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

The association and impact of radiographic, pathological emphysema and spirometric airway obstruction on patients with resectable lung adenocarcinoma

BACKGROUND

Destruction of alveoli structure and lung function are interrelated, however, their correlation and clinical significance have been not well defined in patients with lung cancer. Thus, this study aimed to examine the association among radiographic, pathological emphysema and spirometric airway obstruction in patients with resectable lung cancer as well as explore their impact on postoperative pulmonary complications (PPCs) and long-term prognosis.

METHODS

Lung adenocarcinoma (LUAD) patients who performed chest CT, spirometry, and curative resection were included from a prospective three-institution database. CT-defined emphysema at baseline was assessed visually and quantitatively, pathological emphysema was reviewed on postoperative specimen. Multivariable regression models, propensity score matching, stratified analysis, and subgroup analysis were adopted to reduce selection bias.

RESULTS

Our cohort included 902 patients, with a median follow-up of 5.6 years. CT-defined emphysema was present in 163 patients (18.1%) and most of them (86.5%) were validated with pathological evidence. 169 had spirometric airway obstruction, while only 29.6% patients overlapped with CT-defined emphysema. Multivariable logistic regression models showed CT-defined emphysema, not airway obstruction, was associated with an increased risk of PPCs (adjusted odds ratio, 2.35; 95% CI, 1.40-3.93; P = 0.001). After adjusting for age, sex, body mass index, smoking history, tumour stage, vascular invasion, pleural invasion, multivariate cox analysis identified CT-defined emphysema, not airway obstruction, as an independent prognostic factor for OS (adjusted hazard ratio, 1.44; 95%CI, 1.05-1.97; P = 0.022). Patients with both radiographic and pathological emphysema experienced worse OS (log-rank P < 0.001). In the propensity score-matched cohort, stratified analysis, and never-smokers subgroup analysis, CT-defined emphysema remained a strong and statistically significant factor related to poor survival.

CONCLUSIONS

The presence of radiological and pathological emphysema in resectable LUAD was associated with frequent PPCs and decreased survival.

CLINICAL TRIAL NUMBER

Not applicable.

Uncertainty-aware quantitative CT evaluation of emphysema and mortality risk from variable radiation dose images

OBJECTIVE

To develop an automated method for the joint and consistent evaluation of emphysema and mortality risk that provides quantification of data and model uncertainty.

MATERIALS AND METHODS

Participants from the prospective COPDGene study who underwent both full radiation dose (FD) and reduced radiation dose (RD) chest CT scans at 5-year follow-up were included and divided into training (80%), validation (10%), and testing (10%) datasets. We trained a multi-task Bayesian neural network (BNN) to estimate the FD volume-adjusted lung density (ALD) regardless of acquisition protocol, in addition to the 5-year mortality risk. The data and model uncertainty were quantified in the testing dataset. Our deep learning ALD (DL-ALD) was compared to the conventional ALD.

RESULTS

In total, 1350 participants (mean age 64.4 years ± 8.7; 659 female) were included. Compared to conventional ALD, DL-ALD was more consistent between FD and RD CT images (mean difference: 1 g/L ± 3.1 versus 14.8 g/L ± 5.3, p < 0.001). The predicted 5-year mortality was similar between image protocols (mean difference: 0.0007 ± 0.02, p = 0.76). The uncertainty associated with image variability when quantifying DL-ALD was lower in participants with severe emphysema (Pearson's rho = 0.79, p < 0.001), and the model uncertainty for mortality risk was lower both for severe and early-stage participants compared to other participants (p < 0.001).

CONCLUSION

The presented multi-task BNN provides an increased robustness to imaging protocol compared to conventional methods for CT evaluation of emphysema. Additionally, it provides direct measurements of uncertainty for its generalization to diverse imaging protocols and patient populations.

KEY POINTS

Question Quantitative CT evaluation of emphysema is highly sensitive to CT protocol, which increases uncertainty in disease evaluation and impacts the clinical utility of traditional metrics. Findings Uncertainty-aware deep learning improved consistency in emphysema quantification between fixed and reduced dose CT scans compared to traditional histogram analysis. Clinical relevance CT evaluation of emphysema severity and mortality risk using uncertainty-aware deep learning methods is more consistent across variable radiation dose protocols compared to conventional methods while also providing measurement reliability metrics, improving the evaluation of COPD using CT.

Kernel Conversion Improves the Correlation between the Extent of Emphysema and Clinical Parameters in Chronic Obstructive Pulmonary Disease: A Multicenter Cohort Study

BACKGROUND

Computed tomography (CT) scans are utilized to assess emphysema, a prominent phenotype of chronic obstructive pulmonary disease (COPD). Variability in CT protocols and equipment across hospitals can impact accuracy. This study aims to implement kernel conversion across different CT settings and evaluate changes in the correlation between the emphysema index pre- and post-kernel conversion, along with clinical measures in COPD patients.

METHODS

Data were extracted from the Korea COPD Subgroup Study database, which included CT scan images from 484 COPD patients. These images underwent kernel conversion. Emphysema extent was quantified using the percentage of low-attenuation areas (%LAA-950) determined by a deep learning-based program. The correlation between %LAA-950 and clinical parameters, including lung function tests, the modified Medical Research Council (mMRC), 6-minute walking distance (6MWD), COPD assessment test (CAT), and the St. George's Respiratory Questionnaire for COPD (SGRQ-c), was analyzed. Subsequently, these values were compared across various CT settings.

RESULTS

A total of 484 participants were included. Kernel conversion significantly reduced the variance in %LAA-950 values (before vs. after: 12.6±11.0 vs. 8.8±11.9). Post-kernel conversion, %LAA-950 demonstrated moderate correlations with forced expiratory volume in 1 second (r=-0.41), residual volume/total lung capacity (r=0.42), mMRC (r=0.25), CAT score (r=0.12), SGRQ-c (r=0.21), and 6MWD (r=0.15), all of which were improved compared to the unconverted dataset (all p<0.01).

CONCLUSION

CT images processed through kernel conversion enhance the correlation between the extent of emphysema and clinical parameters in COPD.

Assessment of Emphysema on X-ray Equivalent Dose Photon-Counting Detector CT: Evaluation of Visual Scoring and Automated Quantification Algorithms

OBJECTIVES

The aim of this study was to evaluate the feasibility and efficacy of visual scoring, low-attenuation volume (LAV), and deep learning methods for estimating emphysema extent in x-ray dose photon-counting detector computed tomography (PCD-CT), aiming to explore future dose reduction potentials.

METHODS

One hundred one prospectively enrolled patients underwent noncontrast low- and chest x-ray dose CT scans in the same study using PCD-CT. Overall image quality, sharpness, and noise, as well as visual emphysema pattern (no, trace, mild, moderate, confluent, and advanced destructive emphysema; as defined by the Fleischner Society), were independently assessed by 2 experienced radiologists for low- and x-ray dose images, followed by an expert consensus read. In the second step, automated emphysema quantification was performed using an established LAV algorithm with a threshold of -950 HU and a commercially available deep learning model for automated emphysema quantification. Automated estimations of emphysema extent were converted and compared with visual scoring ratings.

RESULTS

X-ray dose scans exhibited a significantly lower computed tomography dose index than low-dose scans (low-dose: 0.66 ± 0.16 mGy, x-ray dose: 0.11 ± 0.03 mGy, P < 0.001). Interreader agreement between low- and x-ray dose for visual emphysema scoring was excellent (κ = 0.83). Visual emphysema scoring consensus showed good agreement between low-dose and x-ray dose scans (κ = 0.70), with significant and strong correlation (Spearman ρ = 0.79). Although trace emphysema was underestimated in x-ray dose scans, there was no significant difference in the detection of higher-grade (mild to advanced destructive) emphysema ( P = 0.125) between the 2 scan doses. Although predicted emphysema volumes on x-ray dose scans for the LAV method showed strong and the deep learning model excellent significant correlations with predictions on low-dose scans, both methods significantly overestimated emphysema volumes on lower quality scans ( P < 0.001), with the deep learning model being more robust. Further, deep learning emphysema severity estimations showed higher agreement (κ = 0.65) and correlation (Spearman ρ = 0.64) with visual scoring for low-dose scans than LAV predictions (κ = 0.48, Spearman ρ = 0.45).

