CT-Definable Subtypes of Chronic Obstructive Pulmonary Disease: A Statement of the Fleischner Society

A clinical-radiomics nomogram based on automated segmentation of chest CT to discriminate PRISm and COPD patients

Purpose

It is vital to develop noninvasive approaches with high accuracy to discriminate the preserved ratio impaired spirometry (PRISm) group from the chronic obstructive pulmonary disease (COPD) groups. Radiomics has emerged as an image analysis technique. This study aims to develop and confirm the new radiomics-based noninvasive approach to discriminate these two groups.

Methods

Totally 1066 subjects from 4 centers were included in this retrospective research, and classified into training, internal validation or external validation sets. The chest computed tomography (CT) images were segmented by the fully automated deep learning segmentation algorithm (Unet231) for radiomics feature extraction. We established the radiomics signature (Rad-score) using the least absolute shrinkage and selection operator algorithm, then conducted ten-fold cross-validation using the training set. Last, we constructed a radiomics signature by incorporating independent risk factors using the multivariate logistic regression model. Model performance was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analyses (DCA).

Results

The Rad-score, including 15 radiomic features in whole-lung region, which was suitable for diffuse lung diseases, was demonstrated to be effective for discriminating between PRISm and COPD. Its diagnostic accuracy was improved through integrating Rad-score with a clinical model, and the area under the ROC (AUC) were 0.82(95 %CI 0.79-0.86), 0.77(95 %CI 0.72-0.83) and 0.841(95 %CI 0.78-0.91) for training, internal validation and external validation sets, respectively. As revealed by analysis, radiomics nomogram showed good fit and superior clinical utility.

Conclusions

The present work constructed the new radiomics-based nomogram and verified its reliability for discriminating between PRISm and COPD.

Large cell carcinoma of the lung: LDCT features and survival in screen-detected cases

PURPOSE

To investigate the early radiological features and survival of Large Cell Carcinoma (LCC) cases diagnosed in low-dose computed tomography (LDCT) screening trials.

METHODS

Two radiologists jointly reviewed the radiological features of screen-detected LCCs observed in NLST, ITALUNG, and LUSI trials between 2002 and 2016, comprising a total of 29,744 subjects who underwent 3-5 annual screening LDCT examinations. Survival or causes of death were established according to the mortality registries extending more than 12 years since randomization.

RESULTS

LCC was diagnosed in 30 (4 %) of 750 subjects with screen-detected lung cancer (LC), including 15 prevalent and 15 incident cases. Three additional LCCs occurred as interval cancers during the screening period. LDCT images were available for 29 cases of screen-detected LCCs, and 28 showed a single, peripheral, and well-defined solid nodule or mass with regularly smooth (39 %), lobulated (43 %), or spiculated (18 %) margins. One case presented as hilar mass. In 9 incident LCCs, smaller solid nodules were identified in prior LDCT examinations, allowing us to calculate a mean Volume Doubling Time (VDT) of 98.7 ± 47.8 days. The overall five-year survival rate was 50 %, with a significant (p = 0.0001) difference between stages I-II (75 % alive) and stages III-IV (10 % alive).

CONCLUSIONS

LCC is a fast-growing neoplasm that can escape detection by annual LDCT screening. LCC typically presents as a single solid peripheral nodule or mass, often with lobulated margins, and exhibits a short VDT. The 5-year survival reflects the stage at diagnosis.

Association of bronchial disease on CT imaging and clinical definitions of chronic bronchitis in a single-center COPD phenotyping study

INTRODUCTION

Chronic Bronchitis (CB) represents a phenotype of chronic obstructive pulmonary disease (COPD). While several definitions have been used for diagnosis, the relationship between clinical definitions and radiologic assessment of bronchial disease (BD) has not been well studied. The aim of this study was to evaluate the relationship between three clinical definitions of CB and radiographic findings of BD in spirometry-defined COPD patients.

METHODS

A cross-sectional analysis was performed from a COPD phenotyping study. It was a prospective observational cohort. Participants had spirometry-defined COPD and available chest CT imaging. Comparison between CB definitions, Medical Research Council (CBMRC), St. George's Respiratory Questionnaire (CBSGRQ), COPD Assessment Test (CBCAT) and CT findings were performed using Cohen's Kappa, univariate and multivariate logistic regressions.

RESULTS

Of 112 participants, 83 met inclusion criteria. Demographics included age of 70.1 ± 7.0 years old, predominantly male (59.0 %), 45.8 ± 30.8 pack-year history, 21.7 % actively smoking, and mean FEV1 61.5 ± 21.1 %. With MRC, SGRQ and CAT definitions, 22.9 %, 36.6 % and 28.0 % had CB, respectively. BD was more often present in CB compared to non-CB patients; however, it did not have a statistically significant relationship between any of the CB definitions. CBSGRQ had better agreement with radiographically assessed BD compared to the other two definitions.

CONCLUSION

Identification of BD on CT was associated with the diagnoses of CB. However, agreement between imaging and definitions were not significant, suggesting radiologic findings of BD and criteria defining CB may not identify the same COPD phenotype. Research to standardize imaging and clinical methods is needed for more objective identification of COPD phenotypes.

Phenotyping of COPD with MRI in comparison to same-day CT in a multi-centre trial

OBJECTIVES

A prospective, multi-centre study to evaluate concordance of morphologic lung MRI and CT in chronic obstructive pulmonary disease (COPD) phenotyping for airway disease and emphysema.

METHODS

A total of 601 participants with COPD from 15 sites underwent same-day morpho-functional chest MRI and paired inspiratory-expiratory CT. Two readers systematically scored bronchial wall thickening, bronchiectasis, centrilobular nodules, air trapping and lung parenchyma defects in each lung lobe and determined COPD phenotype. A third reader acted as adjudicator to establish consensus. Inter-modality and inter-reader agreement were assessed using Cohen's kappa (im-κ and ir-κ).

RESULTS

The mean combined MRI score for bronchiectasis/bronchial wall thickening was 4.5/12 (CT scores, 2.2/12 for bronchiectasis and 6/12 for bronchial wall thickening; im-κ, 0.04-0.3). Expiratory right/left bronchial collapse was observed in 51 and 47/583 on MRI (62 and 57/599 on CT; im-κ, 0.49-0.52). Markers of small airways disease on MRI were 0.15/12 for centrilobular nodules (CT, 0.34/12), 0.94/12 for air trapping (CT, 0.9/12) and 7.6/12 for perfusion deficits (CT, 0.37/12 for mosaic attenuation; im-κ, 0.1-0.41). The mean lung defect score on MRI was 1.3/12 (CT emphysema score, 5.8/24; im-κ, 0.18-0.26). Airway-/emphysema/mixed COPD phenotypes were assigned in 370, 218 and 10 of 583 cases on MRI (347, 218 and 34 of 599 cases on CT; im-κ, 0.63). For all examined features, inter-reader agreement on MRI was lower than on CT.

CONCLUSION

Concordance of MRI and CT for phenotyping of COPD in a multi-centre setting was substantial with variable inter-modality and inter-reader concordance for single diagnostic key features.

CLINICAL RELEVANCE STATEMENT

MRI of lung morphology may well serve as a radiation-free imaging modality for COPD in scientific and clinical settings, given that its potential and limitations as shown here are carefully considered.

KEY POINTS

• In a multi-centre setting, MRI and CT showed substantial concordance for phenotyping of COPD (airway-/emphysema-/mixed-type). • Individual features of COPD demonstrated variable inter-modality concordance with features of pulmonary hypertension showing the highest and bronchiectasis showing the lowest concordance. • For all single features of COPD, inter-reader agreement was lower on MRI than on CT.

How anatomical impairments found on CT affect perfusion percentage assessed by SPECT/CT scan?

AIM

CT images can identify structural and opacity alterations of the lungs while nuclear medicine's lung perfusion studies show the homogeneity (or lack of) of blood perfusion on the organ. Therefore, the use of SPECT/CT in lung perfusion scintigraphies can help physicians to assess anatomical and functional alterations of the lungs and to differentiate between acute and chronic disease.

OBJECTIVE

To develop a computer-aided methodology to quantify the total global perfusion of the lungs via SPECT/CT images and to compare these results with parenchymal alterations obtained in CT images.

METHODS

39 perfusion SPECT/CT images collected retrospectively from the Nuclear Medicine Facility of Botucatu Medical School's Clinics Hospital in São Paulo, Brazil, were analyzed. Anatomical lung impairments (emphysema, collapsed and infiltrated tissue) and the functional percentage of the lungs (blood perfusion) were quantified from CT and SPECT images, with the aid of the free, open-source software 3D Slicer. The results obtained with 3D Slicer (3D-TGP) were also compared to the total global perfusion of each patient's found on their medical report, obtained from visual inspection of planar images (2D-TGP).

RESULTS

This research developed a novel and practical methodology for obtaining lungs' total global perfusion from SPECT/CT images in a semiautomatic manner. 3D-TGP versus 2D-TGP showed a bias of 7% with a variation up to 67% between the two methods. Perfusion percentage showed a weak positive correlation with infiltration (p = 0.0070 and ρ = 0.43) and collapsed parenchyma (p = 0.040 and ρ = 0.33).

CONCLUSIONS

This research brings meaningful contributions to the scientific community because it used a free open-source software to quantify the lungs blood perfusion via SPECT/CT images and pointed that the relationship between parenchyma alterations and the organ's perfusion capability might not be so direct, given compensatory mechanisms.

Artificial intelligence in COPD CT images: identification, staging, and quantitation

Chronic obstructive pulmonary disease (COPD) stands as a significant global health challenge, with its intricate pathophysiological manifestations often demanding advanced diagnostic strategies. The recent applications of artificial intelligence (AI) within the realm of medical imaging, especially in computed tomography, present a promising avenue for transformative changes in COPD diagnosis and management. This review delves deep into the capabilities and advancements of AI, particularly focusing on machine learning and deep learning, and their applications in COPD identification, staging, and imaging phenotypes. Emphasis is laid on the AI-powered insights into emphysema, airway dynamics, and vascular structures. The challenges linked with data intricacies and the integration of AI in the clinical landscape are discussed. Lastly, the review casts a forward-looking perspective, highlighting emerging innovations in AI for COPD imaging and the potential of interdisciplinary collaborations, hinting at a future where AI doesn't just support but pioneers breakthroughs in COPD care. Through this review, we aim to provide a comprehensive understanding of the current state and future potential of AI in shaping the landscape of COPD diagnosis and management.

Artificial Intelligence in Chronic Obstructive Pulmonary Disease: Research Status, Trends, and Future Directions --A Bibliometric Analysis from 2009 to 2023

Objective

A bibliometric analysis was conducted using VOSviewer and CiteSpace to examine studies published between 2009 and 2023 on the utilization of artificial intelligence (AI) in chronic obstructive pulmonary disease (COPD).

