Discordant Quantitative and Visual CT Assessments in the Diagnosis of Emphysema

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.

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.

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.

Early Diagnosis of High-Risk Chronic Obstructive Pulmonary Disease Based on Quantitative High-Resolution Computed Tomography Measurements

Purpose

Quantitative computed tomography (QCT) techniques, focusing on airway anatomy and emphysema, may help to detect early structural changes of COPD disease. This retrospective study aims to identify high-risk COPD participants by using QCT measurements.

Patients and Methods

We enrolled 140 participants from the Second Affiliated Hospital of Shenyang Medical College who completed inspiratory high-resolution CT scans, pulmonary function tests (PFTs), and clinical characteristics recorded. They were diagnosed Non-COPD by PFT value of FEV1/FVC >70% and divided into two groups according percentage predicted FEV1 (FEV1%), low-risk COPD group: FEV1% ≥ 95%, high-risk group: 80% < FEV1% < 95%. The QCT measurements were analyzed by the Student's t-test (or Mann-Whitney U-test) method. Then, feature candidates were identified using the LASSO method. Meanwhile, the correlation between QCT measurements and PFTs was assessed by the Spearman rank correlation test. Furthermore, support vector machine (SVM) was performed to identify high-risk COPD participants. The performance of the models was evaluated in terms of accuracy (ACC), sensitivity (SEN), specificity (SPE), F1-score, and area under the ROC curve (AUC), with p <0.05 considered statistically significant.

Results

The SVM based on QCT measurements achieved good performance in identifying high-risk COPD patients with 85.71% of ACC, 88.34% of SEN, 84.00% of SPE, 83.33% of F1-score, and 0.93 of AUC. Further, QCT measurements integration of clinical data improved the performance with an ACC of 90.48%. The emphysema index (%LAA-950) of left lower lung was negatively correlated with PFTs (P < 0.001). The airway anatomy indexes of lumen diameter (LD) were correlated with PFTs.

Conclusion

QCT measurements combined with clinical information could provide an effective tool for an early diagnosis of high-risk COPD. The QCT indexes can be used to assess the pulmonary function status of high-risk COPD.

Quantitative CT at Follow-Up of COVID-19 Pneumonia: Relationship with Pulmonary Function Tests

BACKGROUND

The role of quantitative chest computed tomography (CT) is controversial in the follow-up of patients with COVID-19 pneumonia. The aim of this study was to test during the follow-up of COVID-19 pneumonia the association between pulmonary function tests (PFTs) and quantitative parameters extrapolated from follow-up (FU) CT scans performed at least 6 months after COVID-19 onset.

METHODS

The study included patients older than 18 years old, admitted to the emergency department of our institution between 29 February 2020 and 31 December 2020, with a diagnosis of COVID-19 pneumonia, who underwent chest CT at admission and FU CT at least 6 months later; PFTs were performed within 6 months of FU CT. At FU CT, quantitative parameters of well-aerated lung and pneumonia extent were identified both visually and by software using CT density thresholds. The association between PFTs and quantitative parameters was tested by the calculation of the Spearman's coefficient of rank correlation (rho).

RESULTS

The study included 40 patients (38% females; median age 63 years old, IQR, 56-71 years old). A significant correlation was identified between low attenuation areas% (%LAAs) <950 Hounsfield units (HU) and both forced expiratory volume in 1s/forced vital capacity (FEV1/FVC) ratio (rho -0.410, 95% CIs -0.639--0.112, p = 0.008) and %DLCO (rho -0.426, 95% CIs -0.678--0.084, p = 0.017). The remaining quantitative parameters failed to demonstrate a significant association with PFTs (p > 0.05).

CONCLUSIONS

At follow-up, CT scans performed at least 6 months after COVID-19 pneumonia onset showed %LAAs that were inversely associated with %DLCO and could be considered a marker of irreversible lung damage.

Lobar emphysema ratio of more than 1% in the lobe with lung cancer as poor predictor for recurrence and overall survival in patients with stage I non-small cell lung cancer

BACKGROUND

The purpose of this study was to examine the relationship between the lobar emphysema ratio (LER) and tumor recurrence and survival in patients with stage I non-small cell lung cancer (NSCLC).

METHODS

We enrolled 258 patients with surgically proven stage I NSCLC. These patients underwent noncontrast chest CT, and pulmonary lobe segmentation and lobar emphysema quantification were performed using commercially available software. We assessed the LER in the lobe with lung cancer. We divided the patients into two groups according to the LER, and the cut-off value was 1. Furthermore, we analyzed the disease-free survival of high LER and other clinical factors after surgical resection.