CONCLUSIONS

The severity of emphysema can be reliably estimated using visual scoring on CT scans performed with x-ray equivalent doses on a PCD-CT. A deep learning algorithm demonstrated good agreement and strong correlation with the visual scoring method on low-dose scans. However, both the deep learning and LAV algorithms overestimated emphysema extent on x-ray dose scans. Nonetheless, x-ray equivalent radiation dose scans may revolutionize the detection and monitoring of disease in chronic obstructive pulmonary disease patients.

Effect of adaptive statistical iterative reconstruction-V algorithm and deep learning image reconstruction algorithm on image quality and emphysema quantification in COPD patients under ultra-low-dose conditions

PURPOSE

To explore the effect of different reconstruction algorithms (ASIR-V and DLIR) on image quality and emphysema quantification in chronic obstructive pulmonary disease (COPD) patients under ultra-low-dose scanning conditions.

MATERIALS AND METHODS

This prospective study with patient consent included 62 COPD patients. Patients were examined by pulmonary function test (PFT), standard-dose CT (SDCT) and ultra-low-dose CT (ULDCT). SDCT images were reconstructed with filtered-back-projection (FBP), while ULDCT images were reconstructed using FBP, 30%ASIR-V, 60%ASIR-V, 90%ASIR-V, low-strength (DLIR-L), medium-strength (DLIR-M) and high-strength DLIR (DLIR-H) to form 8 image sets. Images were analysed using a commercial computer aided diagnosis (CAD) software. Parameters such as image noise, lung volume (LV), emphysema index (EI), mean lung density (MLD) and 15th percentile of lung density (PD15) were measured. Two radiologists evaluated tracheal and pulmonary artery image quality using a 5-point scale. Measurements were compared and the correlation between EI and PFT indices was analysed.

RESULT

ULDCT used 0.46 ± 0.22 mSv in radiation dose, 93.8% lower than SDCT (P < .001). There was no difference in LV and MLD among image groups (P > .05). ULDCT-ASIR-V90% and ULDCT-DLIR-M had similar image noise and EI and PD15 values to SDCT-FBP, and ULDCT-DLIR-M and ULDCT-DLIR-H had similar subjective scores to SDCT-FBP (all P > .05). ULDCT-DLIR-M provided the best correlation between EI and the FEV1/FVC and FEV1% indices in PFT, and the lowest deviations with SDCT-FBP in both EI and PD15.

CONCLUSION

DLIR-M provides the best image quality and emphysema quantification for COPD patients in ULDCT.

ADVANCES IN KNOWLEDGE

Ultra-low-dose CT scanning combined with DLIR-M reconstruction is comparable to standard dose images for quantitative analysis of emphysema and image quality.

Application of Artificial Intelligence in Thoracic Radiology: A Narrative Review

Thoracic radiology has emerged as a primary field in which artificial intelligence (AI) is extensively researched. Recent advancements highlight the potential to enhance radiologists' performance through AI. AI aids in detecting and classifying abnormalities, and in quantifying both normal and abnormal anatomical structures. Additionally, it facilitates prognostication by leveraging these quantitative values. This review article will discuss the recent achievements of AI in thoracic radiology, focusing primarily on deep learning, and explore the current limitations and future directions of this cutting-edge technique.

Machine-learning tool for classifying pulmonary hypertension via expert reader-provided CT features: An educational resource for non-dedicated radiologists

PURPOSE

Pulmonary hypertension (PH) is a complex disease classified into five groups (I-V) by the European Society of Cardiology/European Respiratory Society (ESC/ERS) guidelines. Chest contrast-enhanced computed tomography (CECT) is crucial in the non-invasive PH assessment. This study aimed to develop a machine learning (ML)-based educational resource for classifying PH cases via CECT according to ESC/ERS groups.

METHODS

We retrospectively included 172 PH patients who underwent CECT at two University Hospitals (Udine and Brescia). Three chest-devoted radiologists independently reviewed the CECTs, reporting on 13 features, including lung conditions, heart abnormalities, chronic thromboembolism, and mediastinal findings. Readers assigned the features as absent/present except for the left atrium (LA) anteroposterior diameter (measured in millimeters) and classified PH cases I-V with likelihood scores (1-100 %) for each group. The majority decisions for features and average LA diameter were used as ML inputs. The highest average likelihood scores determined group assignments, serving as ground truth. Various ML algorithms were tested using the Weka software and evaluated by accuracy, area under the ROC curve (AUROC), and F1-score.

RESULTS

After excluding three group V patients to avoid imbalance, the Naïve-Bayes algorithm showed 0.72 accuracy, 0.84 AUROC, and 0.72 F1-score. Accuracy values for group I-IV were 0.75, 0.78, 0.51, 0.79; AUROC values were 0.78, 0.84, 0.86, 0.87; F1-scores were 0.63, 0.79, 0.61, 0.84, respectively.

CONCLUSIONS

This study is the first to develop an ML-driven tool for classifying PH via chest CECT. While performance metrics require improvement, including the need for a larger sample size, the resource can potentially train non-dedicated radiologists in PH classification, supporting multidisciplinary reasoning.

AutoCOPD-A novel and practical machine learning model for COPD detection using whole-lung inspiratory quantitative CT measurements: a retrospective, multicenter study

Background

The rate of diagnosis for chronic obstructive pulmonary disease (COPD) is low worldwide. Quantitative computed tomography (QCT) parameters add value to quantify alterations in airway and lung parenchyma for COPD. This study aimed to assess the performance of QCT features in COPD detection using a whole-lung inspiratory CT model.

Methods

This multicenter retrospective study was performed on 4106 participants. The derivation cohort containing 1950 participants who enrolled in Guangzhou communities from August 2017 to December 2019, was separated for training and internal validation cohorts, and three external validation cohorts containing 1703 participants were recruited from the public hospitals (Cohort 1: the First Affiliated Hospital of Guangzhou Medical University; Cohort 2: Xiangyang central hospital; Cohort 3: the Second Affiliated Hospital of Xi'an Jiaotong University) in China between April 2017 and May 2024. Questionnaire information, CT reports, and QCT features derived from inspiratory CT were extracted for model development. A novel multimodal framework using eXtreme gradient boosting and hybrid feature selection was established for COPD detection. National Lung Screening Trial (NLST) cohort (n = 453) was applied to validate the multiracial extrapolation and robustness on low-dose CT scans.

Findings

The QCT model (referred to as AutoCOPD) with ten features achieved the highest AUC of 0·860 (95% CI: 0·823-0·898) in the internal validation cohort, and showed excellent discrimination when externally validated [Cohort 1: AUC = 0·915 (95% CI: 0·898-0·931); Cohort 2: AUC = 0·903 (95% CI: 0·864-0·943); Cohort 3: AUC = 0·914 (95% CI: 0·882-0·947); NLST: AUC = 0·881 (95% CI: 0·846-0·915)]. Decision curve analysis demonstrated that AutoCOPD was valuable across a range of COPD risk thresholds between 0·12 and 0·66 compared with intervention in all patients with COPD or no intervention.

Interpretation

Heterogeneous COPD can be well identified using AutoCOPD (https://lwj-lab.shinyapps.io/autocopd/) constructed by a subset of only ten QCT features. It may be generalizable across clinical settings and serve as a feasible tool for early detecting patients with mild or asymptomatic COPD to reduce delayed diagnosis in routine practice.

Funding

The National Natural Science Foundation of China, Guangzhou Laboratory, Natural Science Foundation of Guangdong Province, Guangzhou Municipal Science and Technology grant, State Key Laboratory of Respiratory Disease.