Methods

On March 24, 2024, a computer search was conducted on the Web of Science (WOS) core collection dataset published between January 1, 2009, and December 30, 2023, to identify literature related to the application of artificial intelligence in chronic obstructive pulmonary disease (COPD). VOSviewer was utilized for visual analysis of countries, institutions, authors, co-cited authors, and keywords. CiteSpace was employed to analyze the intermediary centrality of institutions, references, keyword outbreaks, and co-cited literature. Relevant descriptive analysis tables were created using Excel2021 software.

Results

This study included a total of 646 papers from WOS. The number of papers remained small and stable from 2009 to 2017 but started increasing significantly annually since 2018. The United States had the highest number of publications among countries/regions while Silverman Edwin K and Harvard Medical School were the most prolific authors and institutions respectively. Lynch DA, Kirby M. and Vestbo J. were among the top three most cited authors overall. Scientific Reports had the largest number of publications while Radiology ranked as one of the top ten influential journals. The Genetic Epidemiology of COPD (COPDGene) Study Design was frequently cited. Through keyword clustering analysis, all keywords were categorized into four groups: epidemiological study of COPD; AI-assisted imaging diagnosis; AI-assisted diagnosis; and AI-assisted treatment and prognosis prediction in the COPD research field. Currently, hot research topics include explainable artificial intelligence framework, chest CT imaging, and lung radiomics.

Conclusion

At present, AI is predominantly employed in genetic biology, early diagnosis, risk staging, efficacy evaluation, and prediction modeling of COPD. This study's results offer novel insights and directions for future research endeavors related to COPD.

Improved diagnostic yield of peripheral pulmonary malignant lesions with emphysema using a combination of radial endobronchial ultrasonography and rapid on-site evaluation

BACKGROUND

This is a retrospective cohort study from a single center of Chest Medical District of Nanjing Brain Hospital Affiliated to Nanjing Medical University, Jiangsu Province, China. It was aim to evaluate the diagnostic value of radial endobronchial ultrasound (R-EBUS) combination with rapid on-site evaluation (ROSE) guided transbronchial lung biopsy (TBLB) for peripheral pulmonary lesions in patients with emphysema.

METHODS

All 170 patients who underwent PPLs with emphysema received an R-EBUS examination with or without the ROSE procedure, and the diagnostic yield, safety, and possible factors influencing diagnosis were analyzed between the two groups by the SPSS 25.0 software.

RESULTS

The pooled and benign diagnostic yields were not different in the two groups (P = 0.224, 0.924), but the diagnostic yield of malignant PPLs was significantly higher in the group with ROSE than the group without ROSE (P = 0.042). The sensitivity of ROSE was 79.10%, the specificity, 91.67%, the positive predictive value, 98.15%, and the negative predictive value, 84.62%. The diagnostic accuracy, was 95.52%. In the group of R-EBUS + ROSE, the procedural time and the number of times of biopsy or brushing were both significantly reduced (all P<0.05). The incidence of pneumothorax (1.20%) and bleeding (10.84%) in the group of R-EBUS + ROSE were also less than those in the group of R-EBUS (P<0.05). The lesion's diameter ≥ 2 cm, the distance between the pleura and the lesion ≥ 2 cm, the positive air bronchograms sign, the location of the ultrasound probe within the lesion, and the even echo with clear margin feature of lesion ultrasonic image, these factors are possibly relevant to a higher diagnostic yield. The diagnostic yield of PPLs those were adjacent to emphysema were lower than those PPLs which were away from emphysema (P = 0.048) in the group without ROSE, however, in the group of R-EBUS + ROSE, there was no such difference whether the lesion is adjacent to emphysema or not (P = 0.236).

CONCLUSION

Our study found that the combination of R-EBUS and ROSE during bronchoscopy procedure was a safe and effective modality to improve diagnostic yield of PPLs with emphysema, especially for malignant PPLs. The distance between the pleura and the lesion ≥ 2 cm, the positive air bronchograms sign, the location of the ultrasound probe within the lesion, and the even echo with clear margin feature of lesion ultrasonic image, these factors possibly indicated a higher diagnostic yield. Those lesions' position is adjacent to emphysema may reduce diagnostic yield but ROSE may make up for this deficiency.

Microscopic Small Airway Abnormalities Identified in Early Idiopathic Pulmonary Fibrosis In Vivo Using Endobronchial Optical Coherence Tomography

Rationale: Idiopathic pulmonary fibrosis (IPF) affects the subpleural lung but is considered to spare small airways. Micro-computed tomography (micro-CT) studies demonstrated small airway reduction in end-stage IPF explanted lungs, raising questions about small airway involvement in early-stage disease. Endobronchial optical coherence tomography (EB-OCT) is a volumetric imaging modality that detects microscopic features from subpleural to proximal airways. Objectives: In this study, EB-OCT was used to evaluate small airways in early IPF and control subjects in vivo. Methods: EB-OCT was performed in 12 subjects with IPF and 5 control subjects (matched by age, sex, smoking history, height, and body mass index). Subjects with IPF had early disease with mild restriction (FVC: 83.5% predicted), which was diagnosed per current guidelines and confirmed by surgical biopsy. EB-OCT volumetric imaging was acquired bronchoscopically in multiple, distinct, bilateral lung locations (total: 97 sites). IPF imaging sites were classified by severity into affected (all criteria for usual interstitial pneumonia present) and less affected (some but not all criteria for usual interstitial pneumonia present). Bronchiole count and small airway stereology metrics were measured for each EB-OCT imaging site. Measurements and Main Results: Compared with the number of bronchioles in control subjects (mean = 11.2/cm3; SD = 6.2), there was significant bronchiole reduction in subjects with IPF (42% loss; mean = 6.5/cm3; SD = 3.4; P = 0.0039), including in IPF affected (48% loss; mean: 5.8/cm3; SD: 2.8; P < 0.00001) and IPF less affected (33% loss; mean: 7.5/cm3; SD: 4.1; P = 0.024) sites. Stereology metrics showed that IPF-affected small airways were significantly larger, more distorted, and more irregular than in IPF-less affected sites and control subjects. IPF less affected and control airways were statistically indistinguishable for all stereology parameters (P = 0.36-1.0). Conclusions: EB-OCT demonstrated marked bronchiolar loss in early IPF (between 30% and 50%), even in areas minimally affected by disease, compared with matched control subjects. These findings support small airway disease as a feature of early IPF, providing novel insight into pathogenesis and potential therapeutic targets.

An underrecognized phenotype of pulmonary emphysema with marked pulmonary gas exchange but with mild or moderate airway obstruction

In patients with pulmonary emphysema and mild to moderate airflow limitation, one does not expect the features marked exertional dyspnea and hypoxemia as well as a profound decrease in diffusing capacity of the lung for carbon monoxide (DLCO). Here we describe this phenotype and its prognosis. From our database, we retrospectively selected cases associating emphysema, exertional breathlessness, O2 requirement at least upon exercise, forced expiratory volume in 1 sec (FEV1) ≥ 50% predicted, and DLCO ≤ 50% predicted, without associated combined pulmonary fibrosis and emphysema, right-to-left shunt, or severe pulmonary hypertension. Over a 12-year period, we identified 16 patients with emphysema and the above presentation. At the initial evaluation, the median age was 62 years (interquartile range 53.8-68.9). The median FEV1 and DLCO% predicted and mean pulmonary artery pressure were 86 (65-95)%, 38 (31-41)%, and 20 (17-25) mm Hg, respectively. On room air, the median arterial partial pressure of oxygen and partial pressure of carbon dioxide in arterial blood were 63.5 (55.8-69) mm Hg and 34.5 (31-36) mm Hg with increased median alveolar-arterial oxygen difference (46 [39-51] mm Hg). After the initial evaluation, the respiratory condition worsened in 13 of 14 (92.8%) patients with one or more re-evaluations (median follow-up 2.6 [0.9-5.8] years). In 12, lung transplantation was considered. Four patients died after 5.8, 5.7, 7.1, and 0.8 years of follow-up, respectively. We describe an underrecognized phenotype of pulmonary emphysema featuring a particular profile characterized by marked exertional dyspnea, impaired pulmonary gas exchange with low DLCO and marked oxygen desaturation at least on exercise but with mild or moderate airway obstruction.

Clinical features and associated factors of impaired ventilatory efficiency: findings from the ECOPD study in China

BACKGROUND

Impaired ventilatory efficiency during exercise is a predictor of mortality in chronic obstructive pulmonary disease. However, little is known about the clinical features and associated factors of impaired ventilatory efficiency in China.

METHODS

We conducted a cross-sectional community-based study in China and collected demographic and clinical information, cardiopulmonary exercise testing, spirometry, and CT data. Impaired ventilatory efficiency was defined by a nadir ventilatory equivalent for CO2 production above the upper limit of normal. Multivariable linear and logistic regression models were used to explore the clinical features and associated factors of impaired ventilatory efficiency.

RESULTS

The final analyses included 941 subjects, 702 (74.6%) of whom had normal ventilatory efficiency and 239 (25.4%) had impaired ventilatory efficiency. Participants with impaired ventilatory efficiency had more chronic respiratory symptoms, poorer lung function and exercise capacity, and more severe emphysema (natural logarithm transformation of the low-attenuation area of the lung with attenuation values below -950 Hounsfield units, logLAA-950: 0.19±0.65 vs -0.28±0.63, p<0.001) and air trapping (logLAA-856: 1.03±0.65 vs 0.68±0.70, p<0.001) than those with normal ventilatory efficiency. Older age (60-69 years, OR 3.10 (95% CI 1.33 to 7.21), p=0.009 and 70-80 years, OR 6.48 (95% CI 2.56 to 16.43), p<0.001 vs 40-49 years) and smoking (former, OR 3.19 (95% CI 1.29 to 7.86), p=0.012; current, OR 4.27 (95% CI 1.78 to 10.24), p=0.001 vs never) were identified as high risk factors of impaired ventilatory efficiency.

CONCLUSIONS

Impaired ventilatory efficiency was associated with poorer respiratory characteristics. Longitudinal studies are warranted to explore the progression of individuals with impaired ventilatory efficiency.

The link between smoking, emphysema, and fibrosis: A retrospective cohort study

INTRODUCTION

The presence of emphysema is common in patients with interstitial lung disease (ILD), which is designated as combined pulmonary fibrosis and emphysema (CPFE). This study aimed to examine the association between smoking, emphysema, and fibrosis in ILD patients.

METHODS

A total of 800 patients hospitalized for ILD at the affiliated hospital of Qingdao University, Shandong, Qingdao, China, from December 2012 to December 2020 were included in our retrospective cohort study. Participants were divided into CPFE and non-CPFE groups. The patients' clinical presentations and radiographic and laboratory findings were reviewed and compared. The two groups were then divided and compared based on smoking status. Kaplan-Meier survival analysis with log-rank testing and multivariable Cox proportional hazards regression analysis were used to compare all-cause mortality.

