Mario Silva

Stopping Smoking Reduces Mortality in Low-Dose Computed Tomography Screening Participants

Introduction: The National Lung Screening Trial has achieved a 7% reduction in total mortality with low‐dose computed tomography (LDCT) screening as compared with in the chest radiography arm. Other randomized trials are under way, comparing LDCT screening with no intervention. None of these studies was designed to investigate the impact of smoking habits on screening outcome. In the present study, we tested the effect of stopping smoking on the overall mortality of participants undergoing repeated LDCT screening for many years. Methods: Between 2000 and 2010, 3381 smokers aged 50 years or older were enrolled in two LDCT screening programs. On the basis of the last follow‐up information, subjects were divided into two groups: current smokers throughout the screening period and former smokers. Results: With a median follow‐up time of 9.7 years and a total of 32,857 person‐years (PYs) of follow‐up, a total of 151 deaths were observed in the group of 1797 current smokers (17,846 PYs) versus 109 among 1584 former smokers (15,011 PYs), corresponding to mortality rates of 8.46 and 7.26 for every 1000 PYs, respectively. Compared with current smokers, former smokers had an adjusted mortality hazard ratio of 0.61 (95% confidence interval: 0.44–0.83), with a 39% reduction in mortality. A similar reduction in mortality was observed in the subset of 712 late quitters, with a hazard ratio of 0.65 (95% confidence interval: 0.44–0.96). Conclusions: Stopping smoking significantly reduces the overall mortality of smokers enrolled in LDCT screening programs. The beneficial effect of stopping smoking on total mortality appears to be threefold to fivefold greater than the one achieved by earlier detection in the National Lung Screening Trial.

Imaging of Sarcoidosis

The diagnostic imaging contributes significantly to the diagnosis and management of sarcoidosis. Imaging techniques are widely employed in the assessment of thoracic and extra-thoracic involvement from sarcoidosis. For the diagnosis of sarcoidosis, chest radiograph has been the cornerstone of sarcoidosis since 1961, when Scadding proposed a standardized staging system. Currently, computed tomography (CT) represents the reference standard for the assessment of both mediastinal lymph nodes and pulmonary findings. In particular, high-resolution computed tomography (HRCT) is more accurate compared to chest radiography for the detection of subtle parenchymal involvement, and provides comprehensive overview of anatomical detail and abnormalities of lung structures. Notably, HRCT allows for accurate differentiation between reversible and irreversible lung disease, which is cornerstone of prognostication. Radionuclide imaging (gallium-67 and 18F-fluorodeoxyglucose) provides information about activity of the disease and is also useful for diagnostic workup of patients with unexplained persistent disabling symptoms. Magnetic resonance is sensitive for the detection of sarcoidosis granulomata within myocardium, thus providing detailed roadmap for biopsy. For the management of sarcoidosis, CT is of paramount importance in the detection and differential of most common complications, such as vascular disease and suspicious nodular lesions. Conversely, the role of CT in the follow-up of asymptomatic subjects is still under debate. This review focuses on the role of diagnostic imaging in the diagnosis and follow-up of sarcoidosis.

Screening with Low-Dose Computed Tomography Does Not Improve Survival of Small Cell Lung Cancer

Introduction: Small cell lung cancer (SCLC) constitutes a distinct component of symptomatic or advanced‐stage lung cancers in clinical practice and in lung cancer screening trials. The purpose of this study was to describe the outcome of SCLC in lung cancer screening trials and compare the frequency of SCLC in our cohort with that in the major lung cancer screening trials. Methods: Subjects with a diagnosis of SCLC were selected from two lung cancer screening trials by low‐dose computed tomography (LDCT), and their demographic characteristics, clinical parameters, tumor stage at diagnosis, therapy, and survival times were recorded. Survival curves were estimated using the Kaplan‐Meier method. Results: Ten cases of SCLC were reported in 45,141 person‐years (22 in 100,000 person‐years), representing the 6% of all lung cancer cases. Cumulative tobacco consumption was 82 pack‐years compared with 39 and 46 pack‐years for the overall study population and subjects with non‐SCLC, respectively. Most of the neoplasms were in an advanced stage (seven in stage IV and one each in stages IIIb, IIIa, and Ia). Two subjects were treated with lobectomy for curative purposes and died of diffuse metastasis within 2 years of diagnosis. The median overall survival time in the LDCT arms was 20.6 months, with no survivors remaining at 3 years. Conclusions: Subjects in whom SCLC develops are a subgroup of smokers with extremely high cumulative tobacco consumption. Consequently, the frequency of SCLC in our population was lower than in other screening populations, with higher cumulative tobacco consumption. Screening for lung cancer by LDCT does not improve survival of SCLC, with no survivors remaining at 3 years after diagnosis.