Prevalent findings on low-dose CT scan lung cancer screening: a French prospective pilot study

Despite significant therapeutic advances, lung cancer remains the biggest killer among cancers. In France, there is no national screening program against lung cancer. Thus, in this perspective, the Foch Hospital decided to implement a pilot and clinical low-dose CT screening program to evaluate the efficiency of such screening. The purpose of this study was to describe the prevalent findings of this low-dose CT screening program. Participants were recruited in the screening program through general practitioners (GPs), pharmacists, and specialists from June 2023 to June 2024. The inclusion criteria included male or female participants aged 50 to 80 years, current smokers or former smokers who had quit less than 15 years prior, with a smoking history of over 20 pack-years. Chest CT scans were conducted at Foch Hospital using a low-dose CT protocol based on volume mode with a multi-slice scanner (≥60 slices) without contrast injection. In total, 477 participants were recruited in the CT scan screening, 235 (49%) were males with a median age of 60 years [56-67] and 35 smoke pack-years [29-44] and 242 females (51%) with a median age of 60 years [55-60] and 30 smoke pack-years [25-40]. Eight participants showed positive nodules on CT scan, as a 1.7% rate. 66.7% of diagnosed cancers were in early stages (0-I). It is feasible to implement structured lung cancer screening using low-dose CT in a real-world setting among the general population. This approach successfully identifies most early-stage cancers that could be treated curatively.

Dark-field chest radiography signal characteristics in inspiration and expiration in healthy and emphysematous subjects

BACKGROUND

Dark-field chest radiography is sensitive to the lung alveolar structure. We evaluated the change of dark-field signal between inspiration and expiration.

METHODS

From 2018 to 2020, patients who underwent chest computed tomography (CT) were prospectively enrolled, excluding those with any lung condition besides emphysema visible on CT. Participants were imaged in both inspiration and expiration with a prototype dark-field chest radiography system. We calculated the total dark-field signal ∑DF and the dark-field coefficient ϵ, assumed to be proportional to the total number of alveoli and the alveolar density, respectively.

RESULTS

Eighty-eight subjects, aged 64 years ± 11 (mean ± standard deviation), 55 males, were enrolled. Dark-field signal in the lung projection appeared higher in expiration compared to inspiration. Over all participants, ∑DF was higher in inspiration (1.6 × 10-2 ± 0.4 × 10-2 m2) compared to expiration (1.5 × 10-2 ± 0.4 m2) (p < 0.001), with its expiration-to-inspiration not ratio being different for any emphysema subgroup. The dark-field coefficient ϵ was lower in inspiration (2.3 ± 0.6 m-1) compared to expiration (3.1 ± 1.1 m-1) (p < 0.001) over all participants. The dark-field coefficient in inspiration and expiration, as well as their ratio, was lower for at least moderate emphysema when compared to the control group (e.g., ϵ = 2.5 ± 1.0 m-1 for moderate emphysema in expiration versus ϵ = 3.6 ± 0.7 m-1 for participants without emphysema (p = 0.003).

CONCLUSION

The dark-field signal depends on the breathing state. Differences between breathing states are influenced by emphysema severity.

RELEVANCE STATEMENT

The patient's breathing state influences the dark-field chest radiograph, potentially impacting its diagnostic value.

KEY POINTS

Signal characteristics in dark-field chest radiography change between inspiration and expiration. The total dark-field signal decreases slightly from inspiration to expiration, while the dark-field coefficient increases substantially. The ratio of the total dark-field signal between expiration and inspiration is independent of emphysema severity, whereas the ratio of the dark-field coefficient depends on emphysema severity.

The association between emphysema detected on computed tomography and increased risk of lung cancer: a systematic review and meta-analysis

Background

Lung cancer, chronic obstructive pulmonary disease (COPD), and emphysema share common pathophysiological mechanisms, including diffuse chronic inflammation within lung tissue, oxidative stress, and lung destruction. This study aimed to evaluate the effectiveness of computed tomography (CT) imaging in predicting the risk of lung cancer development in patients with emphysema and COPD.

Methods

The databases of PubMed, Embase, Web of Science, and Cochrane Library were searched to identify studies examining the relationship between CT-detected emphysema, COPD, and the risk of developing lung malignancy. The severity of emphysema (from trace to severe) was assessed visually and quantitatively on CT. COPD severity was classified from Global Initiative for Chronic Obstructive Lung Disease (GOLD) I to GOLD IV. Quality Assessment of Diagnostic Accuracy Studies, version 2 (QUADAS-2) was used to assess risk of bias in the included studies. Pooled odds ratios (ORs) with their corresponding 95% confidence intervals (CIs) were calculated for overall and stratified analyses.

Results

Of the 6,114 studies screened, 12 (22,190 patients) were included. The overall pooled OR for lung cancer associated with CT-defined emphysema was 2.45 (95% CI: 2.01-2.99). In studies employing CT-based evaluation methods, the pooled OR for lung cancer was comparable between visual assessment (2.37; 95% CI: 1.93-2.80) and quantitative assessment (2.38; 95% CI: 1.85-3.05). The risk of lung cancer demonstrated a positive correlation with disease severity in both emphysema and COPD cases.

Conclusions

CT-defined emphysema was linked to an elevated risk of lung cancer, which was observed across various assessments. Moreover, the severity of COPD was found also to be a risk factor for the development of lung cancer.

Lung Cancer Screening and Incidental Findings: A Research Agenda: An Official American Thoracic Society Research Statement

Background: Lung cancer screening with low-dose computed tomography (LDCT) may uncover incidental findings (IFs) unrelated to lung cancer. There may be potential benefits from identifying clinically significant IFs that warrant intervention and potential harms related to identifying IFs that are not clinically significant but may result in additional evaluation, clinician effort, patient anxiety, complications, and excess cost. Objectives: To identify knowledge and research gaps and develop and prioritize research questions to address the approach to and management of IFs. Methods: We convened a multidisciplinary panel to review the available literature on IFs detected in lung cancer screening LDCT examinations, focusing on variability and standardizing reporting, management of IFs, and evaluation of the benefits and harms of IFs, particularly cardiovascular-related IFs. We used a three-round modified Delphi process to prioritize research questions. Results: This statement identifies knowledge gaps in 1) reporting of IFs, 2) management of IFs, and 3) identifying and reporting coronary artery calcification found on lung cancer screening LDCT. Finally, we present the panel's initial 36 research questions and the final 20 prioritized questions. Conclusions: This statement provides a prioritized research agenda to further efforts focused on evaluating, managing, and increasing awareness of IFs in lung cancer screening.

Imaging of Bronchoscopic Lung Volume Reduction Using Endobronchial Valves

Lung volume reduction is a treatment option for patients with severe emphysema and predominant chronic obstructive pulmonary disease that is refractory to medical treatment. These patients often experience symptoms associated with hyperinflation including dyspnea and exercise limitation. In recent years, bronchoscopic lung volume reduction using endobronchial valve (EBV) therapy has emerged as a U.S. Food and Drug Administration-approved and less invasive alternative to lung volume reduction surgery. The two approved one-way valves allow air to exit the lung but prohibit air from entering, with the intended goal of reducing hyperinflation. After patients meet clinical eligibility criteria, imaging has an integral role in preprocedural and postprocedural assessment. Findings from qualitative and quantitative preprocedural thin-section CT and perfusion scintigraphic analysis provides the characterization of emphysema, degree of collateral ventilation, and lung perfusion data necessary for target lobe selection, while aiding in detection of the presence of contraindications to the procedure, including suspicious pulmonary nodules, significant bronchiectasis, large bullae, and pleural adhesions. At procedure completion, chest radiography is required for assessment of complications, most commonly pneumothorax. Subsequent imaging may determine whether the procedure has successfully induced lobar atelectasis as well as the presence of additional complications such as infection and valve malposition or migration. Knowledge of EBV therapy and pertinent imaging findings is crucial in optimizing patient selection for the procedure, identifying complications, and evaluating treatment response. ©RSNA, 2025.