RESULTS

Emphysema was present in 188 (23.5%) ILD patients. Smoking was associated with increased odds of CPFE (adjusted odds ratio, AOR=2.13; 95% CI: 1.33-3.41, p=0.002). The CPFE patients had a comparable risk of death to non-CPFE patients (adjusted hazard ratio, AHR=0.89; 95% CI: 0.64-1.24, p=0.493). Smoking was not a risk prognostic factor in the whole group (AHR=1.34; 95% CI: 0.90-1.99, p=0.152) or the CPFE group (AHR=0.90; 95% CI: 0.43-1.86, p=0.771). However, a significant prognostic difference between smokers and non-smokers was found in the non-CPFE group (AHR=1.62; 95% CI: 1.02-2.58, p=0.042). In ILD patients, smoking pack-years were weakly correlated with total centrilobular emphysema (CLE) scores and total fibrosis scores (TFS), but not with total emphysema scores (TES); TFS were weakly correlated with TES.

CONCLUSIONS

CPFE did not affect the prognosis of ILD. Smoking was a risk but not a prognostic factor for CPFE. However, smoking was associated with worse survival in non-CPFE patients. There was an intricate association among smoking, emphysema, and fibrosis in ILD patients.

Safety and efficacy of tract embolization using gelatin sponge particles in reducing pneumothorax after CT-guided percutaneous lung biopsy in patients with emphysema

BACKGROUND

The incidence of pneumothorax is higher in patients with emphysema who undergo percutaneous lung biopsy. Needle embolization has been shown to reduce the incidence of pneumothorax in patients with emphysema. Existing studies have reported small sample sizes of patients with emphysema, or the degree of emphysema has not been graded. Therefore, the efficacy of biopsy embolization in the prevention of pneumothorax induced by percutaneous pulmonary biopsy in patients with emphysema remains to be determined.

METHODS

In this retrospective, controlled study, patients with emphysema who underwent CT-guided PTLB were divided into two groups: group A (n = 523), without tract embolization, and Group B (n = 504), with tract embolization. Clinical and imaging features were collected from electronic medical records and Picture Archiving and Communication Systems. Univariate and multivariate analyses were performed to identify risk factors for pneumothorax and chest tube placement.

RESULTS

The two groups did not differ significantly in terms of demographic characteristics and complications other than pneumothorax. The incidence of pneumothorax and chest tube placement in group B was significantly lower than in group A (20.36% vs. 46.12%, p < 0.001; 3.95% vs. 9.18%, p < 0.001, respectively). In logistic regression analyses, variables affecting the incidence of pneumothorax and chest tube placement were the length of puncture of the lung parenchyma (odds ratio [OR] = 1.18, 95% confidence interval [CI]: 1.07-1.30, p = 0.001; OR = 1.55, 95% CI: 1.30-1.85, p < 0.001, respectively), tract embolization (OR = 0.31, 95% CI: 0.24-0.41, p < 0.001; OR = 0.39, 95% CI: 0.22-0.69, p = 0.001, respectively), and grade of emphysema.

CONCLUSIONS

Tract embolization with gelatin sponge particles after CT-guided PTLB significantly reduced the incidence of pneumothorax and chest tube placement in patients with emphysema. Tract embolization, length of puncture of the lung parenchyma, and grade of emphysema were independent risk factors for pneumothorax and chest tube placement.

TRIAL REGISTRATION

Retrospectively registered.

Is Type and Grade of Emphysema Important for Bone Mineral Density and Aortic Calcifications?

Background: Chronic obstructive pulmonary disease has extrapulmonary manifestations, such as cardiovascular diseases and osteoporosis. The purpose of this research was to determine the relationship between the type and extent of emphysema with thoracic aorta calcification (TAC) and bone mineral density (BMD) at Th4, Th8, and L1 vertebrae. Methods: Emphysema was described by computed tomography parameters (both Fleischner classification and low attenuation value percentage, LAV%) and the clinical FEV1/FVC ratio (Tiffeneau-Pinelli index, TI, TI < 0.7; TI > 0.7). Results: Of 200 included patients (median age 64, 33% women), signs of clinical obstruction (TI) were observed in 104 patients, which had significantly lower BMD and more heavy TAC. BMD correlated negatively with LAV%, Rho = -0.16 to -0.23, while a positive correlation of aortic calcification with LAV% was observed, Rho = 0.30 to 0.33. Multiple linear regression showed that age and TI < 0.7 were independent predictors of BMD, β = -0.20 to -0.40, and β = -0.21 to -0.25; age and hypercholesterolemia were independent predictors of TCA, β = 0.61 and β = 0.19. Conclusions: Clinical TI and morphological LAV% parameters correlated with BMD and TAC, in contrast to Fleischer-graded emphysema, which showed no correlation. However, only TI was an independent predictor of BMD, while the morphologically described type and extent of emphysema could not independently predict any extrapulmonary manifestation.

Ct-defined emphysema prevalence in a Chinese and Dutch general population

PURPOSE

We determine and compare the prevalence, subtypes, severity, and risk factors for emphysema assessed by low-dose CT(LDCT) in Chinese and Dutch general populations.

METHODS

This cross-sectional study included LDCT scans of 1143 participants between May and October 2017 from a Chinese Cohort study and 1200 participants with same age range and different smoking status between May and October 2019 from a Dutch population-based study. An experienced radiologist visually assessed the scans for emphysema presence (≥trace), subtype, and severity. Logistic regression analyses, overall and stratified by smoking status, were performed and adjusted for fume exposure, demographic and smoking data.

RESULTS

The Chinese population had a comparable proportion of women to the Dutch population (54.9 % vs 58.9 %), was older (61.7 ± 6.3 vs 59.8 ± 8.1), included more never smokers (66.4 % vs 38.3 %), had a higher emphysema prevalence ([58.8 % vs 39.7 %], adjusted odds ratio, aOR = 2.06, 95 %CI = 1.68-2.53), and more often had centrilobular emphysema (54.8 % vs 32.8 %, p < 0.001), but no differences in emphysema severity. After stratification, only in never smokers an increased odds of emphysema was observed in the Chinese compared to the Dutch (aOR = 2.55, 95 %CI = 1.95-3.35). Never smokers in both populations shared older age (aOR = 1.59, 95 %CI = 1.25-2.02 vs 1.26, 95 %CI = 0.97-1.64) and male sex (aOR = 1.50, 95 %CI = 1.02-2.22 vs 1.93, 95 %CI = 1.26-2.96) as risk factors for emphysema.

CONCLUSIONS

Only never smokers had a higher prevalence of mainly centrilobular emphysema in the Chinese general population compared to the Dutch after adjusting for confounders, indicating that factors other than smoking, age and sex contribute to presence of CT-defined emphysema.

[Using Artificial Intelligence Software for Diagnosing Emphysema and Interstitial Lung Disease]

Researchers have developed various algorithms utilizing artificial intelligence (AI) to automatically and objectively diagnose patterns and extent of pulmonary emphysema or interstitial lung diseases on chest CT scans. Studies show that AI-based quantification of emphysema on chest CT scans reveals a connection between an increase in the relative percentage of emphysema and a decline in lung function. Notably, quantifying centrilobular emphysema has proven helpful in predicting clinical symptoms or mortality rates of chronic obstructive pulmonary disease. In the context of interstitial lung diseases, AI can classify the usual interstitial pneumonia pattern on CT scans into categories like normal, ground-glass opacity, reticular opacity, honeycombing, emphysema, and consolidation. This classification accuracy is comparable to chest radiologists (70%-80%). However, the results generated by AI are influenced by factors such as scan parameters, reconstruction algorithms, radiation doses, and the training data used to develop the AI. These limitations currently restrict the widespread adoption of AI for quantifying pulmonary emphysema and interstitial lung diseases in daily clinical practice. This paper will showcase the authors' experience using AI for diagnosing and quantifying emphysema and interstitial lung diseases through case studies. We will primarily focus on the advantages and limitations of AI for these two diseases.

Enhancing COPD classification using combined quantitative computed tomography and texture-based radiomics: a CanCOLD cohort study

Background

Recent advances in texture-based computed tomography (CT) radiomics have demonstrated its potential for classifying COPD.

Methods

Participants from the Canadian Cohort Obstructive Lung Disease (CanCOLD) study were evaluated. A total of 108 features were included: eight quantitative CT (qCT), 95 texture-based radiomic and five demographic features. Machine-learning models included demographics along with texture-based radiomics and/or qCT. Combinations of five feature selection and five classification methods were evaluated; a training dataset was used for feature selection and to train the models, and a testing dataset was used for model evaluation. Models for classifying COPD status and severity were evaluated using the area under the receiver operating characteristic curve (AUC) with DeLong's test for comparison. SHapely Additive exPlanations (SHAP) analysis was used to investigate the features selected.

Results

A total of 1204 participants were evaluated (n=602 no COPD; n=602 COPD). There were no differences between the groups for sex (p=0.77) or body mass index (p=0.21). For classifying COPD status, the combination of demographics, texture-based radiomics and qCT performed better (AUC=0.87) than the combination of demographics and texture-based radiomics (AUC=0.81, p<0.05) or qCT alone (AUC=0.84, p<0.05). Similarly, for classifying COPD severity, the combination of demographics, texture-based radiomics and qCT performed better (AUC=0.81) than demographics and texture-based radiomics (AUC=0.72, p<0.05) or qCT alone (AUC=0.79, p<0.05). Texture-based radiomics and qCT features were among the top five features selected (15th percentile of the CT density histogram, CT total airway count, pack-years, CT grey-level distance zone matrix zone distance entropy, CT low-attenuation clusters) for classifying COPD status.

Conclusion

Texture-based radiomics and conventional qCT features in combination improve machine‑learning models for classification of COPD status and severity.

Expiratory Venous Volume and Arterial Tortuosity are Associated with Disease Severity and Mortality Risk in Patients with COPD: Results from COSYCONET

Purpose

The aim of this study was to evaluate the association between computed tomography (CT) quantitative pulmonary vessel morphology and lung function, disease severity, and mortality risk in patients with chronic obstructive pulmonary disease (COPD).

Patients and Methods

Participants of the prospective nationwide COSYCONET cohort study with paired inspiratory-expiratory CT were included. Fully automatic software, developed in-house, segmented arterial and venous pulmonary vessels and quantified volume and tortuosity on inspiratory and expiratory scans. The association between vessel volume normalised to lung volume and tortuosity versus lung function (forced expiratory volume in 1 sec [FEV1]), air trapping (residual volume to total lung capacity ratio [RV/TLC]), transfer factor for carbon monoxide (TLCO), disease severity in terms of Global Initiative for Chronic Obstructive Lung Disease (GOLD) group D, and mortality were analysed by linear, logistic or Cox proportional hazard regression.

Results

Complete data were available from 138 patients (39% female, mean age 65 years). FEV1, RV/TLC and TLCO, all as % predicted, were significantly (p < 0.05 each) associated with expiratory vessel characteristics, predominantly venous volume and arterial tortuosity. Associations with inspiratory vessel characteristics were absent or negligible. The patterns were similar for relationships between GOLD D and mortality with vessel characteristics. Expiratory venous volume was an independent predictor of mortality, in addition to FEV1.

