Emphysema in asymptomatic smokers: Quantitative CT evaluation in correlation with pulmonary function tests

Distinguishing Adenocarcinomas from Granulomas in the CT scan of the chest: performance degradation evaluation in the automatic segmentation framework

OBJECTIVE

The most common histopathologic malignant and benign nodules are Adenocarcinoma and Granuloma, respectively, which have different standards of care. In this paper, we propose an automatic framework for the diagnosis of the Adenocarcinomas and the Granulomas in the CT scans of the chest from a private dataset. We use the radiomic features of the nodules and the attached vessel tortuosity for the diagnosis. The private dataset includes 22 CTs for each nodule type, i.e., adenocarcinoma and granuloma. The dataset contains the CTs of the non-smoker patients who are between 30 and 60 years old. To automatically segment the delineated nodule area and the attached vessels area, we apply a morphological-based approach. For distinguishing the malignancy of the segmented nodule, two texture features of the nodule, the curvature Mean and the number of the attached vessels are extracted.

RESULTS

We compare our framework with the state-of-the-art feature selection methods for differentiating Adenocarcinomas from Granulomas. These methods employ only the shape features of the nodule, the texture features of the nodule, or the torsion features of the attached vessels along with the radiomic features of the nodule. The accuracy of our framework is improved by considering the four selected features.

Optimal threshold in low-dose CT quantification of emphysema

OBJECTIVE

Low-dose CT is now widely used in the screening of lung cancer and the detection of pulmonary nodules. There has also been increasing interest in using Low-dose CT for evaluating emphysema. In conventional dose CT, the threshold of -950HU is a common threshold for density-based emphysema quantification for worldwide population. However, the optimal threshold for assessing emphysema at low-dose CT has not been determined. The purpose of this study is to determine the optimal threshold for low-dose CT quantification of emphysema for Chinese population.

MATERIALS AND METHODS

In this study, 548 low-dose chest CT examinations acquired from different subjects (119 none, 49 mild, 163 moderate, 152 severe, and 65 very severe obstruction) are collected. At the level of the entire lung and individual lobes, the extent of emphysema was quantified by the percentage of the low attenuation area (LAA%) at a wide range of thresholds from -850HU to -1000HU. Both Pearson and Spearman's rank correlation coefficients were used to assess the correlations between 1) LAA% and pulmonary functions and 2) LAA% and the five-category classification. The statistical significance of the difference between correlation coefficients were evaluated using Steiger'Z test.

RESULTS

LAA% had a good correlation with both pulmonary function (|r| = 0.1-0.600, p < 0.001) and the five-category classification (r = 0.163-0.602, p < 0.001) in both the entire lung and individual lobes under different thresholds. The highest correlation coefficient is obtained at -940HU instead of -950HU.

CONCLUSION

Low-dose CT can be used for quantitative assessment of emphysema, and the threshold of -940HU is a suitable threshold for quantifying emphysema in low-dose CT images for Chinese population.

Deep learning-enabled accurate normalization of reconstruction kernel effects on emphysema quantification in low-dose CT

Lung densitometry is being frequently adopted in CT-based emphysema quantification, yet known to be affected by the choice of reconstruction kernel. This study presents a two-step deep learning architecture that enables accurate normalization of reconstruction kernel effects on emphysema quantification in low-dose CT. Deep learning is used to convert a CT image of a sharp kernel to that of a standard kernel with restoration of truncation artifacts and smoothing-free pixel size normalization. We selected 353 scans reconstructed by both standard and sharp kernels from four different CT scanners from the United States National Lung Screening Trial program database. A truncation artifact correction model was constructed with a combination of histogram extrapolation and a deep learning model trained with truncated and non-truncated image sets. Then, we performed frequency domain zero-padding to normalize reconstruction field of view effects while preventing image smoothing effects. The kernel normalization model has a U-Net based architecture trained for each CT scanner dataset. Three lung density measurements including relative lung area under 950 HU (RA950), lower 15th percentile threshold (perc15), and mean lung density were obtained in the datasets from standard, sharp, and normalized kernels. The effect of kernel normalization was evaluated with pair-wise differences in lung density metrics. The mean of pair-wise differences in RA950 between standard and sharp kernel reconstructions was reduced from 10.75% to  -0.07% using kernel normalization. The difference for perc15 decreased from  -31.03 HU to  -0.30 HU after kernel normalization. Our study demonstrated the feasibility of applying deep learning techniques for normalizing CT kernel effects, thereby reducing the kernel-induced variability in lung density measurements. The deep learning model could increase the accuracy of emphysema quantification, thereby allowing reliable surveillance of emphysema in lung cancer screening even when follow-up CT scans are acquired with different reconstruction kernels.

Prevalence of chronic obstructive pulmonary disease in asymptomatic smokers

Background

Physicians do not routinely recommend smokers to undergo spirometry unless they are symptomatic.

Objective

To test the hypothesis that there are a significant number of asymptomatic smokers with chronic obstructive pulmonary disease (COPD), we estimated the prevalence of COPD in a group of asymptomatic smokers.

Methods

Two thousand nine hundred and sixty-one smokers with a cumulative consumption history of at least 10 pack-years, either smokers with symptoms or smokers without symptoms (WOS) were invited to perform a spirometry and complete a symptom questionnaire.

Results

Six hundred and thirty-seven (21.5%) smokers had no symptoms, whereas 2,324 (78.5%) had at least one symptom. The prevalence of COPD in subjects WOS was 1.5% when considering the whole group of smokers (45/2,961) and 7% when considering only the group WOS (45/637). From 329 smokers with COPD, 13.7% were WOS. Subjects WOS were younger, had better lung function and lower cumulative consumption of cigarettes, estimated as both cigarettes per day and pack-years. According to severity of airflow limitation, 69% vs 87% of subjects were classified as Global Initiative for Chronic Obstructive Lung Disease stages I–II in the WOS and smokers with symptoms groups, respectively (P<0.001). A multivariate analysis showed that forced expiratory volume in 1 second (mL) was the only predictive factor for COPD in asymptomatic smokers.

Conclusion

Prevalence of COPD in asymptomatic smokers is 1.5%. This number of asymptomatic smokers may be excluded from the benefit of an “early” intervention, not just pharmacological but also from smoking cessation counseling. The higher forced expiratory volume in 1 second may contribute to prevent early diagnosis.