BreathVisionNet: A pulmonary-function-guided CNN-transformer hybrid model for expiratory CT image synthesis

BACKGROUND AND OBJECTIVE

Chronic obstructive pulmonary disease (COPD) has high heterogeneity in etiologies and clinical manifestations. Expiratory Computed tomography (CT) can effectively assess air trapping, aiding in disease diagnosis. However, due to concerns about radiation exposure and cost, expiratory CT is not routinely performed. Recent work on synthesizing expiratory CT has primarily focused on imaging features while neglecting patient-specific pulmonary function.

METHODS

To address these issues, we developed a novel model named BreathVisionNet that incorporates pulmonary function data to guide the synthesis of expiratory CT from inspiratory CT. An architecture combining a convolutional neural network and transformer is introduced to leverage the irregular phenotypic distribution in COPD patients. The model can better understand the long-range and global contexts by incorporating global information into the encoder. The utilization of edge information and multi-view data further enhances the quality of the synthesized CT. Parametric response mapping (PRM) can be estimated by using synthesized expiratory CT and inspiratory CT to quantify COPD phenotypes of the normal, emphysema, and functional small airway disease (fSAD), including their percentages, spatial distributions, and voxel distribution maps.

RESULTS

BreathVisionNet outperforms other generative models in terms of synthesized image quality. It achieves a mean absolute error, normalized mean square error, structural similarity index and peak signal-to-noise ratio of 78.207 HU, 0.643, 0.847 and 25.828 dB, respectively. Comparing the predicted and real PRM, the Dice coefficient can reach 0.732 (emphysema) and 0.560 (fSAD). The mean of differences between true and predicted fSAD percentage is 4.42 for the development dataset (low radiation dose CT scans), and 9.05 for an independent external validation dataset (routine dose), indicating that model has great generalizability. A classifier trained on voxel distribution maps can achieve an accuracy of 0.891 in predicting the presence of COPD.

CONCLUSIONS

BreathVisionNet can accurately synthesize expiratory CT images from inspiratory CT and predict their voxel distribution. The estimated PRM can help to quantify COPD phenotypes of the normal, emphysema, and fSAD. This capability provides additional insights into COPD diversity while only inspiratory CT images are available.

Novel Lobe-based Transformer model (LobTe) to predict emphysema progression in Alpha-1 Antitrypsin Deficiency

Emphysema, marked by irreversible lung tissue destruction, poses challenges in progression prediction due to its heterogeneity. Early detection is particularly critical for patients with Alpha-1 Antitrypsin Deficiency (AATD), a genetic disorder reducing ATT protein levels. Heterozygous carriers (PiMS and PiMZ) have variable AAT levels thus complicating their prognosis. This study introduces a novel prognostic model, the Lobe-based Transformer encoder (LobTe), designed to predict the annual change in lung density (ΔALD [g/L-yr]) using CT scans. Utilizing a global self-attention mechanism, LobTe specifically analyzes lobar tissue destruction to forecast disease progression. In parallel, we developed and compared a second model utilizing an LSTM architecture that implements a local subject-specific attention mechanism. Our methodology was validated on a cohort of 2,019 participants from the COPDGene study. The LobTe model demonstrated a small root mean squared error (RMSE=1.73 g/L-yr) and a notable correlation coefficient (ρ=0.61), explaining over 35% of the variability in ΔALD (R2= 0.36). Notably, it achieved a higher correlation coefficient of 0.68 for PiMZ heterozygous carriers, indicating its effectiveness in detecting early emphysema progression among smokers with mild to moderate AAT deficiency. The presented models could serve as a tool for monitoring disease progression and informing treatment strategies in carriers and subjects with AATD. Our code is available at github.com/acil-bwh/LobTe.

Oncologic surgical complications: Imaging approach and characteristics

Cancer is a disease that has multisystemic facets in its diagnosis and management. The treatment of choice with curative intent in many sites is surgery for early disease, commonly combined with neoadjuvant or adjuvant treatment. Oncologic surgery can have both locoregional and systemic complications, occasionally accentuated by multimodality treatment. While complications are of concern in any surgical setting, they may have specific intricate implications in the care of a cancer patient. Diagnostic imaging provides a non-invasive means of detecting complications and communicating the findings to the rest of the team for decision-making. Clinical clues, site-specific considerations and visual characteristics can aid the radiologist in arriving at a diagnosis of a locoregional oncologic surgical complication. Knowledge of systemic or distant complications, their clinical and imaging characteristics is a must-know following oncologic surgery. This article as an educational narrative review addresses imaging approach and characteristics of oncologic surgical complications, by pairing clinical considerations and imaging aspects.

Optimizing convolutional neural networks for Chronic Obstructive Pulmonary Disease detection in clinical computed tomography imaging

We aim to optimize the binary detection of Chronic Obstructive Pulmonary Disease (COPD) based on emphysema presence in the lung with convolutional neural networks (CNN) by exploring manually adjusted versus automated window-setting optimization (WSO) on computed tomography (CT) images. 7194 contrast-enhanced CT images (3597 with COPD; 3597 healthy controls) from 78 subjects were selected retrospectively (01.2018-12.2021) and preprocessed. For each image, intensity values were manually clipped to the emphysema window setting and a baseline 'full-range' window setting. Class-balanced train, validation, and test sets contained 3392, 1114, and 2688 images. The network backbone was optimized by comparing various CNN architectures. Furthermore, automated WSO was implemented by adding a customized layer to the model. The image-level area under the Receiver Operating Characteristics curve (AUC) [lower, upper limit 95% confidence] was utilized to compare model variations. Repeated inference (n = 7) on the test set showed that the DenseNet was the most efficient backbone and achieved a mean AUC of 0.80 [0.76, 0.85] without WSO. Comparably, with input images manually adjusted to the emphysema window, the DenseNet model predicted COPD with a mean AUC of 0.86 [0.82, 0.89]. By adding a customized WSO layer to the DenseNet, an optimal window in the proximity of the emphysema window setting was learned automatically, and a mean AUC of 0.82 [0.78, 0.86] was achieved. Detection of COPD with DenseNet models was improved by WSO of CT data to the emphysema window setting range.

Expiration CT Gas Trapping Measures with Texture-Based Radiomics Improves Association with Lung Function and Lung Function Decline in COPD

RATIONALE AND OBJECTIVES

Several methods quantify gas-trapping on expiration computed tomography (CT) images, but they do not consider the spatial relationship of voxels. The objective of this study was to determine if the addition of expiration CT texture-based radiomics features to existing gas-trapping measurements improves model performance for lung function, lung function decline, COPD classification and visual gas-trapping.

MATERIALS AND METHODS

CanCOLD participants performed spirometry, plethysmography and CT chest imaging at full-inspiration/expiration with radiologist-assessed gas-trapping. Quantitative CT measurements were performed: low attenuation areas≤-856HU (LAA856), ratio of expiratory-to-inspiratory mean lung attenuation (E/I MLA), and difference between expiratory-inspiratory lung volumes between -856 and -950 HU (RVC856-950). Texture-based radiomics analysis generated 95 features; LASSO regression coefficients were summed to create a representative variable (RadScore). Multivariable linear regression models determined associations for baseline RV/TLC, FEV1/FVC, FEV1, FEF25-75, and 6-year ΔFEV1, with established CT gas-trapping and RadScore. Binary logistic regression determined associations for COPD classification and visual gas-trapping.

RESULTS

1111 participants were investigated (n=234 never-smokers, n=325 at-risk, n=314 mild COPD, n=238 moderate-severe COPD). In separate models for baseline RV/TLC, FEV1/FVC, FEV1, and FEF25-75, ΔFEV1, COPD classification and visual gas-trapping, all CT gas-trapping and CT RadScore measurements were independently significant (p<0.05). When CT gas-trapping and CT RadScore were included in the same model, all model performance metrics improved significantly (p<0.05).