Conclusion

By using automated software in patients with COPD, clinically relevant information on pulmonary vasculature can be extracted from expiratory CT scans (although not inspiratory scans); in particular, expiratory pulmonary venous volume predicted mortality.

Trial Registration

NCT01245933.

Learning and depicting lobe-based radiomics feature for COPD Severity staging in low-dose CT images

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is a prevalent and debilitating respiratory condition that imposes a significant healthcare burden worldwide. Accurate staging of COPD severity is crucial for patient management and treatment planning.

METHODS

The retrospective study included 530 hospital patients. A lobe-based radiomics method was proposed to classify COPD severity using computed tomography (CT) images. First, we segmented the lung lobes with a convolutional neural network model. Secondly, the radiomic features of each lung lobe are extracted from CT images, the features of the five lung lobes are merged, and the selection of features is accomplished through the utilization of a variance threshold, t-Test, least absolute shrinkage and selection operator (LASSO). Finally, the COPD severity was classified by a support vector machine (SVM) classifier.

RESULTS

104 features were selected for staging COPD according to the Global initiative for chronic Obstructive Lung Disease (GOLD). The SVM classifier showed remarkable performance with an accuracy of 0.63. Moreover, an additional set of 132 features were selected to distinguish between milder (GOLD I + GOLD II) and more severe instances (GOLD III + GOLD IV) of COPD. The accuracy for SVM stood at 0.87.

CONCLUSIONS

The proposed method proved that the novel lobe-based radiomics method can significantly contribute to the refinement of COPD severity staging. By combining radiomic features from each lung lobe, it can obtain a more comprehensive and rich set of features and better capture the CT radiomic features of the lung than simply observing the lung as a whole.

Machine Learning and CT Texture Features in Ex-smokers with no CT Evidence of Emphysema and Mildly Abnormal Diffusing Capacity

RATIONALE AND OBJECTIVES

Ex-smokers without spirometry or CT evidence of chronic obstructive pulmonary disease (COPD) but with mildly abnormal diffusing capacity of the lungs for carbon monoxide (DLCO) are at higher risk of developing COPD. It remains difficult to make clinical management decisions for such ex-smokers without other objective assessments consistent with COPD. Hence, our objective was to develop a machine-learning and CT texture-analysis pipeline to dichotomize ex-smokers with normal and abnormal DLCO (DLCO≥75%pred and DLCO<75%pred).

MATERIALS AND METHODS

In this retrospective study, 71 ex-smokers (50-85yrs) without COPD underwent spirometry, plethysmography, thoracic CT, and 3He MRI to generate ventilation defect percent (VDP) and apparent diffusion coefficients (ADC). PyRadiomics was utilized to extract 496 CT texture-features; Boruta and principal component analysis were used for feature selection and various models were investigated for classification. Machine-learning classifiers were evaluated using area under the receiver operator characteristic curve (AUC), sensitivity, specificity, and F1-measure.

RESULTS

Of 71 ex-smokers without COPD, 29 with mildly abnormal DLCO had significantly different MRI ADC (p < .001), residual-volume to total-lung-capacity ratio (p = .003), St. George's Respiratory Questionnaire (p = .029), and six-minute-walk distance (6MWD) (p < .001), but similar relative area of the lung < -950 Hounsfield-units (RA950) (p = .9) compared to 42 ex-smokers with normal DLCO. Logistic-regression machine-learning mixed-model trained on selected texture-features achieved the best classification accuracy of 87%. All clinical and imaging measurements were outperformed by high-high-pass filter high-gray-level-run-emphasis texture-feature (AUC=0.81), which correlated with DLCO (ρ = -0.29, p = .02), MRI ADC (ρ = 0.23, p = .048), and 6MWD (ρ = -0.25, p = .02).

CONCLUSION

In ex-smokers with no CT evidence of emphysema, machine-learning models exclusively trained on CT texture-features accurately classified ex-smokers with abnormal diffusing capacity, outperforming conventional quantitative CT measurements.

COPD stage detection: leveraging the auto-metric graph neural network with inspiratory and expiratory chest CT images

Chronic obstructive pulmonary disease (COPD) is a common lung disease that can lead to restricted airflow and respiratory problems, causing a significant health, economic, and social burden. Detecting the COPD stage can provide a timely warning for prompt intervention in COPD patients. However, existing methods based on inspiratory (IN) and expiratory (EX) chest CT images are not sufficiently accurate and efficient in COPD stage detection. The lung region images are autonomously segmented from IN and EX chest CT images to extract the 1 , 781 × 2 lung radiomics and 13 , 824 × 2 3D CNN features. Furthermore, a strategy for concatenating and selecting features was employed in COPD stage detection based on radiomics and 3D CNN features. Finally, we combine all the radiomics, 3D CNN features, and factor risks (age, gender, and smoking history) to detect the COPD stage based on the Auto-Metric Graph Neural Network (AMGNN). The AMGNN with radiomics and 3D CNN features achieves the best performance at 89.7 % of accuracy, 90.9 % of precision, 89.5 % of F1-score, and 95.8 % of AUC compared to six classic machine learning (ML) classifiers. Our proposed approach demonstrates high accuracy in detecting the stage of COPD using both IN and EX chest CT images. This method can potentially establish an efficient diagnostic tool for patients with COPD. Additionally, we have identified radiomics and 3D CNN as more appropriate biomarkers than Parametric Response Mapping (PRM). Moreover, our findings indicate that expiration yields better results than inspiration in detecting the stage of COPD.

Smoking-Related Interstitial Lung Disease and Emphysema

Diagnosis and treatment of patients with smoking-related lung diseases often requires multidisciplinary contributions to optimize care. Imaging plays a key role in characterizing the underlying disease, quantifying its severity, identifying potential complications, and directing management. The primary goal of this article is to provide an overview of the imaging findings and distinguishing features of smoking-related lung diseases, specifically, emphysema/chronic obstructive pulmonary disease, respiratory bronchiolitis-interstitial lung disease, smoking-related interstitial fibrosis, desquamative interstitial pneumonitis, combined pulmonary fibrosis and emphysema, pulmonary Langerhans cell histiocytosis, and E-cigarette or vaping related lung injury.

Clinical Variables and Radiomics Features for Predicting Pneumothorax in Patients Undergoing CT-guided Transthoracic Core Needle Biopsy

Purpose To develop a prediction model combining both clinical and CT texture analysis radiomics features for predicting pneumothorax complications in patients undergoing CT-guided core needle biopsy. Materials and Methods A total of 424 patients (mean age, 65.6 years ± 12.7 [SD]; 232 male, 192 female) who underwent CT-guided core needle biopsy between January 2021 and October 2022 were retrospectively included as the training data set. Clinical and procedure-related characteristics were documented. Texture analysis radiomics features were extracted from the subpleural lung parenchyma traversed by needle. Moderate pneumothorax was defined as a postprocedure air rim of 2 cm or greater. The prediction model was developed using logistic regression with backward elimination, presented by linear fusion of the selected features weighted by their coefficients. Model performance was assessed using the area under the receiver operating characteristic curve (AUC). Validation was conducted in an external cohort (n = 45; mean age, 58.2 years ± 12.7; 19 male, 26 female) from a different hospital. Results Moderate pneumothorax occurred in 12.0% (51 of 424) of the training cohort and 8.9% (four of 45) of the external test cohort. Patients with emphysema (P < .001) or a longer needle path length (P = .01) exhibited a higher incidence of moderate pneumothorax in the training cohort. Texture analysis features, including gray-level co-occurrence matrix cluster shade (P < .001), gray-level run-length matrix low gray-level run emphasis (P = .049), gray-level run-length matrix run entropy (P = .003), gray-level size-zone matrix gray-level variance (P < .001), and neighboring gray-tone difference matrix complexity (P < .001), showed higher values in patients with moderate pneumothorax. The combined clinical-radiomics model demonstrated satisfactory performance in both the training (AUC 0.78, accuracy = 71.9%) and external test cohorts (AUC 0.86, accuracy 73.3%). Conclusion The model integrating both clinical and radiomics features offered practical diagnostic performance and accuracy for predicting moderate pneumothorax in patients undergoing CT-guided core needle biopsy. Keywords: Biopsy/Needle Aspiration, Thorax, CT, Pneumothorax, Core Needle Biopsy, Texture Analysis, Radiomics, CT Supplemental material is available for this article. © RSNA, 2024.

Effect of emphysema on AI software and human reader performance in lung nodule detection from low-dose chest CT

BACKGROUND

Emphysema influences the appearance of lung tissue in computed tomography (CT). We evaluated whether this affects lung nodule detection by artificial intelligence (AI) and human readers (HR).

METHODS

Individuals were selected from the "Lifelines" cohort who had undergone low-dose chest CT. Nodules in individuals without emphysema were matched to similar-sized nodules in individuals with at least moderate emphysema. AI results for nodular findings of 30-100 mm3 and 101-300 mm3 were compared to those of HR; two expert radiologists blindly reviewed discrepancies. Sensitivity and false positives (FPs)/scan were compared for emphysema and non-emphysema groups.

RESULTS

Thirty-nine participants with and 82 without emphysema were included (n = 121, aged 61 ± 8 years (mean ± standard deviation), 58/121 males (47.9%)). AI and HR detected 196 and 206 nodular findings, respectively, yielding 109 concordant nodules and 184 discrepancies, including 118 true nodules. For AI, sensitivity was 0.68 (95% confidence interval 0.57-0.77) in emphysema versus 0.71 (0.62-0.78) in non-emphysema, with FPs/scan 0.51 and 0.22, respectively (p = 0.028). For HR, sensitivity was 0.76 (0.65-0.84) and 0.80 (0.72-0.86), with FPs/scan of 0.15 and 0.27 (p = 0.230). Overall sensitivity was slightly higher for HR than for AI, but this difference disappeared after the exclusion of benign lymph nodes. FPs/scan were higher for AI in emphysema than in non-emphysema (p = 0.028), while FPs/scan for HR were higher than AI for 30-100 mm3 nodules in non-emphysema (p = 0.009).

CONCLUSIONS

AI resulted in more FPs/scan in emphysema compared to non-emphysema, a difference not observed for HR.

RELEVANCE STATEMENT

In the creation of a benchmark dataset to validate AI software for lung nodule detection, the inclusion of emphysema cases is important due to the additional number of FPs.

KEY POINTS

• The sensitivity of nodule detection by AI was similar in emphysema and non-emphysema. • AI had more FPs/scan in emphysema compared to non-emphysema. • Sensitivity and FPs/scan by the human reader were comparable for emphysema and non-emphysema. • Emphysema and non-emphysema representation in benchmark dataset is important for validating AI.

