Imaging the posterior mediastinum: A multimodality approach

The posterior mediastinum contains several structures that can produce a wide variety of pathologic conditions. Descending thoracic aorta, esophagus, azygos and hemiazygos veins, thoracic duct, lymph nodes, adipose tissue, and nerves are all located in this anatomical region and can produce diverse abnormalities. Although chest radiography may detect many of these pathologic conditions, computed tomography and magnetic resonance are the imaging modalities of choice for further defining the relationship of posterior mediastinal lesions to neighboring structures and showing specific imaging features that narrow the differential diagnosis. This review emphasizes modality-related answers to morphologic questions, which provide precise diagnostic information

Assessing invasiveness of subsolid lung adenocarcinomas with combined attenuation and geometric feature models

The aim of this study was to develop and test multiclass predictive models for assessing the invasiveness of individual lung adenocarcinomas presenting as subsolid nodules on computed tomography (CT). 227 lung adenocarcinomas were included: 31 atypical adenomatous hyperplasia and adenocarcinomas in situ (class H1), 64 minimally invasive adenocarcinomas (class H2) and 132 invasive adenocarcinomas (class H3). Nodules were segmented, and geometric and CT attenuation features including functional principal component analysis features (FPC1 and FPC2) were extracted. After a feature selection step, two predictive models were built with ordinal regression: Model 1 based on volume (log) (logarithm of the nodule volume) and FPC1, and Model 2 based on volume (log) and Q.875 (CT attenuation value at the 87.5% percentile). Using the 200-repeats Monte-Carlo cross-validation method, these models provided a multiclass classification of invasiveness with discriminative power AUCs of 0.83 to 0.87 and predicted the class probabilities with less than a 10% average error. The predictive modelling approach adopted in this paper provides a detailed insight on how the value of the main predictors contribute to the probability of nodule invasiveness and underlines the role of nodule CT attenuation features in the nodule invasiveness classification

Ultrashort echo time MRI of pulmonary water content: assessment in a sponge phantom at 1.5 and 3.0 Tesla

PURPOSEWe aimed to develop a predictive model for lung water content using ultrashort echo time (UTE) magnetic resonance imaging (MRI) and a sponge phantom. MATERIALS AND METHODSImage quality was preliminarily optimized, and the signal-to-noise ratio (SNR) of UTE was compared with that obtained from a three-dimensional fast gradient echo (FGRE) sequence. Four predetermined volumes of water (3.5, 3.0, 2.5, and 2.0 mL) were soaked in cellulose foam sponges 1.8 cm3 in size and were imaged with UTE-MRI at 1.5 and 3.0 Tesla (T). A multiple echo time experiment (range, 0.1–9.6 ms) was conducted, and the T2 signal decay curve was determined at each volume of water. A three-parameter equation was fitted to the measured signal, allowing for the calculation of proton density and T2*. The calculation error of proton density was determined as a function of echo time. The constants that allowed for the determination of unknown volumes of water from the measured proton density were calculated using linear regression. RESULTSUTE-MRI provided excellent image quality for the four phantoms and showed a higher SNR, compared to that of FGRE. Proton density decreased proportionally with the decreases in both lung water and field strength (from 3.5 to 2.0 mL; proton density range at 1.5 T, 30.5–17.3; at 3.0 T, 84.2–41.5). Minimum echo time less than 0.6 ms at 1.5 T and 1 ms at 3.0 T maintained calculation errors for proton density within the range of 0%–10%. The slopes of the lines for determining the unknown volumes of water with UTE-MRI were 0.12±0.003 at 1.5 T and 0.05±0.002 at 3.0 T (P < 0.0001). CONCLUSIONIn a sponge phantom imaged at 1.5 and 3.0 T, unknown volumes of water can be predicted with high accuracy using UTE-MRI

Quantitative and O2 Enhanced MRI of the Pathologic Lung: Findings in Emphysema, Fibrosis, and Cystic Fibrosis