Lung cancer screening with low-dose spiral computed tomography: evidence from a pooled analysis of two Italian randomized trials.

The benefits and harms of lung cancer (LC) screening with low-dose computed tomography (LDCT) are debatable. Positive results from the US National Lung Screening Trial were not evident in the European trials, possibly due to their smaller sample sizes. To address this issue, we conducted a patient-level pooled analysis of two Italian randomized controlled trials. Data from DANTE and MILD trials were combined for a total of 3640 individuals in the LDCT arm and 2909 in the control arm. LC and overall mortality were analyzed using multivariate hazard ratios (HRs) and log-rank tests stratified by study. The median follow-up was 8.2 years, with a total of 30 480 person-years in the LDCT arm and 22 157 in the control arm. A total of 192 patients developed LC in the LDCT arm and 105 in the control arm. Half of the LC cases in the LDCT arm had stage IA or IB cancer, as compared with 21% in the control arm. Overall mortality rates/100 000 person-years were 925 in the LDCT arm and 1074 in the control arm, and LC mortality rates were 299 and 357, respectively. The multivariate pooled overall mortality HR was 0.89 (95% confidence interval: 0.74–1.06) and the LC mortality HR was 0.83 (95% confidence interval: 0.61–1.12) for the LDCT arm as compared with the control arm. The present pooled analysis shows a nonsignificant 11% reduction in overall mortality in individuals undergoing LDCT screening as compared with the control arm. A pooled analysis of all European trials would be a useful contribution to assess the real benefit of LDCT screening.

CT-guided biopsy of pulmonary nodules: is pulmonary hemorrhage a complication or an advantage?

PURPOSE We aimed to assess the correlation between pulmonary hemorrhage and pneumothorax in computed tomography (CT)-guided transthoracic fine needle aspiration (TTFNA), particularly its possible value as protection against the development of pneumotorax. MATERIALS AND METHODS We reviewed the CT images of 538 patients (364 males and 174 females, mean age 70 years, range 36-90 years) who underwent CT-guided TTFNA of pulmonary nodules between January 2008 and September 2013. The following CT findings were assessed: pulmonary hemorrhage (type 1, along the needle track; type 2, perilesional; low-grade, ≤6 mm; high-grade, >6 mm), pneumothorax, distance between the target nodule and the pleural surface, and emphysema. RESULTS Pneumothorax occurred in 154 cases (28.6%) and pulmonary hemorrhage occurred in 144 cases (26.8%). The incidence of pneumothorax was lower in patients showing type 1 and high-grade pulmonary hemorrhage pattern. The incidence of pneumothorax in biopsies ≥30 mm from pleural surface was 26% (12/46) in cases showing this pattern, while it was 71.4% (30/42) when this pattern was not seen. Similarly, the incidence of pneumothorax in biopsies <30 mm from the pleural surface was 0% (0/28) in cases showing this hemorrhage pattern, while it was 19% (76/394) when this pattern was not seen. CONCLUSION Pulmonary hemorrhage during TTFNA is a frequent event that protects against pneumothorax. A bleeding greater than 6 mm along the needle track is associated with lower incidence of pneumothorax, especially in biopsies deeper than 3 cm.

Increased mean lung density: Another independent predictor of lung cancer?