Advances in COPD imaging using CT and MRI: linkage with lung physiology and clinical outcomes

Recent years have witnessed major advances in lung imaging in patients with COPD. These include significant refinements in images obtained by computed tomography (CT) scans together with the introduction of new techniques and software that aim for obtaining the best image whilst using the lowest possible radiation dose. Magnetic resonance imaging (MRI) has also emerged as a useful radiation-free tool in assessing structural and more importantly functional derangements in patients with well-established COPD and smokers without COPD, even before the existence of overt changes in resting physiological lung function tests. Together, CT and MRI now allow objective quantification and assessment of structural changes within the airways, lung parenchyma and pulmonary vessels. Furthermore, CT and MRI can now provide objective assessments of regional lung ventilation and perfusion, and multinuclear MRI provides further insight into gas exchange; this can help in structured decisions regarding treatment plans. These advances in chest imaging techniques have brought new insights into our understanding of disease pathophysiology and characterising different disease phenotypes. The present review discusses, in detail, the advances in lung imaging in patients with COPD and how structural and functional imaging are linked with common resting physiological tests and important clinical outcomes.

Clinical utilization of airway inflammatory biomarkers in the prediction and monitoring of clinical outcomes in patients with chronic obstructive pulmonary disease

INTRODUCTION

Chronic obstructive pulmonary disease (COPD) accounts for 545 million people living with chronic respiratory disorders and is the third leading cause of morbidity and mortality around the world. COPD is a progressive disease, characterized by episodes of acute worsening of symptoms such as cough, dyspnea, and sputum production.

AREAS COVERED

Airway inflammation is a prominent feature of COPD. Chronic airway inflammation results in airway structural remodeling and emphysema. Persistent airway inflammation is a treatable trait of COPD and plays a significant role in disease development and progression. In this review, the authors summarize the current and emerging biomarkers that reveal the heterogeneity of airway inflammation subtypes, clinical outcomes, and therapeutic response in COPD.

EXPERT OPINION

Airway inflammation can be broadly categorized as eosinophilic (type 2 inflammation) and non-eosinophilic (non-type 2 inflammation) in COPD. Currently, blood eosinophil counts are incorporated in clinical practice guidelines to identify COPD patients who are at a higher risk of exacerbations and lung function decline, and who are likely to respond to inhaled corticosteroids. As new therapeutics are being developed for the chronic management of COPD, it is essential to identify biomarkers that will predict treatment response.

Characteristics and outcomes of gemcitabine-associated pulmonary hypertension

Background

Despite its known cardiac and lung toxicities, the chemotherapy drug gemcitabine has only rarely been associated with pulmonary hypertension (PH), and the underlying mechanism remains unclear. The objective of the present study was to assess the association between gemcitabine and PH.

Methods

We identified incident cases of precapillary PH confirmed by right heart catheterisation in patients treated with gemcitabine from the French PH Registry between January 2007 and December 2022. The aetiology, clinical, functional, radiological and haemodynamic characteristics of PH were reviewed at baseline and during follow-up. A pharmacovigilance disproportionality analysis was conducted using the World Health Organization (WHO) pharmacovigilance database.

Results

We identified nine cases of pulmonary arterial hypertension, either induced (in eight patients) or exacerbated (in one patient) by gemcitabine. Patients exhibited severe precapillary PH, with a median mean pulmonary arterial pressure of 40 (range 26-47) mmHg, a cardiac index of 2.4 (1.6-3.9) L·min-1·m-2 and a pulmonary vascular resistance of 6.3 (3.1-12.6) Wood units. The median time from the initiation of gemcitabine to the onset of PH was 7 (4-50) months, with patients receiving a median of 16 (6-24) gemcitabine injections. Six patients showed clinical improvement upon discontinuation of gemcitabine. In the WHO pharmacovigilance database, we identified a significant signal with 109 cases reporting at least one adverse event related to PH with gemcitabine.

Conclusion

Both clinical cases and pharmacovigilance data substantiate a significant association between gemcitabine use and the onset or worsening of precapillary PH. The observed improvement following the discontinuation of treatment underscores the importance of PH screening in gemcitabine-exposed patients experiencing unexplained dyspnoea.

Artificial Intelligence-Based Emphysema Quantification in Routine Chest Computed Tomography: Correlation With Spirometry and Visual Emphysema Grading

OBJECTIVE

The aim of the study is to assess the correlation between artificial intelligence (AI)-based low attenuation volume percentage (LAV%) with forced expiratory volume in the first second to forced vital capacity (FEV1/FVC) and visual emphysema grades in routine chest computed tomography (CT). Furthermore, optimal LAV% cutoff values for predicting a FEV1/FVC < 70% or moderate to more extensive visual emphysema grades were calculated.

METHODS

In a retrospective study of 298 consecutive patients who underwent routine chest CT and spirometry examinations, LAV% was quantified using an AI-based software with a threshold < -950 HU. The FEV1/FVC was derived from spirometry, with FEV1/FVC < 70% indicating airway obstruction. The mean time interval of CT from spirometry was 3.87 ± 4.78 days. Severity of emphysema was visually graded by an experienced chest radiologist using an established 5-grade ordinal scale (Fleischner Society classification system). Spearman correlation coefficient between LAV% and FEV1/FVC was calculated. Receiver operating characteristic determined the optimal LAV% cutoff values for predicting a FEV1/FVC < 70% or a visual emphysema grade of moderate or higher (Fleischner grade 3-5).

RESULTS

Significant correlation between LAV% and FEV1/FVC was found (ϱ = -0.477, P < 0.001). Increasing LAV% corresponded to higher visual emphysema grades. For patients with absent visual emphysema, mean LAV% was 2.98 ± 3.30, for patients with trace emphysema 3.22 ± 2.75, for patients with mild emphysema 3.90 ± 3.33, for patients with moderate emphysema 6.41 ± 3.46, for patients with confluent emphysema 9.02 ± 5.45, and for patients with destructive emphysema 16.90 ± 8.19. Optimal LAV% cutoff value for predicting a FEV1/FVC < 70 was 6.1 (area under the curve = 0.764, sensitivity = 0.773, specificity = 0.665), while for predicting a visual emphysema grade of moderate or higher, it was 4.7 (area under the curve = 0.802, sensitivity = 0.766, specificity = 0.742). Furthermore, correlation between visual emphysema grading and FEV1/FVC was found. In patients with FEV1/FVC < 70% a high proportion of subjects had emphysema grade 3 (moderate) or higher, whereas in patients with FEV1/FVC ≥ 70%, a larger proportion had emphysema grade 3 (moderate) or lower. The sensitivity for visual emphysema grading predicting a FEV1/FVC < 70% was 56.3% with an optimal cutoff point at a visual grade of 4 (confluent), demonstrating a lower sensitivity compared with LAV% (77.3%).

CONCLUSIONS

A significant correlation between AI-based LAV% and FEV1/FVC as well as visual CT emphysema grades can be found in routine chest CT suggesting that AI-based LAV% measurement might be integrated as an add-on functional parameter in the evaluation of chest CT in the future.

Clinicopathological factors associated with sentinel lymph node detection in non-small-cell lung cancer

BACKGROUND

Mapping of the pulmonary lymphatic system by near-infrared (NIR) fluorescence imaging might not always identify the first lymph node relay. The aim of this study was to determine the clinicopathologic factors allowing the identification of sentinel lymph nodes (SLNs) by NIR fluorescence imaging in thoracic surgery for non-small-cell lung cancer (NSCLC).

METHODS

We conducted a retrospective review of 92 patients treated for suspected or confirmed cN0 lung cancer with curative intent who underwent an intraoperative injection of indocyanine green (ICG) either by direct peritumoral injection or by endobronchial injection using electromagnetic navigational bronchoscopy (ENB). After exclusion of patients for technical failure, benign disease and metastasis, we analyzed the clinicopathologic findings of 65 patients treated for localized-stage NSCLC, comparing the group with identification of SLNs (SLN-positive group) with the group without identification of SLNs (SLN-negative group).

RESULTS

Forty-eight patients (73.8%) were SLN-positive. Patients with SLN positivity were more frequently female (50%) than the SLN-negative patients were (11.8%) (p = 0.006). The mean value of diffusing capacity for carbon monoxide (DLCO) was lower among the patients in the SLN-negative group (64.7% ± 16.7%) than the SLN-positive group (77.6% ± 17.2%, p < 0.01). The ratio of forced expiratory volume in one second to forced vital capacity (FEV1/FCV) was higher in the SLN-positive group (69.0% vs. 60.8%, p = 0.02). Patients who were SLN-negative were characterized by a severe degree of emphysema (p = 0.003). There was no significant difference in pathologic characteristics. On univariate analyses, age, female sex, DLCO, FEV1/FVC, degree of emphysema, and tumor size were significantly associated with SLN detection. On multivariate analysis, DLCO > 75% (HR = 4.92, 95% CI: 1.27-24.7; p = 0.03) and female sex (HR = 5.55, 95% CI: 1.25-39.33; p = 0.04) were independently associated with SLN detection.

CONCLUSIONS

At a time of resurgence in the use of the sentinel lymph node mapping technique in the field of thoracic surgery, this study enabled us to identify, using multivariate analysis, two predictive factors for success: DLCO > 75% and female sex. Larger datasets are needed to confirm our results.

Clinical evaluation of pulmonary quantitative computed tomography parameters for diagnosing eosinophilic chronic obstructive pulmonary disease: Characteristics and diagnostic performance

Aims

To investigate the characteristics and diagnostic performance of quantitative computed tomography (QCT) parameters in eosinophilic chronic obstructive pulmonary disease (COPD) patients.

Methods

High-resolution CT scans of COPD patients were retrospectively analyzed, and various emphysematous parenchyma measurements, including lung volume (LC), lung mean density (LMD), lung standard deviation (LSD), full-width half maximum (FWHM), and lung relative voxel number (LRVN) were performed. The QCT parameters were compared between eosinophilic and noneosinophilic COPD patients, using a definition of eosinophilic COPD as blood eosinophil values ≥ 300 cells·µL-1 on at least three times. Receiver operating characteristic curves and area under the curve (ROC-AUC) and python were used to evaluate discriminative efficacy of QCT.

Results

Noneosinophilic COPD patients had a significantly lower TLMD (-846.3 ± 47.9 Hounsfield Unit [HU]) and TFWHM(162.5 ± 30.6 HU) compared to eosinophilic COPD patients (-817.8 ± 54.4, 177.3 ± 33.1 HU, respectively) (p = 0.018, 0.03, respectively). Moreover, the total LC (TLC) and TLSD were significantly lower in eosinophilic COPD group (3234.4 ± 1145.8, 183.8 ± 33.9 HU, respectively) than the noneosinophilic COPD group (5600.2 ± 1248.4, 203.5 ± 20.4 HU, respectively) (p = 0.009, 0.002, respectively). The ROC-AUC values for TLC, TLMD, TLSD, and TFWHM were 0.91 (95% confidence interval [CI], 0.828-0.936), 0.66 (95% CI, 0.546-0.761), 0.64 (95% CI, 0.524-0.742), and 0.63 (95% CI, 0.511-0.731), respectively. When the TLC value was 4110 mL, the sensitivity was 90.7% (95% CI, 79.7-96.9), specificity was 77.8% (95% CI, 57.7-91.4) and accuracy was 86.4%. Notably, TLC demonstrated the highest discriminative efficiency with an F1 Score of 0.79, diagnostic Odds Ratio of 34.3 and Matthews Correlation Coefficient of 0.69, surpassing TLMD (0.55, 3.66, 0.25), TLSD (0.56, 3.95, 0.26), and TFWHM (0.56, 4.16, 0.33).