Purpose: beyond the pure morphological visual representation, MR imaging offers the possibility to quantify parameters in the healthy, as well as, in pathologic lung parenchyma. Gas exchange is the primary function of the lung and the transport of oxygen plays a key role in pulmonary physiology and pathophysiology. The purpose of this review is to present a short overview of the relaxation mechanisms of the lung and the current technical concepts of T1 mapping and methods of oxygen enhanced MR imaging. Material and Methods: molecular oxygen has weak paramagnetic properties so that an increase in oxygen concentration results in shortening of the T1 relaxation time and thus to an increase of the signal intensity in T1 weighted images. A possible way to gain deeper insights into the relaxation mechanisms of the lung is the calculation of parameter Maps. T1 Maps based on a snapshot FLASH sequence obtained during the inhalation of various oxygen concentrations provide data for the creation of the so-called oxygen transfer function (OTF), assigning a measurement for local oxygen transfer. T1 weighted single shot TSE sequences also permit expression of the signal changing effects associated with the inhalation of pure oxygen. Results: the average of the mean T1 values over the entire lung in inspiration amounts to 1199 +/− 117 milliseconds, the average of the mean T1 values in expiration was 1333 +/− 167 milliseconds. T1 Maps of patients with emphysema and lung fibrosis show fundamentally different behavior patterns. Oxygen enhanced MRT is able to demonstrate reduced diffusion capacity and diminished oxygen transport in patients with emphysema and cystic fibrosis. Discussion: results published in literature indicate that T1 mapping and oxygen enhanced MR imaging are promising new methods in functional imaging of the lung and when evaluated in conjunction with the pure morphological images can provide additional valuable information

Software-based risk stratification of pulmonary adenocarcinomas manifesting as pure ground glass nodules on computed tomography

Objectives: To assess the performance of the “Computer-Aided Nodule Assessment and Risk Yield” (CANARY) software in the differentiation and risk assessment of histological subtypes of lung adenocarcinomas manifesting as pure ground glass nodules on computed tomography (CT). Methods: 64 surgically resected and histologically proven adenocarcinomas manifesting as pure ground-glass nodules on CT were assessed using CANARY software, which classifies voxel-densities into three risk components (low, intermediate, and high risk). Differences in risk components between histological adenocarcinoma subtypes were analysed. To determine the optimal threshold reflecting the presence of an invasive focus, sensitivity, specificity, negative predictive value, and positive predictive value were calculated. Results: 28/64 (44%) were adenocarcinomas in situ (AIS); 26/64 (41%) were minimally invasive adenocarcinomas (MIA); and 10/64 (16%) were invasive ACs (IAC). The software showed significant differences in risk components between histological subtypes (P<0.001–0.003). A relative volume of 45% or less of low-risk components was associated with histological invasiveness (specificity 100%, positive predictive value 100%). Conclusions: CANARY-based risk assessment of ACs manifesting as pure ground glass nodules on CT allows the differentiation of their histological subtypes. A threshold of 45% of low-risk components reflects invasiveness in these groups. Key points • CANARY-based risk assessment allows the differentiation of their histological subtypes. • 45% or less of low-risk component reflects histological invasiveness. • CANARY has potential role in suspected adenocarcinomas manifesting as pure ground-glass nodules. Electronic supplementary material The online version of this article (doi:10.1007/s00330-017-4937-2) contains supplementary material, which is available to authorized users

Plasma cortisol-linked gene networks in hepatic and adipose tissues implicate corticosteroid-binding globulin in modulating tissue glucocorticoid action and cardiovascular risk