CONCLUSION

CT measures extracted from full-expiratory images that quantify the distribution, not just extent, of gas-trapping provide important information related to lung function and lung function decline in COPD.

SUMMARY STATEMENT

Full-expiratory CT texture-based radiomics improves model performance when used in combination with conventional gas-trapping measurements for lung function and lung function decline, COPD classification and presence of visual gas-trapping.

Emphysema or fibrosis progression in patients with combined pulmonary fibrosis and emphysema

BACKGROUND

Patients with combined pulmonary fibrosis and emphysema (CPFE) may experience emphysema or fibrosis progression on chest computed tomography (CT). This study aimed to investigate the relationship and prognosis in CPFE patients with emphysema or fibrosis progression.

METHODS

A total of 188 CPFE patients were included in our retrospective cohort study. The clinical presentations, radiographic features, and laboratory findings of the patients were reviewed.

RESULTS

Among CPFE patients, 28.1% exhibited emphysema progression and 43.3% showed fibrosis progression. Different higher tumour markers were observed in the emphysema or fibrosis progression groups. Smoking, definite usual interstitial pneumonia (UIP), and total extent of emphysema were risk factors for emphysema progression. Age, definite UIP, and mediastinal lymph node enlargement were risk factors for fibrosis progression. Patients with fibrosis progression had worse prognoses than patients without fibrosis progression (HR 2.159; 95% CI, 1.243-3.749; P = 0.006). However, the prognosis was similar between patients with and without emphysema progression (HR 0.839; 95% CI, 0.429-1.641; P = 0.608). There was no significant interaction between emphysema and fibrosis progression (p > 0.05).

CONCLUSIONS

In CPFE patients, emphysema and fibrosis progression had different higher tumour markers, risk factors, and prognosis effects. There was no significant interaction between emphysema and fibrosis progression. Fibrosis progression had a deleterious effect on prognosis, whereas emphysema progression did not affect prognosis. Therefore, the primary objective of CPFE treatment should be to halt or even reverse the progression of fibrosis. CPFE may be primarily a fibrotic disease, with emphysema being an incidental complication.

Co-occurrence of bronchiectasis, airway wall thickening, and emphysema in Chinese low-dose CT screening

OBJECTIVE

To assess the co-occurrence of incidental CT lung findings (emphysema, bronchiectasis, and airway wall thickening) as well as associated risk factors in low-dose CT (LDCT) lung cancer screening in a Chinese urban population.

METHODS

Data from 978 participants aged 40-74 years from the Chinese NELCIN-B3 urban population study who underwent LDCT screening were selected. CT scans were reviewed for incidental lung findings: emphysema, bronchiectasis and airway wall thickness. Emphysema was defined in three ways (≥ trace, ≥ mild, or ≥ moderate) depending on severity. Participants were described and stratified by presence or absence of incidental lung findings. Logistic regression analyses were performed to examine the relationship between participant characteristics and CT findings.

RESULTS

Mean age was 61.3 years ± 6.8 and 533 (54.6%) were female. 48% of participants had incidental lung findings: 19.9% had emphysema (≥ mild), 9.2% had bronchiectasis, and 35.7% had airway wall thickening. Among 978 participants, 2.1% showed all three findings. Multivariable analysis showed that higher age (OR: 1.06; 95% CI: 1.04-1.08; p < 0.001), male sex (OR: 1.68; 95% CI: 1.14-2.47; p = 0.008) smoking history (OR: 1.76; 95% CI: 1.02-3.03; p = 0.04 for former smokers; OR: 2.45; 95% CI: 1.59-3.78; p < 0.001 for current smokers), and the presence of respiratory symptoms (OR: 1.42; 95% CI: 1.01-2.00; p = 0.04) were associated with the risk of having at least one incidental lung findings. When different definitions of emphysema were used, the results remained consistent.

CONCLUSION

In a Chinese urban population undergoing LDCT lung cancer screening, 48% had at least one incidental CT lung finding, which was associated with higher age, male sex, questionnaire-based respiratory symptoms and smoking history.

KEY POINTS

Question Reporting of incidental lung findings that indicate lung disease risk lacks consensus in the cancer screening setting and needs evidence of co-occurrence in general populations. Findings Almost half of the 978 participants had at least one incidental lung CT finding; these were associated with older age, male sex, respiratory symptoms, and smoking history. Clinical relevance Incidental lung findings and related risk factors are often observed in low-dose CT lung cancer screening, and careful consideration of their relevance should be given to their inclusion in future screenings.

Pulmonary Hemodynamic Parameters Derived from 4D Flow MR Imaging Can Provide Sensitive Markers for Chronic Obstructive Pulmonary Disease (COPD) Patients with Right Ventricular Dysfunction

PURPOSE

To investigate the potential of 4D flow MRI-derived pulmonary hemodynamic parameters as sensitive markers for chronic obstructive pulmonary disease (COPD) patients with right ventricular dysfunction (RVD).

METHODS

We enrolled 15 COPD patients combined with RVD and 43 non-RVD participants, all of them underwent pulmonary function tests, thoracic CT and cardiac MR examinations, and the image post-processing analysis was completed. After comparing the 2 groups, the average flow velocity of the main pulmonary artery (Vavg-MPA) and the right pulmonary artery (Vavg-RPA) were identified as statistically significant confounding factors, propensity score matching was used to pair patients controlling for these 2 parameters. Univariate and multivariate logistic regression analyses were performed to assess the pulmonary hemodynamic parameters obtained from 4D flow MRI that could serve as sensitive markers for identifying COPD patients with RVD based on the matched participants dataset.

RESULTS

Fourteen COPD patients combined with RVD and 29 non-RVD participants were successfully matched. Logistic regression analysis showed that the decreased systolic pressure drop along the MRA-RPA tract (odds ratio [OR]: 0.31; 95% confidence interval [CI]: 0.12-0.78; P =0.013) and the presence of vortex (OR: 8.82; 95% CI: 1.11-70.36; P =0.040) were identified as independent risk factors for RVD in COPD patients.

CONCLUSION

Pulmonary hemodynamic parameters derived from 4D flow MRI, specifically the systolic pressure drop along the MPA-RPA tract and the presence of vortex in the main pulmonary artery, can serve as sensitive indicators for predicting right ventricular dysfunction in COPD patients.

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.

Chronic Airflow Limitation, Emphysema, and Impaired Diffusing Capacity in Relation to Smoking Habits in a Swedish Middle-aged Population

Rationale: Chronic obstructive pulmonary disease (COPD) includes respiratory symptoms and chronic airflow limitation (CAL). In some cases, emphysema and impaired diffusing capacity of the lung for carbon monoxide (DlCO) are present, but characteristics and symptoms vary with smoking exposure. Objective: To study the prevalence of CAL, emphysema, and impaired DlCO in relation to smoking and respiratory symptoms in a middle-aged population. Methods: We investigated 28,746 randomly invited individuals (52% women) aged 50-64 years across six Swedish sites. We performed spirometry, DlCO testing, and high-resolution computed tomography and asked for smoking habits and respiratory symptoms. CAL was defined as post-bronchodilator forced expiratory volume in 1 second divided by forced vital capacity (FEV1/FVC) < 0.7. Results: The overall prevalence was 8.8% for CAL, 5.7% for impaired DlCO (DlCO < LLN), and 8.8% for emphysema, with a higher prevalence in current smokers than in ex-smokers and never-smokers. The proportion of never-smokers among those with CAL, emphysema, and impaired DlCO was 32%, 19%, and 31%, respectively. Regardless of smoking habits, the prevalence of respiratory symptoms was higher among people with CAL and impaired DlCO than those with normal lung function. Asthma prevalence in never-smokers with CAL was 14%. In this group, asthma was associated with lower FEV1 and more respiratory symptoms. Conclusions: In this large population-based study of middle-aged people, CAL and impaired DlCO were associated with common respiratory symptoms. Self-reported asthma was not associated with CAL in never-smokers. Our findings suggest that CAL in never-smokers signifies a separate clinical phenotype that may be monitored and, possibly, treated differently from smoking-related COPD.