Conclusion

Eosinophilic COPD patients exhibit lower levels of emphysema and a more uniform density distribution throughout the lungs compared to noneosinophilic COPD patients. Furthermore, TLC demonstrated the highest diagnostic efficiency and may serve as a valuable diagnostic marker for distinguishing between the two groups.

Deep Learning-Based Kernel Adaptation Enhances Quantification of Emphysema on Low-Dose Chest CT for Predicting Long-Term Mortality

OBJECTIVES

The aim of this study was to ascertain the predictive value of quantifying emphysema using low-dose computed tomography (LDCT) post deep learning-based kernel adaptation on long-term mortality.

MATERIALS AND METHODS

This retrospective study investigated LDCTs obtained from asymptomatic individuals aged 60 years or older during health checkups between February 2009 and December 2016. These LDCTs were reconstructed using a 1- or 1.25-mm slice thickness alongside high-frequency kernels. A deep learning algorithm, capable of generating CT images that resemble standard-dose and low-frequency kernel images, was applied to these LDCTs. To quantify emphysema, the lung volume percentage with an attenuation value less than or equal to -950 Hounsfield units (LAA-950) was gauged before and after kernel adaptation. Low-dose chest CTs with LAA-950 exceeding 6% were deemed emphysema-positive according to the Fleischner Society statement. Survival data were sourced from the National Registry Database at the close of 2021. The risk of nonaccidental death, excluding causes such as injury or poisoning, was explored according to the emphysema quantification results using multivariate Cox proportional hazards models.

RESULTS

The study comprised 5178 participants (mean age ± SD, 66 ± 3 years; 3110 males). The median LAA-950 (18.2% vs 2.6%) and the proportion of LDCTs with LAA-950 exceeding 6% (96.3% vs 39.3%) saw a significant decline after kernel adaptation. There was no association between emphysema quantification before kernel adaptation and the risk of nonaccidental death. Nevertheless, after kernel adaptation, higher LAA-950 (hazards ratio for 1% increase, 1.01; P = 0.045) and LAA-950 exceeding 6% (hazards ratio, 1.36; P = 0.008) emerged as independent predictors of nonaccidental death, upon adjusting for age, sex, and smoking status.

CONCLUSIONS

The application of deep learning for kernel adaptation proves instrumental in quantifying pulmonary emphysema on LDCTs, establishing itself as a potential predictive tool for long-term nonaccidental mortality in asymptomatic individuals.

Centrilobular emphysema and airway dysanapsis: factors associated with low respiratory function in younger smokers

Background

Low respiratory function in young adulthood is one of the important factors in the trajectory leading to the future development of COPD, but its morphological characteristics are not well characterised.

Methods

We retrospectively enrolled 172 subjects aged 40-49 years with ≥10 pack-years smoking history who underwent lung cancer screening by computed tomography (CT) and spirometry at two Japanese hospitals. Emphysema was visually assessed according to the Fleischner Society guidelines and classified into two types: centrilobular emphysema (CLE) and paraseptal emphysema (PSE). Airway dysanapsis was assessed with the airway/lung ratio (ALR), which was calculated by the geometric mean of the lumen diameters of the 14 branching segments divided by the cube root of total lung volume on a CT scan.

Results

Among the subjects, CLE and PSE were observed in 20.9% and 30.8%, respectively. The mean ALR was 0.04 and did not differ between those with and without each type of emphysema. Multivariable regression analysis models adjusted for age, sex, body mass index and smoking status indicated that CLE and a low ALR were independently associated with lower forced expiratory volume in 1 s (FEV1)/forced vital capacity (estimate -1.64 (95% CI -2.68- -0.60) and 6.73 (95% CI 4.24-9.24), respectively) and FEV1 % pred (estimate -2.81 (95% CI -5.10- -0.52) and 10.9 (95% CI 5.36-16.4), respectively).

Conclusions

CLE and airway dysanapsis on CT were independently associated with low respiratory function in younger smokers.

Evaluation of retrieval accuracy and visual similarity in content-based image retrieval of chest CT for obstructive lung disease

The aim of our study was to assess the performance of content-based image retrieval (CBIR) for similar chest computed tomography (CT) in obstructive lung disease. This retrospective study included patients with obstructive lung disease who underwent volumetric chest CT scans. The CBIR database included 600 chest CT scans from 541 patients. To assess the system performance, follow-up chest CT scans of 50 patients were evaluated as query cases, which showed the stability of the CT findings between baseline and follow-up chest CT, as confirmed by thoracic radiologists. The CBIR system retrieved the top five similar CT scans for each query case from the database by quantifying and comparing emphysema extent and size, airway wall thickness, and peripheral pulmonary vasculatures in descending order from the database. The rates of retrieval of the same pairs of query CT scans in the top 1-5 retrievals were assessed. Two expert chest radiologists evaluated the visual similarities between the query and retrieved CT scans using a five-point scale grading system. The rates of retrieving the same pairs of query CTs were 60.0% (30/50) and 68.0% (34/50) for top-three and top-five retrievals. Radiologists rated 64.8% (95% confidence interval 58.8-70.4) of the retrieved CT scans with a visual similarity score of four or five and at least one case scored five points in 74% (74/100) of all query cases. The proposed CBIR system for obstructive lung disease integrating quantitative CT measures demonstrated potential for retrieving chest CT scans with similar imaging phenotypes. Further refinement and validation in this field would be valuable.

Alpha-1 Antitrypsin Gene Variants in Patients without Severe Deficiency Diagnosed with Pulmonary Emphysema on Chest CT

Introduction

Although pulmonary involvement due to alpha-1 antitrypsin (AAT) deficiency has been widely described, most studies focus on the genotypes causing severe deficiency (<60 mg/dL).

Objective

The aim of this study was to analyze the prevalence of the different AAT gene variants that do not cause severe deficiency in patients with pulmonary emphysema diagnosed by thoracic computed tomography (CT). Furthermore, we assessed the risk associated with a non-severe decrease in AAT values in the pathogenesis of emphysema.

Methods

Case-control study design that included patients who had a CT scan available of the entire thorax. In total, 176 patients with emphysema (cases) and 100 control subjects without emphysema were analyzed.

Results

The prevalence of variants was higher among cases (25.6%; 45/176) than controls (22%; 22/100), although the difference was not statistically significant (P=0.504) when analyzed globally. In the control group, all the variants detected were MS. Excluding this variant, statistically significant differences were observed in the remaining variants (MZ, SS and SZ). Only 18% of the controls (all MS) presented values below our limit of normality, and all had values very close to the reference value (90 mg/dL). In contrast, 76% of patients with the other variants presented pathological levels. In a logistic regression model, both smoking and a non-severe reduction in AAT (60 to 90 mg/dL) increased the probability of emphysema.

Conclusion

Our study confirms an association between certain variants in the alpha-1 antitrypsin gene that do not cause severe deficiency and the presence of pulmonary emphysema. This association with variants that are associated with reductions in serum AAT values is statistically significant and independent of smoking habit.

Is 18F-fluorodeoxyglucose PET recommended for small lung nodules? CT findings of 18F-fluorodeoxyglucose non-avid lung cancer

OBJECTIVES

To determine the image characteristics associated with low 18F-FDG (18F-fluorodeoxyglucose) avidity among 8-15 mm solid lung cancer.

METHODS

Patients satisfying the following criteria were included: underwent surgery between January 2014 and December 2019 for lung cancer, presented 8-15 mm nodule without measurable ground glass component on preoperative CT, and underwent 18F-FDG PET before resection. Image characteristics, including air bronchogram, concave shape, pleural attachment, and background emphysema, were evaluated by two board-certified radiologists. The Mann-Whitney U test was used to compare maximum standardized uptake (SUVmax) values from 18F-FDG PET images.

RESULTS

The analysis included 235 patients. The SUVmax values of lesions with air bronchogram and concave shape were significantly lower than the SUVmax values of lesions without these features (median: 1.55 vs 2.56 and 1.66 vs 2.45, both P < .001), whereas lesions arising from emphysematous lungs had significantly higher SUVmax values than lesions arising from non-emphysematous lungs (2.90 vs 1.69, P < .001). No significant differences were detected between lesions attached and not attached to pleura. The interobserver agreement was almost perfect for air bronchograms and background emphysema (κ = 0.882 and 0.927, respectively), and 89.7% of lesions with air bronchograms and arising from non-emphysematous lungs showed SUVmax values below 2.5.

CONCLUSIONS

Among 8-15 mm solid lung cancer, the presence of air bronchograms and concave shape and the absence of background emphysema were associated with low 18F-FDG accumulation.

ADVANCES IN KNOWLEDGE

18F-FDG PET can be misleading in differentiating certain type of small solid lung cancer.

Fleischner Society: Glossary of Terms for Thoracic Imaging

Members of the Fleischner Society have compiled a glossary of terms for thoracic imaging that replaces previous glossaries published in 1984, 1996, and 2008, respectively. The impetus to update the previous version arose from multiple considerations. These include an awareness that new terms and concepts have emerged, others have become obsolete, and the usage of some terms has either changed or become inconsistent to a degree that warranted a new definition. This latest glossary is focused on terms of clinical importance and on those whose meaning may be perceived as vague or ambiguous. As with previous versions, the aim of the present glossary is to establish standardization of terminology for thoracic radiology and, thereby, to facilitate communications between radiologists and clinicians. Moreover, the present glossary aims to contribute to a more stringent use of terminology, increasingly required for structured reporting and accurate searches in large databases. Compared with the previous version, the number of images (chest radiography and CT) in the current version has substantially increased. The authors hope that this will enhance its educational and practical value. All definitions and images are hyperlinked throughout the text. Click on each figure callout to view corresponding image. © RSNA, 2024 Supplemental material is available for this article. See also the editorials by Bhalla and Powell in this issue.

