2020 with COVID-19 pneumonia. Significant changes in malondialdehyde, 8-hydroxy-2’-deoxyguanosine and advanced oxidation protein products levels exist during the course of COVID-19. Special emphasis should be placed on the fact that the pattern of changes differs between non-hospitalized and hospitalized individuals. Our results point to the time-dependent relation of oxidative stress parameters with inflammatory and multiorgan impairment biomarkers, as well as pulmonary patterns in COVID-19 pneumonia patients. Correlation between redox biomarkers and immunological or multiorgan impairment biomarkers, as well as pulmonary CT pattern, confirms the suggested involvement of neutrophils networks, IL-6 production, along with different organ/tissue involvement in systemic oxidative stress in COVID-19.

RationaleSevere pulmonary hypertension (PH) is very uncommon in COPD, and a distinct phenotype has been hypothesised. We aimed to evaluate whether CT can help to recognise this condition non-invasively by measuring small pulmonary vessels.Material and methodsPatients with COPD who underwent pulmonary function tests, unenhanced CT of the chest and right heart catheterisation (RHC) during a period of stability were included in the study. From 105 included patients, 20 patients with COPD with severe PH (mean pulmonary arterial pressure, mPAP>35 mm Hg) were compared with 20 FEV1-matched and age-matched patients with COPD with mild or without PH (mPAP<35 mm Hg). The percentage of total cross-sectional area of vessels less than 5 mm2 normalised by lung area (%CSA<5) and 5–10 mm2 (%CSA5–10), the mean number of cross-sectioned vessels (CSNs) and bronchial wall thickness (WT) were measured on CT examination and compared between groups. Paw scores combining PaO2 measurement and CT parameters best correlated with mPAP were compared by receiver operating characteristic analysis to predict severe PH in COPD.ResultsPatients with severe PH COPD had higher %CSA and CSN values than those of patients with COPD without severe PH. Using multiple regression analysis, %CSA<5 and WT were the best predictors of mPAP in patients with and without severe PH, respectively. A score combining %CSA<5, PaO2 and WT best predicted severe PH in patients with COPD.ConclusionsCT measurements of small vessels support a distinct vessel-related phenotype in patients with COPD with severe PH, and combined with WT and PaO2 parameters in the paw score, which may offer a non-invasive tool to select patients for RHC.

ObjectivesTo establish the level of observer variation for the current ATS/ERS/JRS/ALAT criteria for a diagnosis of usual interstitial pneumonia (UIP) on CT among a large group of thoracic radiologists of varying levels of experience.Materials and methods112 observers (96 of whom were thoracic radiologists) categorised CTs of 150 consecutive patients with fibrotic lung disease using the ATS/ERS/JRS/ALAT CT criteria for a UIP pattern (3 categories—UIP, possibly UIP and inconsistent with UIP). The presence of honeycombing, traction bronchiectasis and emphysema was also scored using a 3-point scale (definitely present, possibly present, absent). Observer agreement for the UIP categorisation and for the 3 CT patterns in the entire observer group and in subgroups stratified by observer experience, were evaluated.ResultsInterobserver agreement across the diagnosis category scores among the 112 observers was moderate, ranging from 0.48 (IQR 0.18) for general radiologists to 0.52 (IQR 0.20) for thoracic radiologists of 10–20 years’ experience. A binary score for UIP versus possible or inconsistent with UIP was examined. Observer agreement for this binary score was only moderate. No significant differences in agreement levels were identified when the CTs were stratified according to multidisciplinary team (MDT) diagnosis or patient age or when observers were categorised according to experience. Observer agreement for each of honeycombing, traction bronchiectasis and emphysema were 0.59±0.12, 0.42±0.15 and 0.43±0.18, respectively.ConclusionsInterobserver agreement for the current ATS/ERS/JRS/ALAT CT criteria for UIP is only moderate among thoracic radiologists, irrespective of their experience, and did not vary with patient age or the MDT diagnosis.

Quantitative 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). Patients 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. Seventy-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. This 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.

Background The 2011 idiopathic pulmonary fibrosis (IPF) guidelines are based on the diagnosis of IPF using only high-resolution computed tomography (HRCT). However, few studies have thus far reviewed the usefulness of the HRCT scoring system based on the grading scale provided in the guidelines. We retrospectively studied 98 patients with respect to assess the prognostic value of changes in HRCT findings using a new HRCT scoring system based on the grading scale published in the guidelines. Methods Consecutive patients with IPF who were diagnosed using HRCT alone between January 2008 and January 2012 were evaluated. HRCT examinations and pulmonary function tests were performed at six-month intervals for the first year after diagnosis. The HRCT findings were evaluated using the new HRCT scoring system (HRCT fibrosis score) over time. The findings and survival rates were analyzed using a Kaplan-Meier analysis. Results The HRCT fibrosis scores at six and 12 months after diagnosis were significantly increased compared to those observed at the initial diagnosis (p < 0.001). The patients with an elevated HRCT fibrosis score at six months based on a receiver operating characteristic (ROC) curves analysis had a poor prognosis (log-rank, hazard ratio [HR] 2.435, 95% CI 1.196-4.962; p = 0.0142). Furthermore, among the patients without marked changes in %FVC, those with an elevated score above the cut-off value had a poor prognosis (HR 2.192, 95% CI 1.003-4.791; p = 0.0491). Conclusions Our data demonstrate that the HRCT scoring system based on the grading scale is useful for predicting the clinical outcomes of IPF and identifying patients with an adverse prognosis when used in combination with spirometry.