RESULTS

The 258 patients were divided into two groups: low LER (n = 195) and high LER (n = 63). The right upper lobe was the most frequent location in lung cancer and the most severe location in emphysema. In the Kaplan‒Meier curve, high LER showed a significantly lower disease-free survival (8.21 ± 0.27 years vs 6.53 ± 0.60 years, p = 0.005) and overall survival (9.56 ± 0.15 years vs. 8.51 ± 0.49 years, p = 0.011) than low LER. Stage Ib (2.812 [1.661-4.762], p<0.001) and high LER (2.062 [1.191-3.571], p = 0.010) were poor predictors for disease-free survival in multivariate Cox regression analysis. Stage Ib (4.729 [1.674-13.356], p = 0.003) and high LER (3.346 [1.208-9.269], p = 0.020) were significant predictors for overall survival in multivariate Cox regression analysis.

CONCLUSION

A LER of more than 1% in the lobe with lung cancer is a poor predictor for cancer recurrence and overall survival in patients with stage I NSCLC.

Chronic Obstructive Pulmonary Disease and Small Airways Diseases

The small airways are a common target of injury within the lungs and may be affected by a wide variety of inhaled, systemic, and other disorders. Imaging is critical in the detection and diagnosis of small airways disease since significant injury may occur prior to pulmonary function tests showing abnormalities. The goal of this article is to describe the typical imaging findings and patterns of small airways diseases. An approach which divides the imaging appearances into four categories (tree-in-bud opacities, poorly defined centrilobular nodules, mosaic attenuation, and emphysema) will provide a framework in which to formulate appropriate and focused differential diagnoses.

When Treatment of Pulmonary Emphysema with Endobronchial Valves Did Not Work: Evaluation of Quantitative CT Analysis and Pulmonary Function Tests Before and After Valve Explantation

Purpose

To investigate changes in quantitative CT analysis (QCT) and pulmonary function tests (PFT) in pulmonary emphysema patients who required premature removal of endobronchial valves (EBV).

Patients and Methods

Our hospital’s medical records listed 274 patients with high-grade COPD (GOLD stages 3 and 4) and pulmonary emphysema who were treated with EBV to reduce lung volume. Prior to intervention, a complete evaluation was performed that included quantitative computed tomography analysis (QCT) of scans acquired at full inspiration and full expiration, pulmonary function tests (PFT), and paraclinical findings (6-minute walking distance test (6MWDT) and quality of life questionnaires). In 41 of these 274 patients, EBV treatment was unsuccessful and the valves had to be removed for various reasons. A total of 10 of these 41 patients ventured a second attempt at EBV therapy and underwent complete reevaluation. In our retrospective study, results from three time points were compared: Before EBV implantation (BL), after EBV implantation (TP2), and after EBV explantation (TP3). QCT parameters included lung volume, total emphysema score (TES, ie, the emphysema index) and the 15th percentile of lung attenuation (P15) for the whole lung and each lobe separately. Differences in these parameters between inspiration and expiration were calculated (Vol. Diff (%), TES Diff (%), P15 Diff (%)). The results of PFT and further clinical tests were taken from the patient’s records.

Results

We found persistent therapy effect in the target lobe even after valve explantation together with a compensatory hyperinflation of the rest of the lung. As a result of these two divergent effects, the volume of the total lung remained rather constant. Furthermore, there was a slight deterioration of the emphysema score for the whole lung, whereas the TES of the target lobe persistently improved.

Conclusion

Interestingly, we found evidence that, contrary to our expectations, unsuccessful EBV therapy can have a persistent positive effect on target lobe QCT scores.

AI-Driven Model for Automatic Emphysema Detection in Low-Dose Computed Tomography Using Disease-Specific Augmentation

The objective of this study is to evaluate the feasibility of a disease-specific deep learning (DL) model based on minimum intensity projection (minIP) for automated emphysema detection in low-dose computed tomography (LDCT) scans. LDCT scans of 240 individuals from a population-based cohort in the Netherlands (ImaLife study, mean age ± SD = 57 ± 6 years) were retrospectively chosen for training and internal validation of the DL model. For independent testing, LDCT scans of 125 individuals from a lung cancer screening cohort in the USA (NLST study, mean age ± SD = 64 ± 5 years) were used. Dichotomous emphysema diagnosis based on radiologists' annotation was used to develop the model. The automated model included minIP processing (slab thickness range: 1 mm to 11 mm), classification, and detection maps generation. The data-split for the pipeline evaluation involved class-balanced and imbalanced settings. The proposed DL pipeline showed the highest performance (area under receiver operating characteristics curve) for 11 mm slab thickness in both the balanced (ImaLife = 0.90 ± 0.05) and the imbalanced dataset (NLST = 0.77 ± 0.06). For ImaLife subcohort, the variation in minIP slab thickness from 1 to 11 mm increased the DL model's sensitivity from 75 to 88% and decreased the number of false-negative predictions from 10 to 5. The minIP-based DL model can automatically detect emphysema in LDCTs. The performance of thicker minIP slabs was better than that of thinner slabs. LDCT can be leveraged for emphysema detection by applying disease specific augmentation.