Oxidative Damage and Telomere Length as Markers of Lung Cancer Development among Chronic Obstructive Pulmonary Disease (COPD) Smokers

Lung cancer (LC) constitutes an important cause of death among patients with Chronic Obstructive Pulmonary Disease (COPD). Both diseases may share pathobiological mechanisms related to oxidative damage and cellular senescence. In this study, the potential value of leucocyte telomere length, a hallmark of aging, and 8-OHdG concentrations, indicative of oxidative DNA damage, as risk biomarkers of LC was evaluated in COPD patients three years prior to LC diagnosis. Relative telomere length measured using qPCR and serum levels of 8-OHdG were determined at the baseline in 99 COPD smokers (33 with LC and 66 age-matched COPD without LC as controls). Of these, 21 COPD with LC and 42 controls had the biomarkers measured 3 years before. Single nucleotide variants (SNVs) in TERT, RTEL, and NAF1 genes were also determined. COPD cases were evaluated, which showed greater telomere length (p < 0.001) and increased serum 8-OHdG levels (p = 0.004) three years prior to LC diagnosis compared to the controls. This relationship was confirmed at the time of LC diagnosis. No significant association was found between the studied SNVs in cases vs. controls. In conclusion, this preliminary study shows that longer leucocyte telomere length and increased 8-OHdG serum levels can be useful as early biomarkers of the risk for future lung cancer development among COPD patients.

Detection of subsolid nodules on chest CT scans during the COVID-19 pandemic

OBJECTIVE

To investigate the detection of subsolid nodules (SSNs) on chest CT scans of outpatients before and during the COVID-19 pandemic, as well as to correlate the imaging findings with epidemiological data. We hypothesized that (pre)malignant nonsolid nodules were underdiagnosed during the COVID-19 pandemic because of an overlap of imaging findings between SSNs and COVID-19 pneumonia.

METHODS

This was a retrospective study including all chest CT scans performed in adult outpatients (> 18 years of age) in September of 2019 (i.e., before the COVID-19 pandemic) and in September of 2020 (i.e., during the COVID-19 pandemic). The images were reviewed by a thoracic radiologist, and epidemiological data were collected from patient-filled questionnaires and clinical referrals. Regression models were used in order to control for confounding factors.

RESULTS

A total of 650 and 760 chest CT scans were reviewed for the 2019 and 2020 samples, respectively. SSNs were found in 10.6% of the patients in the 2019 sample and in 7.9% of those in the 2020 sample (p = 0.10). Multiple SSNs were found in 23 and 11 of the patients in the 2019 and 2020 samples, respectively. Women constituted the majority of the study population. The mean age was 62.8 ± 14.8 years in the 2019 sample and 59.5 ± 15.1 years in the 2020 sample (p < 0.01). COVID-19 accounted for 24% of all referrals for CT examination in 2020.

CONCLUSIONS

Fewer SSNs were detected on chest CT scans of outpatients during the COVID-19 pandemic than before the pandemic, although the difference was not significant. In addition to COVID-19, the major difference between the 2019 and 2020 samples was the younger age in the 2020 sample. We can assume that fewer SSNs will be detected in a population with a higher proportion of COVID-19 suspicion or diagnosis.

Pneumothorax and pulmonary hemorrhage after C-arm cone-beam computed tomography-guided percutaneous transthoracic lung biopsy: incidence, clinical significance, and correlation

OBJECTIVE

This study aimed to assess the incidence and clinical significance of pneumothorax (PTX) and pulmonary hemorrhage (PH) after percutaneous transthoracic lung biopsy (PTLB) guided by C-arm cone-beam computed tomography (CBCT). Furthermore, this study aimed to examine the relationships between PTX and PH with demographics, clinical characteristics, imaging, and PTLB parameters.

METHODS

A retrospective analysis was conducted on 192 patients who underwent PTLB at our hospital between January 2019 and October 2022. Incidences of PTX and PH were recorded. PTX was considered clinically significant if treated with chest tube insertion (CTI), and PH if treated with bronchoscopes or endovascular treatments. The various factors on PTX and PH were analyzed using the Chi-squared test and Student t-test. Logistic regression analyses were then used to determine these factors on the correlation to develop PTX and PH.

RESULTS

PTX occurred in 67/192 cases (34.9%); CTI was required in 5/67 (7.5%). PH occurred in 63/192 cases (32.8%) and none of these cases required bronchoscopes or endovascular treatments. Lesion diameter (ORPTX = 0.822; ORPH = 0.785), presence of pulmonary emphysema (ORPH = 2.148), the number of samples (ORPH = 1.834), the use of gelfoam (ORPTX = 0.474; ORPH = 0.341) and ablation (ORPTX = 2.351; ORPH = 3.443) showed statistically significant correlation to PTX and PH.

CONCLUSIONS

CBCT-guided PTLB is a safe and effective method for performing lung biopsies. The use of gelfoam has been shown to reduce the occurrence of PTX and PH. However, caution should be exercised when combining radiofrequency ablation with PTLB, as it may increase the risk of PTX and PH.

Echo time-dependent observed T1 and quantitative perfusion in chronic obstructive pulmonary disease using magnetic resonance imaging

Introduction

Due to hypoxic vasoconstriction, perfusion is interesting in the lungs. Magnetic Resonance Imaging (MRI) perfusion imaging based on Dynamic Contrast Enhancement (DCE) has been demonstrated in patients with Chronic Obstructive Pulmonary Diseases (COPD) using visual scores, and quantification methods were recently developed further. Inter-patient correlations of echo time-dependent observed T1 [T1(TE)] have been shown with perfusion scores, pulmonary function testing, and quantitative computed tomography. Here, we examined T1(TE) quantification and quantitative perfusion MRI together and investigated both inter-patient and local correlations between T1(TE) and quantitative perfusion.

Methods

22 patients (age 68.0 ± 6.2) with COPD were examined using morphological MRI, inversion recovery multi-echo 2D ultra-short TE (UTE) in 1-2 slices for T1(TE) mapping, and 4D Time-resolved angiography With Stochastic Trajectories (TWIST) for DCE. T1(TE) maps were calculated from 2D UTE at five TEs from 70 to 2,300 μs. Pulmonary Blood Flow (PBF) and perfusion defect (QDP) maps were produced from DCE measurements. Lungs were automatically segmented on UTE images and morphological MRI and these segmentations registered to DCE images. DCE images were separately registered to UTE in corresponding slices and divided into corresponding subdivisions. Spearman's correlation coefficients were calculated for inter-patient correlations using the entire segmented slices and for local correlations separately using registered images and subdivisions for each TE. Median T1(TE) in normal and defect areas according to QDP maps were compared.

Results

Inter-patient correlations were strongest on average at TE2 = 500 μs, reaching up to |ρ| = 0.64 for T1 with PBF and |ρ| = 0.76 with QDP. Generally, local correlations of T1 with PBF were weaker at TE2 than at TE1 or TE3 and with maximum values of |ρ| = 0.66 (from registration) and |ρ| = 0.69 (from subdivision). In 18 patients, T1 was shorter in defect areas than in normal areas, with the relative difference smallest at TE2.

Discussion

The inter-patient correlations of T1 with PBF and QDP found show similar strength and TE-dependence as those previously reported for visual perfusion scores and quantitative computed tomography. The local correlations and median T1 suggest that not only base T1 but also the TE-dependence of observed T1 in normal areas is closer to that found previously in healthy volunteers than in defect areas.

Spatial Transcriptomics Resolve an Emphysema-Specific Lymphoid Follicle B Cell Signature in Chronic Obstructive Pulmonary Disease

Rationale: Within chronic obstructive pulmonary disease (COPD), emphysema is characterized by a significant yet partially understood B cell immune component. Objectives: To characterize the transcriptomic signatures from lymphoid follicles (LFs) in ever-smokers without COPD and patients with COPD with varying degrees of emphysema. Methods: Lung sections from 40 patients with COPD and ever-smokers were used for LF proteomic and transcriptomic spatial profiling. Formalin- and O.C.T.-fixed lung samples obtained from biopsies or lung explants were assessed for LF presence. Emphysema measurements were obtained from clinical chest computed tomographic scans. High-confidence transcriptional target intersection analyses were conducted to resolve emphysema-induced transcriptional networks. Measurements and Main Results: Overall, 115 LFs from ever-smokers and Global Initiative for Chronic Obstructive Lung Disease (GOLD) 1-2 and GOLD 3-4 patients were analyzed. No LFs were found in never-smokers. Differential gene expression analysis revealed significantly increased expression of LF assembly and B cell marker genes in subjects with severe emphysema. High-confidence transcriptional analysis revealed activation of an abnormal B cell activity signature in LFs (q-value = 2.56E-111). LFs from patients with GOLD 1-2 COPD with emphysema showed significantly increased expression of genes associated with antigen presentation, inflammation, and B cell activation and proliferation. LFs from patients with GOLD 1-2 COPD without emphysema showed an antiinflammatory profile. The extent of centrilobular emphysema was significantly associated with genes involved in B cell maturation and antibody production. Protein-RNA network analysis showed that LFs in emphysema have a unique signature skewed toward chronic B cell activation. Conclusions: An off-targeted B cell activation within LFs is associated with autoimmune-mediated emphysema pathogenesis.

A reference equation for lung volume on computed tomography in Japanese middle-aged and elderly adults

BACKGROUND

Effective use of lung volume data measured on computed tomography (CT) requires reference values for specific populations. This study examined whether an equation previously generated for multiple ethnic groups in the United States, including Asians predominantly composed of Chinese people, in the Multi-Ethnic Study of Atherosclerosis (MESA) could be used for Japanese people and, if necessary, to optimize this equation. Moreover, the equation was used to characterize patients with chronic obstructive pulmonary disease (COPD) and lung hyperexpansion.

METHODS

This study included a lung cancer screening CT cohort of asymptomatic never smokers aged ≥40 years from two institutions (n = 364 and 419) to validate and optimize the MESA equation and a COPD cohort (n = 199) to test its applicability.

RESULTS

In all asymptomatic never smokers, the variance explained by the predicted values (R2) based on the original MESA equation was 0.60. The original equation was optimized to minimize the root mean squared error (RMSE) by adjusting the scaling factor but not the age, sex, height, or body mass index terms of the equation. The RMSE changed from 714 ml in the original equation to 637 ml in the optimized equation. In the COPD cohort, lung hyperexpansion, defined based on the 95th percentile of the ratio of measured lung volume to predicted lung volume in never smokers (122 %), was observed in 60 (30 %) patients and was associated with centrilobular emphysema and air trapping on inspiratory/expiratory CT.

CONCLUSIONS

The MESA equation was optimized for Japanese middle-aged and elderly adults.

Variation in Reporting of Incidental Findings on Initial Lung Cancer Screening and Associations With Clinician Assessment

PURPOSE

The aim of this study was to quantify the distribution, frequency, and clinical significance of incidental findings (IFs) on initial lung cancer screening (LCS) and the association of report characteristics with subsequent assessment.

METHODS

Health records of patients undergoing initial LCS from 2015 to 2018 in the Minneapolis VA Health Care System were retrospectively reviewed for demographics, Lung CT Screening Reporting & Data System coding, IFs, and subsequent clinical assessment. IFs were considered potentially significant if they were likely to require any follow-up. High-risk significant IFs (SIFs) were potentially malignant. The primary outcome was the SIF being addressed. Outcomes were analyzed using a mixed-effects model.