Sarcoidosis is a chronic, idiopathic, multisystemic inflammatory disease characterized by non-caseating granulomas, most commonly affecting the lungs and mediastinal lymph nodes. Radiological imaging plays a fundamental role in the diagnosis, assessment of disease extent, and differentiation from other pulmonary conditions. This narrative review offers a comprehensive overview of the imaging features of pulmonary sarcoidosis, focusing on both typical patterns—such as bilateral hilar lymphadenopathy, perilymphatic nodules, and upper lobe-predominant infiltrates—and atypical manifestations—including alveolar opacities, miliary nodules, fibrocystic changes, and lower lobe involvement. Emphasis is placed on the utility of high-resolution computed tomography (HRCT) in detecting early parenchymal changes and complications such as fibrosis, bronchiectasis, and pulmonary hypertension. Differential diagnosis, including tuberculosis, silicosis, metastatic disease, organizing pneumonia, and hypersensitivity pneumonitis, are discussed to aid interpretation. Recognizing the spectrum of radiological presentations is essential for distinguishing sarcoidosis from other interstitial and granulomatous lung diseases. Radiologists play a pivotal role in the multidisciplinary diagnostic process, contributing to timely diagnosis, risk stratification, and optimized patient management.

Little is known about in vivo alterations at bronchial and vascular levels in severe pulmonary hypertension (PH) of different etiologies. We aimed to compare quantitative computed tomography (CT) data from the following three groups of severe precapillary PH patients: COPD, idiopathic pulmonary arterial hypertension (iPAH), and chronic thromboembolic PH (CTEPH). This study was approved by the institutional review board. Severe PH patients (mean pulmonary arterial pressure [mPAP] ≥35 mmHg) with COPD, iPAH, or CTEPH (n=24, 16, or 16, respectively) were included retrospectively between January 2008 and January 2017. Univariate analysis of mPAP was performed in each severe PH group. Bronchial wall thickness (WT) and percentage of cross sectional area of pulmonary vessels less than 5 mm normalized by lung area (%CSA ) were measured and compared using CT, and then combined to arterial partial pressure of oxygen (PaO ) to generate a \"paw score\" compared within the three groups using Kruskal-Wallis and its sensitivity using Fisher's exact test. WT was higher and %CSA was lower in the COPD group compared to iPAH and CTEPH groups. Mosaic pattern was higher in CTEPH group than in others. In severe PH patients secondary to COPD, mPAP was positively correlated to %CSA . By contrast, in severe iPAH, this correlation was negative, or not correlated in severe CTEPH groups. In the COPD group, \"paw score\" showed higher sensitivity than in the other two groups. Unlike in severe iPAH and CTEPH, severe PH with COPD can be predicted by \"paw score\" reflecting bronchial and vascular morphological differential alterations.

ObjectiveTo investigate the natural history of persistent pulmonary pure ground-glass nodules (pGGNs) with deep learning–assisted nodule segmentation.MethodsBetween January 2007 and October 2018, 110 pGGNs from 110 patients with 573 follow-up CT scans were included in this retrospective study. pGGN automatic segmentation was performed on initial and all follow-up CT scans using the Dr. Wise system based on convolution neural networks. Subsequently, pGGN diameter, density, volume, mass, volume doubling time (VDT), and mass doubling time (MDT) were calculated automatically. Enrolled pGGNs were categorized into growth, 52 (47.3%), and non-growth, 58 (52.7%), groups according to volume growth. Kaplan-Meier analyses with the log-rank test and Cox proportional hazards regression analysis were conducted to analyze the cumulative percentages of pGGN growth and identify risk factors for growth.ResultsThe mean follow-up period of the enrolled pGGNs was 48.7 ± 23.8 months. The median VDT of the 52 pGGNs having grown was 1448 (range, 339–8640) days, and their median MDT was 1332 (range, 290–38,912) days. The 12-month, 24.7-month, and 60.8-month cumulative percentages of pGGN growth were 10%, 25.5%, and 51.1%, respectively, and they significantly differed among the initial diameter, volume, and mass subgroups (all p < 0.001). The growth pattern of pGGNs may conform to the exponential model. Lobulated sign (p = 0.044), initial mean diameter (p < 0.001), volume (p = 0.003), and mass (p = 0.023) predicted pGGN growth.ConclusionsPersistent pGGNs showed an indolent course. Deep learning can assist in accurately elucidating the natural history of pGGNs. pGGNs with lobulated sign and larger initial diameter, volume, and mass are more likely to grow.Key Points• The pure ground-glass nodule (pGGN) segmentation accuracy of the Dr. Wise system based on convolution neural networks (CNNs) was 96.5% (573/594).• The median volume doubling time (VDT) of 52 pure ground-glass nodules (pGGNs) having grown was 1448 days (range, 339–8640 days), and their median mass doubling time (MDT) was 1332 days (range, 290–38,912 days). The mean time to growth in volume was 854 ± 675 days (range, 116–2856 days).• The 12-month, 24.7-month, and 60.8-month cumulative percentages of pGGN growth were 10%, 25.5%, and 51.1%, respectively, and they significantly differed among the initial diameter, volume, and mass subgroups (all p values < 0.001). The growth pattern of pure ground-glass nodules may conform to exponential model.