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.

[CT imaging of chronic obstructive pulmonary disease: What aspects and what role?]

Chronic obstructive pulmonary disease (COPD), commonly defined as irreversible airflow limitation, is associated with specific morphological changes involving all three parts of the lung, namely the bronchi, parenchyma and pulmonary vessels. In vivo imaging, with its ability to describe the different types of lung alterations and their regional distribution, helps to elucidate the relationship between lung structure and respiratory function. High-resolution computed tomography (CT) of the lung is the imaging modality best suited to assessing the pathological changes associated with airflow obstruction occurring in COPD. Over the last few decades, numerous studies have demonstrated the role of CT as a morphological and functional method conducive to the phenotyping of COPD patients. This review proposes to examine the data on CT imaging of COPD with a critical approach to recent data, and to determine the extent to which CT could be integrated into care or clinical research on patients with this/these disease(s).

CT features associated with contralateral recurrence of spontaneous pneumothorax

INTRODUCTION

Spontaneous pneumothorax recurs in 30-54% of patients without surgery. Identifying individuals likely to suffer a recurrence, who might benefit from pre-emptive surgery, is challenging. Previous meta-analysis suggested a relationship between contralateral recurrence and specific CT findings.

METHODS

We analysed CT images and recurrence rates of 243 patients seen by our tertiary referral pneumothorax service.

RESULTS

We validated the meta-analysis observation that contralateral lung cysts are associated with a higher risk of contralateral recurrence in younger individuals. Furthermore, we observed that the size of contralateral cysts to be associated with increased contralateral recurrence in younger patients.

CONCLUSION

The detection of contralateral lung cysts might therefore help identify younger patients more likely to benefit from pre-emptive surgery.

The Pivotal Role of Baseline LDCT for Lung Cancer Screening in the Era of Artificial Intelligence

In this narrative review, we address the ongoing challenges of lung cancer (LC) screening using chest low-dose computerized tomography (LDCT) and explore the contributions of artificial intelligence (AI), in overcoming them. We focus on evaluating the initial (baseline) LDCT examination, which provides a wealth of information relevant to the screening participant's health. This includes the detection of large-size prevalent LC and small-size malignant nodules that are typically diagnosed as LCs upon growth in subsequent annual LDCT scans. Additionally, the baseline LDCT examination provides valuable information about smoking-related comorbidities, including cardiovascular disease, chronic obstructive pulmonary disease, and interstitial lung disease (ILD), by identifying relevant markers. Notably, these comorbidities, despite the slow progression of their markers, collectively exceed LC as ultimate causes of death at follow-up in LC screening participants. Computer-assisted diagnosis tools currently improve the reproducibility of radiologic readings and reduce the false negative rate of LDCT. Deep learning (DL) tools that analyze the radiomic features of lung nodules are being developed to distinguish between benign and malignant nodules. Furthermore, AI tools can predict the risk of LC in the years following a baseline LDCT. AI tools that analyze baseline LDCT examinations can also compute the risk of cardiovascular disease or death, paving the way for personalized screening interventions. Additionally, DL tools are available for assessing osteoporosis and ILD, which helps refine the individual's current and future health profile. The primary obstacles to AI integration into the LDCT screening pathway are the generalizability of performance and the explainability.

Lung imaging in COPD and asthma

Chronic obstructive pulmonary disease (COPD) and asthma are common lung diseases with heterogeneous clinical presentations. Lung imaging allows evaluations of underlying pathophysiological changes and provides additional personalized approaches for disease management. This narrative review provides an overview of recent advances in chest imaging analysis using various modalities, such as computed tomography (CT), dynamic chest radiography, and magnetic resonance imaging (MRI). Visual CT assessment localizes emphysema subtypes and mucus plugging in the airways. Dedicated software quantifies the severity and spatial distribution of emphysema and the airway tree structure, including the central airway wall thickness, branch count and fractal dimension of the tree, and airway-to-lung size ratio. Nonrigid registration of inspiratory and expiratory CT scans quantifies small airway dysfunction, local volume changes and shape deformations in specific regions. Lung ventilation and diaphragm movement are also evaluated on dynamic chest radiography. Functional MRI detects regional oxygen transfer across the alveolus using inhaled oxygen and ventilation defects and gas diffusion into the alveolar-capillary barrier tissue and red blood cells using inhaled hyperpolarized 129Xe gas. These methods have the potential to determine local functional properties in the lungs that cannot be detected by lung function tests in patients with COPD and asthma. Further studies are needed to apply these technologies in clinical practice, particularly for early disease detection and tailor-made interventions, such as the efficient selection of patients likely to respond to biologics. Moreover, research should focus on the extension of healthy life expectancy in patients at higher risk and with established diseases.

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.

Deep learning reconstruction for zero echo time lung magnetic resonance imaging: impact on image quality and lesion detection

AIMS

This study aimed to examine the impact of deep-learning reconstruction (DLR) on zero echo time (ZTE) lung MRI.

MATERIALS AND METHODS

Fifty-nine patients who underwent both chest CT and ZTE lung magnetic resonance imaging (MRI) were enrolled. Noise reduction in ZTE lung MRI was compared using various DLR intensities (DLR-M, DLR-H) and conventional image filtering techniques (NF1 ∼ NF4). The normalized noise power spectrum (NPS) was analysed through phantom experiments. Image sharpness was evaluated using a blur metric. We compared subjective image quality and the detection of sub-centimetre nodules and emphysema between the original and noise-reduced images. Statistical analyses included the Wilcoxon signed-rank and McNemar's tests, with inter-reader agreement assessed via Kappa coefficients.

RESULTS

NPS peaks were lower in NF1 through NF4, DLR-M, and DLR-H compared to the original images. While the average spatial frequency of the NPS shifted towards lower frequencies with increasing NF levels, it remained unchanged with DLR. Blur metric values of NF1∼NF4 were significantly higher than those of the original images (p<0.008). However, there were no significant differences in blur metric values between DLR-M, DLR-H, and the original images. Image quality was rated highest for DLR-H, with a statistically significant improvement over the original (p<0.05). DLR-H showed higher diagnostic confidence for detecting sub-centimetre nodules than the original images. DLR-H showed higher diagnostic performance than the original for detecting emphysema.

CONCLUSIONS

DLR can improve ZTE lung MRI quality while preserving image texture and sharpness, thereby enhancing the potential of ZTE for evaluating pulmonary parenchymal disease.

Pulmonary emphysema: the assessment of lung perfusion with Dual-Energy CT and pulmonary scintigraphy

AIM

To assess the correlation of quantitative data of pulmonary Perfused Blood Volume (PBV) on Dual-Energy CT (DECT) datasets in patients with moderate - severe Pulmonary Emphysema (PE) with Lung Perfusion Scintigraphy (LPS) as the reference standard. The secondary endpoints are the correlation between the CT densitometric analysis and the visual assessment of parenchymal destruction with PBV.

MATERIALS AND METHODS

Patients with moderate - severe PE candidate to Lung Volumetric Reduction (LVR), with available a pre-procedural LS and a contrast-enhanced DECT were retrospectively included. DECT studies were performed with a 3rd generation Dual-Source CT and the PBV was obtained with a 3-material decomposition algorithm. The CT densitometric analysis was performed with a dedicated commercial software (Pulmo3D). The Goddard Score was used for visual assessment. The perfusion LS were performed after the administration of albumin macroaggregates labeled with 99mTechnetium. The image revision was performed by two radiologists or nuclear medicine physicians blinded, respectively, to LS and DECT data. The statistical analysis was performed with nonparametric tests.