Genome-wide association meta-analysis (GWAMA) by the Cortisol Network (CORNET) consortium identified genetic variants spanning the SERPINA6/SERPINA1 locus on chromosome 14 associated with morning plasma cortisol, cardiovascular disease (CVD), and SERPINA6 mRNA expression encoding corticosteroid-binding globulin (CBG) in the liver. These and other findings indicate that higher plasma cortisol levels are causally associated with CVD; however, the mechanisms by which variations in CBG lead to CVD are undetermined. Using genomic and transcriptomic data from The Stockholm Tartu Atherosclerosis Reverse Networks Engineering Task (STARNET) study, we identified plasma cortisol-linked single-nucleotide polymorphisms (SNPs) that are trans-associated with genes from seven different vascular and metabolic tissues, finding the highest representation of trans-genes in the liver, subcutaneous fat, and visceral abdominal fat, [false discovery rate (FDR) = 15%]. We identified a subset of cortisol-associated trans-genes that are putatively regulated by the glucocorticoid receptor (GR), the primary transcription factor activated by cortisol. Using causal inference, we identified GR-regulated trans-genes that are responsible for the regulation of tissue-specific gene networks. Cis-expression Quantitative Trait Loci (eQTLs) were used as genetic instruments for identification of pairwise causal relationships from which gene networks could be reconstructed. Gene networks were identified in the liver, subcutaneous fat, and visceral abdominal fat, including a high confidence gene network specific to subcutaneous adipose (FDR = 10%) under the regulation of the interferon regulatory transcription factor, IRF2. These data identify a plausible pathway through which variation in the liver CBG production perturbs cortisol-regulated gene networks in peripheral tissues and thereby promote CVD

Radiation dose management in thoracic CT: an international survey

PURPOSEWe aimed to examine current practice patterns of international thoracic radiologists regarding radiation dose management in adult thoracic computed tomography (CT) examinations. MATERIALS AND METHODSAn electronic questionnaire was sent to 800 members of five thoracic radiology societies in North America, Europe, Asia, and Latin America addressing radiation dose training and education, standard kVp and mAs settings for thoracic CT, dose reduction practices, clinical scenarios, and demographics. RESULTSOf the 800 radiologists, 146 responded to our survey. Nearly half (66/146, 45% [95% confidence interval, 37%–53%]) had no formal training in dose reduction, with “self-study of the literature” being the most common form of training (54/146, 37% [29%–45%]). One hundred and seventeen (80% [74%–87%]) had automatic exposure control, and 76 (65% [56%–74%]) used it in all patients. Notably, most respondents (89% [84%–94%]) used a 120 to 125 kVp standard setting, whereas none used 140 kVp. The most common average dose-length-product (DLP) value was 150 to 249 mGy.cm (75/146, 51% [43%–59%]), and 59% (51%–67%) delivered less than 250 mGy.cm in a 70 kg patient. There was a tendency towards higher DLP values with multidetector-row CT. Age, gender, and pregnancy were associated more with dose reduction than weight and clinical indication. CONCLUSIONEfforts for reducing patient radiation dose are highly prevalent among thoracic radiologists. Areas for improvement include reduction of default tube current settings, reduction of anatomical scan coverage, greater use of automatic exposure control, and eventually, reduction of current reference dose values. Our study emphasizes the need for international guidelines to foster greater conformity in dose reduction by thoracic radiologists

Transition probabilities for general birth-death processes with applications in ecology, genetics, and evolution

A birth-death process is a continuous-time Markov chain that counts the number of particles in a system over time. In the general process with $n$ current particles, a new particle is born with instantaneous rate $\lambda_n$ and a particle dies with instantaneous rate $\mu_n$. Currently no robust and efficient method exists to evaluate the finite-time transition probabilities in a general birth-death process with arbitrary birth and death rates. In this paper, we first revisit the theory of continued fractions to obtain expressions for the Laplace transforms of these transition probabilities and make explicit an important derivation connecting transition probabilities and continued fractions. We then develop an efficient algorithm for computing these probabilities that analyzes the error associated with approximations in the method. We demonstrate that this error-controlled method agrees with known solutions and outperforms previous approaches to computing these probabilities. Finally, we apply our novel method to several important problems in ecology, evolution, and genetics