Inter-observer and inter-examination variability of manual vertebral bone attenuation measurements on computed tomography

Objective: To determine inter-observer and inter-examination variability of manual attenuation measurements of the vertebrae in low-dose unenhanced chest computed tomography (CT). Methods: Three hundred and sixty-seven lung cancer screening trial participants who underwent baseline and repeat unenhanced low-dose CT after 3 months because of an indeterminate lung nodule were included. The CT attenuation value of the first lumbar vertebrae (L1) was measured in all CTs by one observer to obtain inter-examination reliability. Six observers performed measurements in 100 randomly selected CTs to determine agreement with limits of agreement and Bland-Altman plots and reliability with intraclass correlation coefficients (ICCs). Reclassification analyses were performed using a threshold of 110 HU to define osteoporosis. Results: Inter-examination reliability was excellent with an ICC of 0.92 (p < 0.001). Inter-examination limits of agreement ranged from -26 to 28 HU with a mean difference of 1 ± 14 HU. Inter-observer reliability ICCs ranged from 0.70 to 0.91. Inter-examination variability led to 11.2 % reclassification of participants and inter-observer variability led to 22.1 % reclassification. Conclusions: Vertebral attenuation values can be manually quantified with good to excellent inter-examination and inter-observer reliability on unenhanced low-dose chest CT. This information is valuable for early detection of osteoporosis on low-dose chest CT. Key Points: • Vertebral attenuation values can be manually quantified on low-dose unenhanced CT reliably.• Vertebral attenuation measurements may be helpful in detecting subclinical low bone density.• This could become of importance in the detection of osteoporosis

CT-Based Local Distribution Metric Improves Characterization of COPD

Parametric response mapping (PRM) of paired CT lung images has been shown to improve the phenotyping of COPD by allowing for the visualization and quantification of non-emphysematous air trapping component, referred to as functional small airways disease (fSAD). Although promising, large variability in the standard method for analyzing PRM(fSAD) has been observed. We postulate that representing the 3D PRM(fSAD) data as a single scalar quantity (relative volume of PRM(fSAD)) oversimplifies the original 3D data, limiting its potential to detect the subtle progression of COPD as well as varying subtypes. In this study, we propose a new approach to analyze PRM. Based on topological techniques, we generate 3D maps of local topological features from 3D PRM(fSAD) classification maps. We found that the surface area of fSAD (S(fSAD)) was the most robust and significant independent indicator of clinically meaningful measures of COPD. We also confirmed by micro-CT of human lung specimens that structural differences are associated with unique S(fSAD) patterns, and demonstrated longitudinal feature alterations occurred with worsening pulmonary function independent of an increase in disease extent. These findings suggest that our technique captures additional COPD characteristics, which may provide important opportunities for improved diagnosis of COPD patients

Unravelling complexities of the subsolid pulmonary nodule—detection, characterization, natural history, monitoring and (future) patient management

Lung cancer is a major cause of death worldwide with an estimated 1.8 million new diagnoses and 1.6 million deaths annually (1). The five-year survival rate is poor as symptoms usually occur in an advanced stage of cancer where treatment options are limited and no curative therapies are available. Detecting suspicious lesions at an early stage is therefore thought to improve overall lung cancer survival and for this reason several lung cancer screening trials with chest computed tomography have been employed. In addition, the use of chest computed tomography (CT) in clinical care has sharply risen in the past decade. This has taught us important lessons on pulmonary lesions that look like cancer and even have malignant cells in histology, but do not behave malignant. This observation is quite similar to indolent lesions in other organs (2)

Unravelling complexities of the subsolid pulmonary nodule—detection, characterization, natural history, monitoring and (future) patient management

Lung cancer is a major cause of death worldwide with an estimated 1.8 million new diagnoses and 1.6 million deaths annually (1). The five-year survival rate is poor as symptoms usually occur in an advanced stage of cancer where treatment options are limited and no curative therapies are available. Detecting suspicious lesions at an early stage is therefore thought to improve overall lung cancer survival and for this reason several lung cancer screening trials with chest computed tomography have been employed. In addition, the use of chest computed tomography (CT) in clinical care has sharply risen in the past decade. This has taught us important lessons on pulmonary lesions that look like cancer and even have malignant cells in histology, but do not behave malignant. This observation is quite similar to indolent lesions in other organs (2)

Normalized emphysema scores on low dose CT: Validation as an imaging biomarker for mortality

Contains fulltext : 181640.pdf (publisher's version ) (Open Access

Airway wall thickening on CT: Relation to smoking status and severity of COPD

Contains fulltext : 201027.pdf (publisher's version ) (Closed access

Biotic modifiers, environmental modulation and species distribution models

The ability of species to modulate environmental conditions and resources has long been of interest. In the past three decades the impacts of these biotic modifiers have been investigated as ‘ecosystem engineers’, ‘niche constructors’, ‘facilitators’ and ‘keystone species’. This environmental modulation can vary spatially from extremely local to global, temporally from days to geological time, and taxonomically from a few to a very large number of species. Modulation impacts are pervasive and affect, inter alia, the climate, structural environments, disturbance rates, soils and the atmospheric chemical composition. Biotic modifiers may profoundly transform the projected environmental conditions, and consequently have a significant impact on the predicted occurrence of the focal species in species distribution models (SDMs). This applies especially when these models are projected into different geographical regions or into the future or the past, where these biotic modifiers may be absent, or other biotic modifiers may be present. We show that environmental modulation can be represented in SDMs as additional variables. In some instances it is possible to use the species (e.g. biotic modifiers) in order to reflect the modulation. This would apply particularly to cases where the effect is the result of a single or a small number of species (e.g. elephants transforming woodland to grassland). Where numerous species generate an effect (such as tree species making a forest, or grasses facilitating fire) that modulates the abiotic environment, the effect itself might be a better descriptor for the aggregated action of the numerous species. We refer to this ‘effect’ as the modulator. Much of the information required to incorporate environmental modulation effects in SDMs is already available from remote-sensing data and vegetation models