Lung nodules: size still matters

The incidence of indeterminate pulmonary nodules has risen constantly over the past few years. Determination of lung nodule malignancy is pivotal, because the early diagnosis of lung cancer could lead to a definitive intervention. According to the current international guidelines, size and growth rate represent the main indicators to determine the nature of a pulmonary nodule. However, there are some limitations in evaluating and characterising nodules when only their dimensions are taken into account. There is no single method for measuring nodules, and intrinsic errors, which can determine variations in nodule measurement and in growth assessment, do exist when performing measurements either manually or with automated or semi-automated methods. When considering subsolid nodules the presence and size of a solid component is the major determinant of malignancy and nodule management, as reported in the latest guidelines. Nevertheless, other nodule morphological characteristics have been associated with an increased risk of malignancy. In addition, the clinical context should not be overlooked in determining the probability of malignancy. Predictive models have been proposed as a potential means to overcome the limitations of a sized-based assessment of the malignancy risk for indeterminate pulmonary nodules

Pulmonary adenocarcinomas presenting as ground-glass opacities on multidetector CT: three-dimensional computer-assisted analysis of growth pattern and doubling time

We aimed to evaluate the growth pattern and doubling time (DT) of pulmonary adenocarcinomas exhibiting ground-glass opacities (GGOs) on multidetector computed tomography (CT). METHODS The growth pattern and DT of 22 pulmonary adenocarcinomas exhibiting GGOs were retrospectively analyzed using three-dimensional semiautomatic software. Analysis of each lesion was based on calculations of volume and mass changes and their respective DTs throughout CT follow- up. Three-dimensional segmentation was performed by a single radiologist on each CT scan. The same observer and another radiologist independently repeated the segmentation at the baseline and the last CT scan to determine the variability of the measurements. The relationships among DTs, histopathology, and initial CT features of the lesions were also analyzed. RESULTS Pulmonary adenocarcinomas presenting as GGOs exhibited different growth patterns: some lesions grew rapidly and some grew slowly, whereas others alternated between periods of growth, stability, or shrinkage. A significant increase in volume and mass that exceeded the coefficient of repeatability of interobserver variability was observed in 72.7% and 84.2% of GGOs, respectively. The volume-DTs and mass-DTs were heterogeneous throughout the follow-up CT scan (range, -4293 to 21928 and -3113 to 17020 days, respectively), and their intra- and interobserver variabilities were moderately high. The volume-DTs and mass-DTs were not correlated with the initial CT features of GGOs; however, they were significantly shorter in invasive adenocarcinomas (P = 0.002 and P = 0.001, respectively). CONCLUSION Pulmonary adenocarcinomas exhibiting GGOs show heterogeneous growth patterns with a trend toward a progressive increase in size. DTs may be useful for predicting tumor aggressiveness

Using Transitional Changes on Highâ Resolution Computed Tomography to Monitor the Impact of Cyclophosphamide or Mycophenolate Mofetil on Systemic Sclerosisâ Related Interstitial Lung Disease

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/153695/1/art41085.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/153695/2/art41085_am.pd

Comparison of temporal evolution of computed tomography imaging features in COVID-19 and influenza infections in a multicenter cohort study