RESULTS

Patients (n = 901) were primarily male (94.1%) smokers (62.1%) with a mean age of 65.2 years. IFs were extremely common (93.9%), with an average of 2.6 IFs per scan (n = 2,296). Seven hundred eighty-six IFs (34.2%) were deemed likely SIFs, of which 58 (7.4%) were high risk. Two hundred twenty-two (28.2%) were addressed by clinicians, of which 104 (13.2%) underwent testing. Reporting of SIFs varied among radiologists, with at least one SIF in the impression in 24% to 78% of low-dose CT studies with the S modifier, used to indicate the presence of a SIF, applied to 0% to 51% of reports. In the mutually adjusted model, radiologist recommendation (adjusted odds ratio [OR], 4.67; 95% confidence interval [CI], 2.23-9.76), high-risk finding (adjusted OR, 4.35; 95% CI, 1.81-10.45), and reporting in the impression (adjusted OR, 2.58; 95% CI, 1.28-5.18) were associated with increased odds of the SIF's being addressed.

CONCLUSIONS

Radiologists vary in their reporting of IFs on LCS. Further standardization of reporting of SIFs may improve this process, with the simultaneous goals of generating appropriate testing when needed and minimizing low-value care.

The influence of image reconstruction methods on the diagnosis of pulmonary emphysema with convolutional neural network

This study investigated the influence of iterative reconstruction (IR) methods on computed tomography (CT) images when training convolutional neural network (CNN) models to diagnose pulmonary emphysema. To evaluate the influence of the IR algorithm on CNN, the present study comprised two steps: the comparison of noise reduction by IR algorithms using phantom examinations and the change in performance of CNN with IR algorithms using patient data. We retrospectively analyzed 97 patients. Raw CT data were reconstructed using the filtered back-projection (FBP) and adaptive statistical iterative reconstruction V (ASIR-V) algorithms with blending levels of 30%, 50%, and 70%. The models were trained using reconstructed CT images and were named the FBP, ASIR-V30, ASIR-V50, and ASIR-V70 models. The mean and the standard deviation of the CT values were 11.3 ± 21.2 at FBP, 11.0 ± 17.3 at ASIR-V30, 11.0 ± 14.4 at ASIR-V50, and 11.0 ± 11.8 at ASIR-V70. For all the evaluation metrics, the best values were obtained with the FBP model applied to the ASIR-V70 test images. The worst values were obtained with the ASIR-V70 model applied to the FBP test images. The model trained with FBP images exhibited significantly better performance than the models trained using IR images. The reduction in image noise with the IR algorithm on the test images contributed to improving the accuracy of the classification of emphysema subtypes using CNN.

Chest MRI and CT Predictors of 10-Year All-Cause Mortality in COPD

Pulmonary imaging measurements using magnetic resonance imaging (MRI) and computed tomography (CT) have the potential to deepen our understanding of chronic obstructive pulmonary disease (COPD) by measuring airway and parenchymal pathologic information that cannot be provided by spirometry. Currently, MRI and CT measurements are not included in mortality risk predictions, diagnosis, or COPD staging. We evaluated baseline pulmonary function, MRI and CT measurements alongside imaging texture-features to predict 10-year all-cause mortality in ex-smokers with (n = 93; 31 females; 70 ± 9years) and without (n = 69; 29 females, 69 ± 9years) COPD. CT airway and vessel measurements, helium-3 (3He) MRI ventilation defect percent (VDP) and apparent diffusion coefficients (ADC) were quantified. MRI and CT texture-features were extracted using PyRadiomics (version2.2.0). Associations between 10-year all-cause mortality and all clinical and imaging measurements were evaluated using multivariable regression model odds-ratios. Machine-learning predictive models for 10-year all-cause mortality were evaluated using area-under-receiver-operator-characteristic-curve (AUC), sensitivity and specificity analyses. DLCO (%pred) (HR = 0.955, 95%CI: 0.934-0.976, p < 0.001), MRI ADC (HR = 1.843, 95%CI: 1.260-2.871, p < 0.001), and CT informational-measure-of-correlation (HR = 3.546, 95% CI: 1.660-7.573, p = 0.001) were the strongest predictors of 10-year mortality. A machine-learning model trained on clinical, imaging, and imaging textures was the best predictive model (AUC = 0.82, sensitivity = 83%, specificity = 84%) and outperformed the solely clinical model (AUC = 0.76, sensitivity = 77%, specificity = 79%). In ex-smokers, regardless of COPD status, addition of CT and MR imaging texture measurements to clinical models provided unique prognostic information of mortality risk that can allow for better clinical management.Clinical Trial Registration: www.clinicaltrials.gov NCT02279329.

Deep learning parametric response mapping from inspiratory chest CT scans: a new approach for small airway disease screening

OBJECTIVES

Parametric response mapping (PRM) enables the evaluation of small airway disease (SAD) at the voxel level, but requires both inspiratory and expiratory chest CT scans. We hypothesize that deep learning PRM from inspiratory chest CT scans can effectively evaluate SAD in individuals with normal spirometry.

METHODS

We included 537 participants with normal spirometry, a history of smoking or secondhand smoke exposure, and divided them into training, tuning, and test sets. A cascaded generative adversarial network generated expiratory CT from inspiratory CT, followed by a UNet-like network predicting PRM using real inspiratory CT and generated expiratory CT. The performance of the prediction is evaluated using SSIM, RMSE and dice coefficients. Pearson correlation evaluated the correlation between predicted and ground truth PRM. ROC curves evaluated predicted PRMfSAD (the volume percentage of functional small airway disease, fSAD) performance in stratifying SAD.

RESULTS

Our method can generate expiratory CT of good quality (SSIM 0.86, RMSE 80.13 HU). The predicted PRM dice coefficients for normal lung, emphysema, and fSAD regions are 0.85, 0.63, and 0.51, respectively. The volume percentages of emphysema and fSAD showed good correlation between predicted and ground truth PRM (|r| were 0.97 and 0.64, respectively, p < 0.05). Predicted PRMfSAD showed good SAD stratification performance with ground truth PRMfSAD at thresholds of 15%, 20% and 25% (AUCs were 0.84, 0.78, and 0.84, respectively, p < 0.001).

CONCLUSION

Our deep learning method generates high-quality PRM using inspiratory chest CT and effectively stratifies SAD in individuals with normal spirometry.

Evaluation of preventive tract embolization with standardized gelatin sponge slurry on chest tube placement rate after CT-guided lung biopsy: a propensity score analysis

BACKGROUND

To evaluate the effect of tract embolization (TE) with gelatin sponge slurries during a percutaneous lung biopsy on chest tube placement and to evaluate the predictive factors of chest tube placement.

METHODS

Percutaneous CT-guided lung biopsies performed with (TE) or without (non-TE) tract embolization or between June 2012 and December 2021 at three referral tertiary centers were retrospectively analyzed. The exclusion criteria were mediastinal biopsies, pleural tumors, and tumors adjacent to the pleura without pleural crossing. Variables related to patients, tumors, and procedures were collected. Univariable and multivariable analyses were performed to determine risk factors for chest tube placement. Furthermore, the propensity score matching analysis was adopted to yield a matched cohort.

RESULTS

A total of 1157 procedures in 1157 patients were analyzed, among which 560 (48.4%) were with TE (mean age 66.5 ± 9.2, 584 men). The rates of pneumothorax (44.9% vs. 26.1%, respectively; p < 0.001) and chest tube placement (4.8% vs. 2.3%, respectively; p < 0.001) were significantly higher in the non-TE group than in the TE group. No non-targeted embolization or systemic air embolism occurred. In the whole population, two protective factors for chest tube placement were found in univariate analysis: TE (OR 0.465 [0.239-0.904], p < 0.05) and prone position (OR 0.212 [0.094-0.482], p < 0.001). These data were confirmed in multivariate analysis (p < 0.001 and p < 0.0001 respectively). In the propensity matched cohort, TE reduces significatively the risk of chest tube insertion (OR = 0.44 [0.21-0.87], p < 0.05).

CONCLUSIONS

The TE technique using standardized gelatin sponge slurry reduces the need for chest tube placement after percutaneous CT-guided lung biopsy.

CRITICAL RELEVANCE STATEMENT

The tract embolization technique using standardized gelatin sponge slurry reduces the need for chest tube placement after percutaneous CT-guided lung biopsy.

KEY POINTS

1. Use of tract embolization with gelatine sponge slurry during percutaneous lung biopsy is safe. 2. Use of tract embolization significantly reduces the risk of chest tube insertion. 3. This is the first multicenter study to show the protective effect of tract embolization on chest tube insertion.

Baseline characteristics from a 3-year longitudinal study to phenotype subjects with COPD: the FOOTPRINTS study

BACKGROUND

FOOTPRINTS® is a prospective, longitudinal, 3-year study assessing the association between biomarkers of inflammation/lung tissue destruction and chronic obstructive pulmonary disease (COPD) severity and progression in ex-smokers with mild-to-severe COPD. Here, we present baseline characteristics and select biomarkers of study subjects.

METHODS

The methodology of FOOTPRINTS® has been published previously. The study population included ex-smokers with a range of COPD severities (Global Initiative for Chronic Obstructive Lung Disease [GOLD] stages 1-3), ex-smokers with COPD and alpha-1-antitrypsin deficiency (A1ATD) and a control group of ex-smokers without airflow limitation (EwAL). At study entry, data were collected for: demographics, disease characteristics, history of comorbidities and COPD exacerbations, symptoms, lung function and volume, exercise capacity, soluble biomarkers, and quantitative and qualitative computed tomography. Baseline data are presented with descriptive statistical comparisons for soluble biomarkers in the individual GOLD and A1ATD groups versus EwAL.

RESULTS

In total, 463 subjects were enrolled. The per-protocol set comprised 456 subjects, mostly male (64.5%). The mean (standard deviation) age was 60.7 (6.9) years. At baseline, increasing pulmonary symptoms, worse lung function, increased residual volume, reduced diffusing capacity of the lung for carbon monoxide (DLco) and greater prevalence of centrilobular emphysema were observed with increasing disease severity amongst GOLD 1-3 subjects. Subjects with A1ATD (n = 19) had similar lung function parameters to GOLD 2-3 subjects, a high residual volume comparable to GOLD 3 subjects, and similar air trapping to GOLD 2 subjects. Compared with EwAL (n = 61), subjects with A1ATD had worse lung function, increased residual volume, reduced DLco, and a greater prevalence of confluent or advanced destructive emphysema. The soluble inflammatory biomarkers white blood cell count, fibrinogen, high-sensitivity C-reactive protein and plasma surfactant protein were higher in GOLD 1-3 groups than in the EwAL group. Interleukin-6 was expressed less often in EwAL subjects compared with subjects in the GOLD and A1ATD groups. Soluble receptor for advanced glycation end product was lowest in GOLD 3 subjects, indicative of more severe emphysema.

CONCLUSIONS

These findings provide context for upcoming results from FOOTPRINTS®, which aims to establish correlations between biomarkers and disease progression in a representative COPD population.

TRIAL REGISTRATION NUMBER

NCT02719184, study start date 13/04/2016.