OBJECTIVES To investigate the relationship between emphysema phenotype, mean lung density (MLD), lung function and lung cancer by using an automated multiple feature analysis tool on thin-section computed tomography (CT) data. METHODS Both emphysema phenotype and MLD evaluated by automated quantitative CT analysis were compared between outpatients and screening participants with lung cancer (n=119) and controls (n=989). Emphysema phenotype was defined by assessing features such as extent, distribution on core/peel of the lung and hole size. Adjusted multiple logistic regression models were used to evaluate independent associations of CT densitometric measurements and pulmonary function test (PFT) with lung cancer risk. RESULTS No emphysema feature was associated with lung cancer. Lung cancer risk increased with decreasing values of forced expiratory volume in 1s (FEV1) independently of MLD (OR 5.37, 95% CI: 2.63-10.97 for FEV1<60% vs. FEV1≥90%), and with increasing MLD independently of FEV1 (OR 3.00, 95% CI: 1.60-5.63 for MLD>-823 vs. MLD<-857 Hounsfield units). CONCLUSION Emphysema per se was not associated with lung cancer whereas decreased FEV1 was confirmed as being a strong and independent risk factor. The cross-sectional association between increased MLD and lung cancer requires future validations.

Computed tomography measurement of rib cage morphometry in emphysema.

Background Factors determining the shape of the human rib cage are not completely understood. We aimed to quantify the contribution of anthropometric and COPD-related changes to rib cage variability in adult cigarette smokers. Methods Rib cage diameters and areas (calculated from the inner surface of the rib cage) in 816 smokers with or without COPD, were evaluated at three anatomical levels using computed tomography (CT). CTs were analyzed with software, which allows quantification of total emphysema (emphysema%). The relationship between rib cage measurements and anthropometric factors, lung function indices, and %emphysema were tested using linear regression models. Results A model that included gender, age, BMI, emphysema%, forced expiratory volume in one second (FEV1)%, and forced vital capacity (FVC)% fit best with the rib cage measurements (R2 = 64% for the rib cage area variation at the lower anatomical level). Gender had the biggest impact on rib cage diameter and area (105.3 cm2; 95% CI: 111.7 to 98.8 for male lower area). Emphysema% was responsible for an increase in size of upper and middle CT areas (up to 5.4 cm2; 95% CI: 3.0 to 7.8 for an emphysema increase of 5%). Lower rib cage areas decreased as FVC% decreased (5.1 cm2; 95% CI: 2.5 to 7.6 for 10 percentage points of FVC variation). Conclusions This study demonstrates that simple CT measurements can predict rib cage morphometric variability and also highlight relationships between rib cage morphometry and emphysema.

Lung volume reduction of pulmonary emphysema: the radiologist task.

Purpose of review Several lung volume reduction (LVR) techniques have been increasingly evaluated in patients with advanced pulmonary emphysema, especially in the last decade. Radiologist plays a pivotal role in the characterization of parenchymal damage and, thus, assessment of eligibility criteria. This review aims to discuss the most common LVR techniques, namely LVR surgery, endobronchial valves, and coils LVR, with emphasis on the role of computed tomography (CT). Recent findings Several trials have recently highlighted the importance of regional quantification of emphysema by computerized CT-based segmentation of hyperlucent parenchyma, which is strongly recommended for candidates to any LVR treatment. In particular, emphysema distribution pattern and fissures integrity are evaluated to tailor the choice of the most appropriate LVR technique. Furthermore, a number of CT measures have been tested for the personalization of treatment, according to imaging detected heterogeneity of parenchymal disease. Summary CT characterization of heterogeneous parenchymal abnormalities provides criteria for selection of the preferable treatment in each patient and improves outcome of LVR as reflected by better quality of life, higher exercise tolerance, and lower mortality.

Longitudinal evolution of incidentally detected solitary pure ground-glass nodules on CT: relation to clinical metrics

PURPOSE We aimed to assess the relation between basic clinical parameters and evolution of solitary pure ground-glass nodules (pGGN) in the lungs. METHODS Baseline and follow-up computed tomography (CT) of patients with solitary pGGN were selected and two radiologists independently reviewed CTs for nodule characterization. CT features of solitary pGGN were manually measured maximum diameter (D1) and its orthogonal diameter (D2), mean diameter (mD), D1 to D2 ratio as surrogate of roundness, and location according to lobar anatomy. Longitudinal changes were assessed and solitary pGGNs were classified as resolved or persisting. Persisting nodules were further classified as stable or grown according to an increase in mD of ≥2 mm or appearance of solid component. Baseline CT features of solitary pGGNs and clinical metrics of patients were compared between resolved and persisting nodules and, thereafter, between stable and grown lesions. RESULTS A total of 95 subjects with solitary pGGN were included. After a median 16-month follow-up, 20 nodules resolved, while 75 persisted. Among persisting nodules, 18 were grown and 57 were stable. Grown nodules showed larger D1 and mD compared with stable pGGNs (P < 0.001). Subjects with grown nodules were older (P = 0.021). Logistic regression analyses showed higher likelihood of growth for nodules ≥10 mm (odds ratio [OR], 8.355; P = 0.001) and subjects older than 67 years (OR, 3.656; P = 0.034). CONCLUSION Nodules ≥10 mm in subjects older than 67 years showed higher likelihood of growth. These data could contribute to a more individual approach to the management of solitary pGGN.

