Chest CT Imaging Provides More Information Than Chest Radiography Alone in Determining Extent of Physiologic Impairment in Pulmonary Sarcoidosis.

BACKGROUND: Sarcoidosis staging primarily has relied on the Scadding chest radiographic system, although chest CT imaging is finding increased clinical use. RESEARCH QUESTION: Whether standardized chest CT scan assessment provides additional understanding of lung function beyond Scadding stage and demographics is unknown and the focus of this study. STUDY DESIGN AND METHODS: We used the National Heart, Lung, and Blood Institute study Genomics Research in Alpha-1 Antitrypsin Deficiency and Sarcoidosis cases of sarcoidosis (n = 351) with Scadding stage and chest CT scans obtained in a standardized manner. One chest radiologist scored all CT scans with a visual scoring system, with a subset read by another chest radiologist. We compared demographic features, Scadding stage, and CT scan findings and the correlation between these measures. Associations between spirometry results and Dlco, CT scan findings, and Scadding stage were determined using regression analysis (n = 318). Agreement between readers was evaluated using Cohen's κ value. RESULTS: CT scan features were inconsistent with Scadding stage in approximately 40% of cases. Most CT scan features assessed on visual scoring were associated negatively with lung function. Associations persisted for FEV1 and Dlco when adjusting for Scadding stage, although some CT scan feature associations with FVC became insignificant. Scadding stage was associated primarily with FEV1, and inclusion of CT scan features reduced significance in association between Scadding stage and lung function. Multivariable regression modeling to identify radiologic measures explaining lung function included Scadding stage for FEV1 and FEV1 to FVC ratio (P < .05) and marginally for Dlco (P < .15). Combinations of CT scan measures accounted for Scadding stage for FVC. Correlations among Scadding stage and CT scan features were noted. Agreement between readers was poor to moderate for presence or absence of CT scan features and poor for degree and location of abnormality. INTERPRETATION: CT scan features explained additional variability in lung function beyond Scadding stage, with some CT scan features obviating the associations between lung function and Scadding stage. Whether CT scan features, phenotypes, or endotypes could be useful for managing patients with sarcoidosis needs more study.

Radiomic phenotyping of the lung parenchyma in a lung cancer screening cohort.

High-throughput extraction of radiomic features from low-dose CT scans can characterize the heterogeneity of the lung parenchyma and potentially aid in identifying subpopulations that may have higher risk of lung diseases, such as COPD, and lung cancer due to inflammation or obstruction of the airways. We aim to determine the feasibility of a lung radiomics phenotyping approach in a lung cancer screening cohort, while quantifying the effect of different CT reconstruction algorithms on phenotype robustness. We identified low-dose CT scans (n = 308) acquired with Siemens Healthineers scanners from patients who completed low-dose CT within our lung cancer screening program between 2015 and 2018 and had two different sets of image reconstructions kernel available (i.e., medium (I30f.), sharp (I50f.)) for the same acquisition. Following segmentation of the lung field, a total of 26 radiomic features were extracted from the entire 3D lung-field using a previously validated fully-automated lattice-based software pipeline, adapted for low-dose CT scans. The lattice in-house software was used to extract features including gray-level histogram, co-occurrence, and run-length descriptors. The lattice approach uses non-overlapping windows for traversing along pixels of images and calculates different features. Each feature was averaged for each scan within a range of lattice window sizes (W) of 4, 8 and 20 mm. The extracted imaging features from both datasets were harmonized to correct for differences in image acquisition parameters. Subsequently, unsupervised hierarchical clustering was applied on the extracted features to identify distinct phenotypic patterns of the lung parenchyma, where consensus clustering was used to identify the optimal number of clusters (K = 2). Differences between phenotypes for demographic and clinical covariates including sex, age, BMI, pack-years of smoking, Lung-RADS and cancer diagnosis were assessed for each phenotype cluster, and then compared across clusters for the two different CT reconstruction algorithms using the cluster entanglement metric, where a lower entanglement coefficient corresponds to good cluster alignment. Furthermore, an independent set of low-dose CT scans (n = 88) from patients with available pulmonary function data on lung obstruction were analyzed using the identified optimal clusters to assess associations to lung obstruction and validate the lung phenotyping paradigm. Heatmaps generated by radiomic features identified two distinct lung parenchymal phenotype patterns across different feature extraction window sizes, for both reconstruction algorithms (P < 0.05 with K = 2). Associations of radiomic-based clusters with clinical covariates showed significant differences for BMI and pack-years of smoking (P < 0.05) for both reconstruction kernels. Radiomic phenotype patterns were more similar across the two reconstructed kernels, when smaller window sizes (W = 4 and 8 mm) were used for radiomic feature extraction, as deemed by their entanglement coefficient. Validation of clustering approaches using cluster mapping for the independent sample with lung obstruction also showed two statistically significant phenotypes (P < 0.05) with significant difference for BMI and smoking pack-years. Radiomic analysis can be used to characterize lung parenchymal phenotypes from low-dose CT scans, which appear reproducible for different reconstruction kernels. Further work should seek to evaluate the effect of additional CT acquisition parameters and validate these phenotypes in characterizing lung cancer screening populations, to potentially better stratify disease patterns and cancer risk.