RESULTS

Thirty-one patients (18 males, median age 69 y.o., interquartile range 62-71 y.o.) with moderate - severe PE (Median Goddard Score 14/20 and 31% of emphysematous parenchyma at quantitative CT) candidate to LVR were retrospectively included. The median enhancement on PBV was 17 HU. Significant correlation coefficients were demonstrated between lung PBV and LS, poor in apical regions (Rho = 0.1-0.2) and fair (Rho = 0.3-0.5) in middle and lower regions. No significant correlations were recorded between the CT densitometric analysis, the visual score, and the PBV.

CONCLUSIONS

Lung perfusion with PBV on DECT is feasible in patients with moderate - severe PE candidate to LVR, and has a poor to fair agreement with LPS.

What can we learn about bone density in COPD patients from a chest CT? A systematic review

We systematically reviewed the current research literature to 1) investigate whether there was a difference in bone mineral density (BMD) between chronic obstructive pulmonary disease (COPD) patients and non-COPD controls, 2) determine the influence of severity and subtype of COPD on BMD, and 3) determine the risk factors for lower BMD in COPD patients. The Web of Science and PubMed databases were searched on September 25, 2023. Studies where BMD was evaluated with computed tomography (CT) or quantitative CT in patients with COPD were included in the review. We collected data on the number of COPD patients, the average age, average body mass index, average predicted forced expiratory volume in one second (%) or Global Initiative for Chronic Obstructive Lung Disease stage, the average of low attenuation areas, the use of corticosteroid therapy, the use of osteoporosis therapy, the average BMD, and the location of BMD measurement. Twelve studies met our review criteria. Although in several studies COPD was associated with a decreased BMD, most of the studies suggested that COPD, especially in its milder forms, was not strongly associated with osteopenia or osteoporosis of the thoracic and lumbar spine.

Efficacy and safety of radial probe endobronchial ultrasound-guided biopsy for peripheral lung lesions in chronic obstructive pulmonary disease patients

Background

Chronic obstructive pulmonary disease (COPD) is associated with frequent complications after transthoracic biopsy. Radial probe endobronchial ultrasound-guided transbronchial lung biopsy (RP-EBUS-TBLB) is widely used to diagnose peripheral pulmonary lesions (PPLs). However, the efficacy and safety of this procedure for the diagnosis of PPLs in patients with COPD remain poorly understood. We investigated the usefulness of RP-EBUS-TBLB for diagnosing PPLs in patients with COPD.

Methods

This retrospective observational study aimed to identify clinical outcomes of RP-EBUS-TBLB in patients with COPD. A total of 175 patients with COPD and 439 patients without COPD were included in this study. RP-EBUS-TBLB was performed without fluoroscopy using a guide sheath.

Results

The overall diagnostic accuracies in patients with COPD and without COPD were 80.6% (141/175) and 78.8% (346/439), respectively. There was no significant difference in the diagnostic yield based on the severity of airflow limitation (80.0%, 81.4%, and 79.2% for mild, moderate, and severe to very airflow limitations, respectively; P=0.97). In patients with COPD, diagnostic yields for malignant and benign lesions were 85.6% (95/111) and 71.9% (46/64). In multivariable analyses, larger lesion size [≥30 mm; odds ratio (OR), 2.86; 95% confidence interval (CI): 1.10-7.45; P=0.03] and within the lesion on EBUS image (OR 9.29; 95% CI: 3.79-22.79; P<0.001) were associated with diagnostic success in patients with COPD, whereas lesion location of upper lobe (OR, 0.36; 95% CI: 0.14-0.92; P=0.03) were associated with diagnostic failure. The overall complication rate in our study was 7.4% (13/175) in patients with COPD. Pneumothorax occurred in 4.6% (8/175), and chest tube insertion was needed in 1.7% (3/175) of the patients.

Conclusions

RP-EBUS-TBLB can be used as an appropriate method to diagnose PPLs in patients with COPD. The size of the lesion (≥30 mm) and having the probe within the lesion were important for successful diagnosis. The location of the lesion in the upper lobe is associated with diagnostic failure. No difference was observed in the diagnostic yield based on the severity of airflow limitation. The complication rates were acceptable.

What can we learn about bone density in COPD patients from a chest CT? A systematic review

We systematically reviewed the current research literature to 1) investigate whether there was a difference in bone mineral density (BMD) between chronic obstructive pulmonary disease (COPD) patients and non-COPD controls, 2) determine the influence of severity and subtype of COPD on BMD, and 3) determine the risk factors for lower BMD in COPD patients. The Web of Science and PubMed databases were searched on September 25, 2023. Studies where BMD was evaluated with computed tomography (CT) or quantitative CT in patients with COPD were included in the review. We collected data on the number of COPD patients, the average age, average body mass index, average predicted forced expiratory volume in one second (%) or Global Initiative for Chronic Obstructive Lung Disease stage, the average of low attenuation areas, the use of corticosteroid therapy, the use of osteoporosis therapy, the average BMD, and the location of BMD measurement. Twelve studies met our review criteria. Although in several studies COPD was associated with a decreased BMD, most of the studies suggested that COPD, especially in its milder forms, was not strongly associated with osteopenia or osteoporosis of the thoracic and lumbar spine.

Impact of HHIP gene polymorphisms on phenotypes, serum IL-17 and IL-18 in COPD patients of the Chinese Han population

BACKGROUND

Genetic factors, including the Hedgehog Interacting Protein (HHIP) gene, play a crucial role in Chronic Obstructive Pulmonary Disease (COPD) susceptibility. This study examines the association between HHIP gene polymorphisms and COPD susceptibility, phenotypes, and serum IL-17 and IL-18 levels in a Han Chinese population.

METHODS

A case-control study was conducted with 300 COPD patients and 300 healthy controls in Chinese Han population. Participants underwent genotyping for HHIP gene polymorphisms, pulmonary function tests, and quantitative CT scans. DNA samples were sequenced using a custom chip targeting the HHIP gene. Serum IL-17 and IL-18 levels were measured by enzyme-linked immunosorbent assay. Associations between SNPs, COPD susceptibility, and phenotypes were analyzed using logistic and multiple linear regression models, adjusting for confounders.

RESULTS

Our study identified the rs11100865 polymorphism in the HHIP gene as significantly associated with COPD susceptibility (OR 2.479, 95% CI 1.527-4.024, P = 2.39E-04) after screening 114 SNPs through rigorous quality control. Stratified analyses further indicated this association was particularly in individuals aged 60 or older. Serum levels of IL-17 and IL-18 were significantly elevated in COPD patients compared to controls, with rs11100865 showing a notable association with IL-18 levels (B = 49.654, SE = 19.627, P = 0.012). However, no significant associations were observed between rs11100865 and serum IL-17 levels, COPD-related imaging parameters, or clinical phenotypes.

CONCLUSION

This study identified a significant association between HHIP gene polymorphisms and COPD susceptibility in a Han Chinese population, with connections to inflammation, but found no significant associations between this SNP and COPD-related imaging or clinical phenotypes.

TRIAL REGISTRATION

www.chictr.org.cn ID: ChiCTR2300071579 2023-05-18.

Discordant definitions of small airway dysfunction between spirometry and parametric response mapping: the HRCT-based study

OBJECTIVES

To analyze the lung structure of small airway dysfunction (SAD) defined by spirometry and parametric response mapping (PRM) using high-resolution computed tomography (HRCT), and to analyze the predictive factors for SAD.

METHODS

A prospective study was conducted with 388 participants undergoing pulmonary function test (PFT) and inspiratory-expiratory chest CT scans. The clinical data and HRCT assessments of SAD patients defined by both methods were compared. A prediction model for SAD was constructed based on logistic regression.