Deep learning for classifying fibrotic lung disease on high-resolution computed tomography: a case-cohort study

BACKGROUND Based on international diagnostic guidelines, high-resolution CT plays a central part in the diagnosis of fibrotic lung disease. In the correct clinical context, when high-resolution CT appearances are those of usual interstitial pneumonia, a diagnosis of idiopathic pulmonary fibrosis can be made without surgical lung biopsy. We investigated the use of a deep learning algorithm for provision of automated classification of fibrotic lung disease on high-resolution CT according to criteria specified in two international diagnostic guideline statements: the 2011 American Thoracic Society (ATS)/European Respiratory Society (ERS)/Japanese Respiratory Society (JRS)/Latin American Thoracic Association (ALAT) guidelines for diagnosis and management of idiopathic pulmonary fibrosis and the Fleischner Society diagnostic criteria for idiopathic pulmonary fibrosis. METHODS In this case-cohort study, for algorithm development and testing, a database of 1157 anonymised high-resolution CT scans showing evidence of diffuse fibrotic lung disease was generated from two institutions. We separated the scans into three non-overlapping cohorts (training set, n=929; validation set, n=89; and test set A, n=139) and classified them using 2011 ATS/ERS/JRS/ALAT idiopathic pulmonary fibrosis diagnostic guidelines. For each scan, the lungs were segmented and resampled to create a maximum of 500 unique four slice combinations, which we converted into image montages. The final training dataset consisted of 420 096 unique montages for algorithm training. We evaluated algorithm performance, reported as accuracy, prognostic accuracy, and weighted κ coefficient (κw) of interobserver agreement, on test set A and a cohort of 150 high-resolution CT scans (test set B) with fibrotic lung disease compared with the majority vote of 91 specialist thoracic radiologists drawn from multiple international thoracic imaging societies. We then reclassified high-resolution CT scans according to Fleischner Society diagnostic criteria for idiopathic pulmonary fibrosis. We retrained the algorithm using these criteria and evaluated its performance on 75 fibrotic lung disease specific high-resolution CT scans compared with four specialist thoracic radiologists using weighted κ coefficient of interobserver agreement. FINDINGS The accuracy of the algorithm on test set A was 76·4%, with 92·7% of diagnoses within one category. The algorithm took 2·31 s to evaluate 150 four slice montages (each montage representing a single case from test set B). The median accuracy of the thoracic radiologists on test set B was 70·7% (IQR 65·3-74·7), and the accuracy of the algorithm was 73·3% (93·3% were within one category), outperforming 60 (66%) of 91 thoracic radiologists. Median interobserver agreement between each of the thoracic radiologists and the radiologists majority opinion was good (κw=0·67 [IQR 0·58-0·72]). Interobserver agreement between the algorithm and the radiologists majority opinion was good (κw=0·69), outperforming 56 (62%) of 91 thoracic radiologists. The algorithm provided equally prognostic discrimination between usual interstitial pneumonia and non-usual interstitial pneumonia diagnoses (hazard ratio 2·88, 95% CI 1·79-4·61, p<0·0001) compared with the majority opinion of the thoracic radiologists (2·74, 1·67-4·48, p<0·0001). For Fleischner Society high-resolution CT criteria for usual interstitial pneumonia, median interobserver agreement between the radiologists was moderate (κw=0·56 [IQR 0·55-0·58]), but was good between the algorithm and the radiologists (κw=0·64 [0·55-0·72]). INTERPRETATION High-resolution CT evaluation by a deep learning algorithm might provide low-cost, reproducible, near-instantaneous classification of fibrotic lung disease with human-level accuracy. These methods could be of benefit to centres at which thoracic imaging expertise is scarce, as well as for stratification of patients in clinical trials. FUNDING None.