Purpose To compare temporal evolution of imaging features of coronavirus disease 2019 (COVID-19) and influenza in computed tomography and evaluate their predictive value for distinction. Methods In this retrospective, multicenter study 179 CT examinations of 52 COVID-19 and 44 influenza critically ill patients were included. Lung involvement, main pattern (ground glass opacity, crazy paving, consolidation) and additional lung and chest findings were evaluated by two independent observers. Additional findings and clinical data were compared patient-wise. A decision tree analysis was performed to identify imaging features with predictive value in distinguishing both entities. Results In contrast to influenza patients, lung involvement remains high in COVID-19 patients > 14 days after the diagnosis. The predominant pattern in COVID-19 evolves from ground glass at the beginning to consolidation in later disease. In influenza there is more consolidation at the beginning and overall less ground glass opacity (p = 0.002). Decision tree analysis yielded the following: Earlier in disease course, pleural effusion is a typical feature of influenza (p = 0.007) whereas ground glass opacities indicate COVID-19 (p = 0.04). In later disease, particularly more lung involvement (p < 0.001), but also less pleural (p = 0.005) and pericardial (p = 0.003) effusion favor COVID-19 over influenza. Regardless of time point, less lung involvement (p < 0.001), tree-in-bud (p = 0.002) and pericardial effusion (p = 0.01) make influenza more likely than COVID-19. Conclusions This study identified differences in temporal evolution of imaging features between COVID-19 and influenza. These findings may help to distinguish both diseases in critically ill patients when laboratory findings are delayed or inconclusive

Severity classification for idiopathic pulmonary fibrosis by using fuzzy logic

OBJECTIVE: To set out a severity classification for idiopathic pulmonary fibrosis (IPF) based on the interaction of pulmonary function parameters with high resolution computed tomography (CT) findings. INTRODUCTION: Despite the contribution of functional and radiological methods in the study of IPF, there are few classification proposals for the disease based on these examinations. METHODS: A cross-sectional study was carried out, in which 41 non-smoking patients with IPF were evaluated. The following high resolution CT findings were quantified using a semi-quantitative scoring system: reticular abnormality, honeycombing and ground-glass opacity. The functional variables were measured by spirometry, forced oscillation technique, helium dilution method, as well as the single-breath method of diffusing capacity of carbon monoxide. With the interaction between functional indexes and high resolution CT scores through fuzzy logic, a classification for IPF has been built. RESULTS: Out of 41 patients studied, 26 were male and 15 female, with a mean age of 70.8 years. Volume measurements were the variables which showed the best interaction with the disease extension on high resolution CT, while the forced vital capacity showed the lowest estimative errors in comparison to total lung capacity. A classification for IPF was suggested based on the 95% confidence interval of the forced vital capacity %: mild group (>92.7); moderately mild (76.9-92.6); moderate (64.3-76.8%); moderately severe (47.1-64.2); severe (24.3-47.0); and very severe (<24.3). CONCLUSION: Through fuzzy logic, an IPF classification was built based on forced vital capacity measurement with a simple practical application

A New Method of Measuring the Amount of Soft Tissue in Pulmonary Ground-Glass Opacity Nodules: a Phantom Study

OBJECTIVE: To devise a new method to measure the amount of soft tissue in pulmonary ground-glass opacity nodules, and to compare the use of this method with a previous volumetric measurement method by use of a phantom study. MATERIALS AND METHODS: Phantom nodules were prepared with material from fixed normal swine lung. Forty nodules, each with a diameter of 10 mm, were made with a variable mean attenuation. The reference-standard amount of soft tissue in the nodules was obtained by dividing the weight by the specific gravity. The imaging data on the phantom nodules were acquired with the use of a 16-channel multidetector CT scanner. The CT-measured amount of soft tissue of the nodules was calculated as follows: soft tissue amount = volume x (1 + mean attenuation value / 1,000). The relative percentage error (RPE) between the CT-measured amount of the soft tissue and the reference-standard amount of the soft tissue was also measured. The RPEs determined with use of the new method were compared with the RPEs determined with the current volumetric measurement method by the use of the paired t test. RESULTS: The CT-measured amount of soft tissue showed a strong correlation with the reference-standard amount of soft tissue (R(2) = 0.996, p < 0.01). The mean RPE of the CT-measured amount of soft tissue in the nodules was -7.79 +/- 1.88%. The mean RPE of the CT-measured volume was 114.78 +/- 51.02%, which was significantly greater than the RPE of the CT-measured amount of soft tissue (p < 0.01). CONCLUSION: The amount of soft tissue measured by the use of CT reflects the reference-standard amount of soft tissue in the ground-glass opacity nodules much more accurately than does the use of the CT-measured volume