Photon-counting detector CT allows significant reduction in radiation dose while maintaining image quality and noise on non-contrast chest CT.

Purpose: To investigate if clinical non-contrast chest CT studies obtained with PCD CT using much lower radiation exposure can achieve the same image quality as with the currently established EID protocol. Materials/methods: A total of seventy-one patients were identified who had a non-contrast chest computed tomography (CT) done on PCD CT and EID CT scanners within a 4-month interval. Five fellowship trained chest radiologists, blinded to the scanner details were asked to review the cases side-by-side and record their preference for images from either the photon-counting-detector (PCD) CT or the energy-integrating detector (EID) CT scanner. Results: The median CTDIvol for PCD-CT system was 4.710 mGy and EID system was 7.80 mGy (p < 0.001). The median DLP with the PCD-CT was 182.0 mGy.cm and EID system was 262.60 mGy.cm (p < 0.001). The contrast to noise ratio (CNR) was superior on the PCD-CT system 59.2 compared to the EID-CT 53.3; (p < 0.001). Kappa-statistic showed that there was poor agreement between the readers over the image quality from the PCD and EID scanners (κ = 0.19; 95 % CI: 0.12 - 0.27; p < 0.001). Chi-square analysis revealed that 3 out of 5 readers showed a significant preference for images from the PCDCT (p ≤ 0.012). There was no significant difference in the preferences of two readers between EID-CT and PCD-CT images. Conclusion: The first clinical PCD-CT system allows a significant reduction in radiation exposure while maintaining image quality and image noise using a standardized non-contrast chest CT protocol.

The Role of Imaging in the Management of Suspected or Known COVID-19 Pneumonia: A Multidisciplinary Perspective.

COVID-19 is an illness caused by a novel coronavirus that has rapidly escalated into a global pandemic leading to an urgent medical effort to better characterize this disease biologically, clinically and by imaging. In this review, we present the current approach to imaging of COVID-19 pneumonia. We focus on the appropriate utilization of thoracic imaging modalities to guide clinical management. We will also describe radiologic findings that are considered typical, atypical and generally not compatible with of COVID-19 infection. Further, we review imaging examples of COVID-19 imaging mimics, such as organizing pneumonia, eosinophilic pneumonia and other viral infections.

The Role of Imaging in the Management of Suspected or Known COVID-19 Pneumonia. A Multidisciplinary Perspective.