RESULTS

SAD was defined in 122 individuals by spirometry and 158 by PRM. In HRCT visual assessment, emphysema, tree-in-bud sign, and bronchial wall thickening have higher incidence in SAD defined by each method. (p < 0.001). Quantitative CT showed that spirometry-SAD had thicker airway walls (p < 0.001), smaller lumens (p = 0.011), fewer bronchi (p < 0.001), while PRM-SAD had slender blood vessels. Predictive factors for spirometry-SAD were age, male gender, the volume percentage of emphysema in PRM (PRMEmph), tree-in-bud sign, bronchial wall thickening, bronchial count; for PRM-SAD were age, male gender, BMI, tree-in-bud sign, emphysema, the percentage of blood vessel volume with a cross-sectional area less than 1 mm2 (BV1/TBV). The area under curve (AUC) values for the fitted predictive models were 0.855 and 0.808 respectively.

CONCLUSIONS

Compared with PRM, SAD defined by spirometry is more closely related to airway morphology, while PRM is sensitive to early pulmonary dysfunction but may be interfered by pulmonary vessels. Models combining patient information and HRCT assessment have good predictive value for SAD.

CRITICAL RELEVANCE STATEMENT

HRCT reveals lung structural differences in small airway dysfunction defined by spirometry and parametric response mapping. This insight aids in understanding methodological differences and developing radiological tools for small airways that align with pathophysiology.

KEY POINTS

Spirometry-SAD shows thickened airway walls, narrowed lumen, and reduced branch count, which are closely related to airway morphology. PRM shows good sensitivity to early pulmonary dysfunction, although its assessment of SAD based on gas trapping may be affected by the density of pulmonary vessels and other lung structures. Combining patient information and HRCT features, the fitted model has good predictive performance for SAD defined by both spirometry and PRM (AUC values are 0.855 and 0.808, respectively).

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.

Low-Dose Whole-Chest Dynamic CT for the Assessment of Large Airway Collapsibility in Patients with Suspected Tracheobronchial Instability

Purpose To quantify tracheal collapsibility using low-dose four-dimensional (4D) CT and to compare visual and quantitative 4D CT-based assessments with assessments from paired inspiratory-expiratory CT, bronchoscopy, and spirometry. Materials and Methods The authors retrospectively analyzed 4D CT examinations (January 2016-December 2022) during shallow respiration in 52 patients (mean age, 66 years ± 12 [SD]; 27 female, 25 male), including 32 patients with chronic obstructive pulmonary disease (mean forced expiratory volume in 1 second percentage predicted [FEV1%], 50% ± 27), with suspected tracheal collapse. Paired CT data were available for 27 patients and bronchoscopy data for 46 patients. Images were reviewed by two radiologists in consensus, classifying patients into three groups: 50% or greater tracheal collapsibility, less than 50% collapsibility, or fixed stenosis. Changes in minimal tracheal lumen area, tracheal volume, and lung volume from inspiration to expiration were quantified using YACTA software. Tracheal collapsibility between groups was compared employing one-way analysis of variance (ANOVA). For related samples within one group, ANOVA with repeated measures was used. Spearman rank order correlation coefficient was calculated for collapsibility versus pulmonary function tests. Results At 4D CT, 25 of 52 (48%) patients had tracheal collapsibility of 50% or greater, 20 of 52 (38%) less than 50%, and seven of 52 (13%) had fixed stenosis. Visual assessment of 4D CT detected more patients with collapsibility of 50% or greater than paired CT, and concordance was 41% (P < .001). 4D CT helped identify more patients with tracheal collapsibility of 50% or greater than did bronchoscopy, and concordance was 74% (P = .39). Mean collapsibility of tracheal lumen area and volume at 4D CT were higher for 50% or greater visually assessed collapsibility (area: 53% ± 9 and lumen: 52% ± 10) compared with the less than 50% group (27% ± 9 and 26% ± 6, respectively) (P < .001), whereas both tracheal area and volume were stable for the fixed stenosis group (area: 16% ± 12 and lumen: 21% ± 11). Collapsibility of tracheal lumen area and volume did not correlate with FEV1% (rs = -0.002 to 0.01, P = .99-.96). Conclusion The study demonstrated that 4D CT is feasible and potentially more sensitive than paired CT for central airway collapse. Expectedly, FEV1% was not correlated with severity of tracheal collapsibility. Keywords: CT-Quantitative, Tracheobronchial Tree, Chronic Obstructive Pulmonary Disease, Imaging Postprocessing, Thorax Supplemental material is available for this article. © RSNA, 2024.

Proposal for a computed tomography score to predict major complications requiring hospitalization after percutaneous lung biopsy: a single-center retrospective study

Background

Image-guided percutaneous lung biopsy (PLB) may lead to major complications requiring hospitalization. This study aims to evaluate the rate of major PLB complications and determine a predictive computed tomography (CT) score to define patients requiring hospitalization due to these complications.

Methods

This single-center retrospective study included all PLBs performed from July 2019 to December 2020 in Nimes University Hospital, France. Patients who were undergoing thermo-ablation during the same procedure or for whom PLB procedure data were not available were excluded. All major complications leading to hospitalization were recorded. A Percutaneous Image-guided Lung biopsy In/out Patient score (PILIP) based on variables significantly associated with major complications was calculated by multivariate analysis.

Results

A total of 240 consecutive patients (160 men, 80 women; mean age: 67.3±10.5 years) were included. The major complication rate was 10.4%. Length of lung parenchyma traversed <20 vs. 20-40 mm [P=0.017, odds ratio (OR) =5.02; 95% confidence interval (CI): 1.33-18.92] and vs. >40 mm (P=0.010, OR =6.15; 95% CI: 1.54-24.53), middle vs. superior lobar location (P=0.011, OR =6.34; 95% CI: 1.53-26.31), emphysema along the needle pathway (P<0.0001, OR =10.96; 95% CI: 3.61-33.28), and pleural/scissural attraction (P=0.023, OR =3.50; 95% CI: 1.19-10.32) were independently associated with major complications. Based on these parameters, the PILIP made it possible to differentiate low-risk patients (PILIP <4) from those at high risk (PILIP ≥4) of major complications with 0.40 sensitivity (95% CI: 0.21-0.59), 0.95 specificity (95% CI: 0.93-0.98), a positive predictive value of 0.50 (95% CI: 0.28-0.72) and a negative predictive value of 0.93 (95% CI: 0.90-0.97).

Conclusions

PLB showed a major complication rate of 10.4%. The PILIP is an easy-to-use CT score for differentiating patients at a low or high risk of complications requiring hospitalization.

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.

Emphysema and lung cancer risk

Background

With increasing significance of lung cancer screening programs, it is essential to determine the group of participants, who would benefit the most from screening. In our study, we aimed to establish the correlation between lung emphysema and lung cancer risk.

Methods

The study design was cross-sectional. Low-dose computed tomography (LDCT) scans of 896 subjects from MOLTEST-BIS lung cancer screening program, including 100 subjects with detected lung cancer, were visually evaluated for the presence, type and severity of emphysema. Quantitative emphysema evaluation was performed with Siemens syngo.via Pulmo 3D application.

Results

Visually detected presence of centrilobular emphysema (CLE) correlated with male gender (P=0.02), age (P<0.001) and pack-years of smoking (P=0.004), as well as with quantitative assessment of Emphysema Index (EI) (P=0.008), and with emphysema clusters of given size (Clas 1-4) Clas 1, Clas 3 and Clas 4 (P<0.001). Visually assessed severity grade of emphysema correlated with age (P<0.001), pack-years of smoking history (P=0.002) and EI (P<0.001). There was a correlation between lung cancer occurrence and pack-years (P<0.001), age (P<0.001), and presence of CLE (P<0.001) but no correlation with gender (P=0.88) and EI (P=0.32) was found. In the logistic regression model pack-years, age, qualitative severity of CLE and Clas 1 were significant factors correlated with lung cancer occurrence (P<0.001).

Conclusions

Qualitative and quantitative emphysema evaluation correlate with each other. Both, presence and severity of CLE correlate with higher incidence of lung cancer. Severity of visually assessed emphysema, age and pack-years of smoking are significant predictors of lung cancer occurrence.

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.