Background Dyspnea is one of the most common symptoms in the post-acute phase of COVID-19 pneumonia. Conventional pulmonary function tests and computed tomography (CT) scores often fail to show correlation with symptom severity, highlighting the need for more sensitive imaging biomarkers. Machine-learning–based quantitative CT analysis and parametric response mapping (PRM) can capture subtle structural and functional abnormalities that may be associated with persistent dyspnea. Methods We analyzed inspiratory and paired inspiratory–expiratory CT scans of early (3–6 months) post-COVID-19 pneumonia patients. Inspiratory CT images were segmented using a random forest algorithm to quantify lung parenchymal patterns. Paired inspiratory/expiratory scans were co-registered to derive ventilation metrics and PRM-defined functional small airway disease (fSAD), emphysema, emptying emphysema, and normal lung. Associations between imaging metrics and patient-reported dyspnea assessed by a visual analogue scale (VAS) were evaluated using univariable and multivariable linear regression, with adjustment for age, sex, BMI, and smoking history. Results One hundred twenty-three patients had usable inspiratory CT scans, and 116 patients had paired inspiratory/expiratory scans of sufficient quality for analysis. In the adjusted multivariable models, greater PRM-defined functional small airway disease (fSAD) was positively associated with dyspnea (standardized β = 1.21, p  = 0.002). Moreover, a lower standard deviation of dense ground-glass attenuation in the left lung (standardized β = −0.82, p  = 0.033) and greater total volume of dense ground-glass opacities (standardized β = 0.71, p  = 0.033) were independently associated with dyspnea. Conclusions In early post-COVID-19 pneumonia, machine-learning–based CT pattern recognition and PRM revealed that functional small airway disease, and the total volume and heterogeneity of lung dense ground-glass opacities are significantly associated with persistent dyspnea. These findings highlight the potential of quantitative CT to identify pulmonary imaging biomarkers relevant to long COVID symptom burden. Trial registration ClinicalTrials.gov (NCT04406324).

Background Lung cancer is a leading cause of death worldwide; it refers to the uncontrolled growth of abnormal cells in the lung. A computed tomography (CT) scan of the thorax is the most sensitive method for detecting cancerous lung nodules. A lung nodule is a round lesion which can be either non-cancerous or cancerous. In the CT, the lung cancer is observed as round white shadow nodules. The possibility to obtain a manually accurate interpretation from CT scans demands a big effort by the radiologist and might be a fatiguing process. Therefore, the design of a computer-aided diagnosis (CADx) system would be helpful as a second opinion tool. Methods The stages of the proposed CADx are: a supervised extraction of the region of interest to eliminate the shape differences among CT images. The Daubechies db1, db2, and db4 wavelet transforms are computed with one and two levels of decomposition. After that, 19 features are computed from each wavelet sub-band. Then, the sub-band and attribute selection is performed. As a result, 11 features are selected and combined in pairs as inputs to the support vector machine (SVM), which is used to distinguish CT images containing cancerous nodules from those not containing nodules. Results The clinical data set used for experiments consists of 45 CT scans from ELCAP and LIDC. For the training stage 61 CT images were used (36 with cancerous lung nodules and 25 without lung nodules). The system performance was tested with 45 CT scans (23 CT scans with lung nodules and 22 without nodules), different from that used for training. The results obtained show that the methodology successfully classifies cancerous nodules with a diameter from 2 mm to 30 mm. The total preciseness obtained was 82%; the sensitivity was 90.90%, whereas the specificity was 73.91%. Conclusions The CADx system presented is competitive with other literature systems in terms of sensitivity. The system reduces the complexity of classification by not performing the typical segmentation stage of most CADx systems. Additionally, the novelty of the algorithm is the use of a wavelet feature descriptor.