Coronavirus disease (COVID-19) is an illness caused by a novel coronavirus that has rapidly escalated into a global pandemic leading to an urgent medical effort to better characterize this disease biologically, clinically, and by imaging. In this review, we present the current approach to imaging of COVID-19 pneumonia. We focus on the appropriate use of thoracic imaging modalities to guide clinical management. We also describe radiologic findings that are considered typical, atypical, and generally not compatible with COVID-19. Furthermore, we review imaging examples of COVID-19 imaging mimics, such as organizing pneumonia, eosinophilic pneumonia, and other viral infections.

Computational analysis of thoracic multidetector row HRCT for segmentation and quantification of small airway air trapping and emphysema in obstructive pulmonary disease.

RATIONALE AND OBJECTIVES: Obstructive pulmonary disease phenotypes are related to variable combinations of emphysema and small-airway disease, the latter manifested as air trapping (AT) on imaging. The investigators propose a method to extract AT information quantitatively from thoracic multi-detector row high-resolution computed tomography (HRCT), validated by pulmonary function testing (PFT) correlation. MATERIALS AND METHODS: Seventeen patients with obstructive pulmonary disease who underwent HRCT and PFT within a 3-day interval were retrospectively identified. Thin-section volumetric HRCT in inspiration and expiration was registered and analyzed using custom-made software. Nonaerated regions of lung were segmented through exclusion of voxels > -50 Hounsfield units (HU); emphysematous areas were segmented as voxels < -950 HU on inspiratory images. Small-airway AT volume (ATV) was segmented as regions of lung voxels whose attenuation values increased by less than a specified change threshold (set from 5 to 300 HU in 25-HU increments) between inspiration and expiration. Inspiratory and expiratory total segmented lung volumes, emphysema volume (EV), and ATV for each threshold were subsequently calculated and correlated with PFT parameters. RESULTS: A strong positive correlation was obtained between total segmented lung volume in inspiration and total lung capacity (r = 0.83). A strong negative correlation (r = -0.80) was obtained between EV and the ratio between forced expiratory volume in 1 second and forced vital capacity. Stronger negative correlation with forced expiratory volume in 1 second/forced vital capacity (r = -0.85) was demonstrated when ATV (threshold, 50 HU) was added to EV, indicating improved quantification of total AT to predict obstructive disease severity. A moderately strong positive correlation between ATV and residual volume was observed, with a maximum r value of 0.72 (threshold, 25 HU), greater than that between EV and residual volume (r = 0.58). The benefit of ATV quantification was greater in a subgroup of patients with negligible emphysema compared to patients with moderate to severe emphysema. CONCLUSIONS: Small-airway AT segmentation in conjunction with emphysema segmentation through computer-assisted methodologies may provide better correlations with key PFT parameters, suggesting that the quantification of emphysema-related and small airway-related components of AT from thoracic HRCT has great potential to elucidate phenotypic differences in patients with chronic obstructive pulmonary disease.

Is modulation of nicotinic acetylcholine receptors by melatonin relevant for therapy with cholinergic drugs?

Melatonin, the darkness hormone, synchronizes several physiological functions to light/dark cycle. Besides the awake/sleep cycle that is intuitively linked to day/night, daily variations in memory acquisition and innate or acquired immune responses are some of the major activities linked to melatonin rhythm. The daily variation of these complex processes is due to changes in specific mechanisms. In the last years we focused on the influence of melatonin on the expression and function of nicotinic acetylcholine receptors (nAChRs). Melatonin, either "in vivo" or "in vitro", increases, in a selective manner, the efficiency of alpha-bungarotoxin (alpha-BTX)-sensitive nAChRs. Melatonin's effect on receptors located in rat sympathetic nerve terminals, cerebellum, skeletal muscle and chick retina, was tested. We observed that melatonin is essential for the development of alpha-BTX-sensitive nAChRs, and important for receptor maintenance in aging models. Taking into account that both melatonin and alpha-7 nAChRs (one of the subtypes sensitive to alpha-BTX) are involved in the development of Alzheimer's disease, here we discuss the possibility of a therapeutic strategy focused on both melatonin replacement and its potential association with cholinergic drugs.