Quantitative CT analysis in patients with pulmonary emphysema: is lung function influenced by concomitant unspecific pulmonary fibrosis?

Relationship between the severity of emphysematous change in the lung and morbidity after esophagectomy for esophageal cancer: A retrospective study on a novel strategy for risk prediction

BACKGROUND

Chronic obstructive pulmonary disease (COPD) prevalence increases post-esophagectomy morbidity. However, the association between COPD severity and post-esophagectomy morbidity remains unclear because of the lack of an objective method to classify COPD severity. Low attenuation volume ratio (LAVR) estimated using Ziostation2 may reflect the extent of emphysematous changes in the lungs and COPD severity, thereby predicting post-esophagectomy morbidity.

METHODS

A total of 776 patients who underwent curative McKeown esophagectomy for esophageal cancer between April 2005 and June 2021 were included. The patients were divided into high and low preoperative LAVR groups. Short-term outcomes between the groups were compared for patients who underwent open esophagectomy (OE) and minimally invasive esophagectomy (MIE).

RESULTS

A total of 219 (28%) patients were classified into the high LAVR group. High LAVR was significantly associated with disadvantageous patient characteristics such as advanced age, heavy smoking, and impaired respiratory function. Patients with high LAVR had a significantly higher incidence of severe morbidity and pneumonia after OE. High LAVR was an independent risk factor for severe morbidity (odds ratio [OR], 2.52; 95% confidence interval [CI]: 1.237-5.143; p = 0.011) and pneumonia (OR, 2.12; 95% CI: 1.003-4.493; p = 0.049) after OE. Meanwhile, LAVR was not correlated with the incidence of post-MIE morbidity.

CONCLUSIONS

LAVR may reflect COPD severity and predict severe morbidity and pneumonia after OE, but not after MIE. Less invasiveness of MIE may alleviate the effects of various disadvantageous backgrounds associated with high LAVR on worse short-term outcomes.

Quantitative CT parameters correlate with lung function in chronic obstructive pulmonary disease: A systematic review and meta-analysis

Objective

This study aimed to analyze the correlation between quantitative computed tomography (CT) parameters and airflow obstruction in patients with COPD.

Methods

PubMed, Embase, Cochrane and Web of Knowledge were searched by two investigators from inception to July 2022, using a combination of pertinent items to discover articles that investigated the relationship between CT measurements and lung function parameters in patients with COPD. Five reviewers independently extracted data, and evaluated it for quality and bias. The correlation coefficient was calculated, and heterogeneity was explored. The following CT measurements were extracted: percentage of lung attenuation area <-950 Hounsfield Units (HU), mean lung density, percentage of airway wall area, air trapping index, and airway wall thickness. Two airflow obstruction parameters were extracted: forced expiratory volume in the first second as a percentage of prediction (FEV₁%pred) and FEV₁ divided by forced expiratory volume lung capacity.

Results

A total of 141 studies (25,214 participants) were identified, which 64 (6,341 participants) were suitable for our meta-analysis. Results from our analysis demonstrated that there was a significant correlation between quantitative CT parameters and lung function. The absolute pooled correlation coefficients ranged from 0.26 (95% CI, 0.18 to 0.33) to 0.70 (95% CI, 0.65 to 0.75) for inspiratory CT and 0.56 (95% CI, 0.51 to 0.60) to 0.74 (95% CI, 0.68 to 0.80) for expiratory CT.

Conclusions

Results from this analysis demonstrated that quantitative CT parameters are significantly correlated with lung function in patients with COPD. With recent advances in chest CT, we can evaluate morphological features in the lungs that cannot be obtained by other clinical indices, such as pulmonary function tests. Therefore, CT can provide a quantitative method to advance the development and testing of new interventions and therapies for patients with COPD.

Use of machine learning models to predict prognosis of combined pulmonary fibrosis and emphysema in a Chinese population

Background

Combined pulmonary fibrosis and emphysema (CPFE) is a novel clinical entity with a poor prognosis. This study aimed to develop a clinical nomogram model to predict the 1-, 2- and 3-year mortality of patients with CPFE by using the machine learning approach, and to validate the predictive ability of the interstitial lung disease-gender-age-lung physiology (ILD-GAP) model in CPFE.

Methods

The data of CPFE patients from January 2015 to October 2021 who met the inclusion criteria were retrospectively collected. We utilized LASSO regression and multivariable Cox regression analysis to identify the variables associated with the prognosis of CPFE and generate a nomogram. The Harrell's C index, the calibration curve and the area under the receiver operating characteristic (ROC) curve (AUC) were used to evaluate the performance of the nomogram. Then, we performed likelihood ratio test, net reclassification improvement (NRI), integrated discrimination improvement (IDI) and decision curve analysis (DCA) to compare the performance of the nomogram with that of the ILD-GAP model.

Results

A total of 184 patients with CPFE were enrolled. During the follow-up, 90 patients died. After screening out, diffusing lung capacity for carbon monoxide (DLCO), right ventricular diameter (RVD), C-reactive protein (CRP), and globulin were found to be associated with the prognosis of CPFE. The nomogram was then developed by incorporating the above five variables, and it showed a good performance, with a Harrell's C index of 0.757 and an AUC of 0.800 (95% CI 0.736–0.863). Moreover, the calibration plot of the nomogram showed good concordance between the prediction probabilities and the actual observations. The nomogram also improved the discrimination ability of the ILD-GAP model compared to that of the ILD-GAP model alone, and this was substantiated by the likelihood ratio test, NRI and IDI. The significant clinical utility of the nomogram was demonstrated by DCA.

Conclusion

Age, DLCO, RVD, CRP and globulin were identified as being significantly associated with the prognosis of CPFE in our cohort. The nomogram incorporating the 5 variables showed good performance in predicting the mortality of CPFE. In addition, although the nomogram was superior to the ILD-GAP model in the present cohort, further validation is needed to determine the clinical utility of the nomogram.

Strain Analysis in Patients at High-Risk for COPD Using Four-Dimensional Dynamic-Ventilation CT

Purpose

To quantitatively identify abnormal lung motion in chronic obstructive pulmonary disease (COPD) using strain analysis, and further clarify the potential differences of deformation in COPD with different severity of airflow limitation.

Materials and Methods

Totally, 53 patients at high-risk for COPD were enrolled in this study. All CT examinations were performed on a 320-row MDCT scanner, and strain measurement based on dynamic-ventilation CT data was performed with a computational fluid dynamics analysis software (Micro Vec V3.6.2). The strain-related parameters derived from the whole expiration phase (PSmax-all, PSmean-all, Speedmax-all ), the first 2s of expiration phase (PSmax2s, PSmean2s, Speedmax2s ) were divided respectively by the changes in lung volume to adjust for the degree of expiration. Spearman rank correlation analysis was used to evaluate associations between the strain-related parameters and various spirometric parameters. Comparisons of the strain-related parameters between COPD and non-COPD patients, between GOLD I (mild airflow restriction) and GOLD II-IV (moderate to severe airflow restriction) were made using the Mann-Whitney U-test. Receiver-operating characteristic (ROC) analysis was performed to evaluate the diagnostic performance of the strain-related parameters for COPD. P <0.05 was considered statistically significant.

Results

Strain-related parameters demonstrated positive correlations with spirometric parameters (ρ=0.275~0.687, P<0.05), suggesting that heterogeneity in lung motion was related to abnormal spirometric results. Strain-related parameters can quantitatively distinguish COPD from non-COPD patients with moderate diagnostic significance with the AUC values ranged from 0.821 to 0.894. Furthermore, parameters of the whole expiration phase (PSmax-all, Speedmax-all) demonstrated significant differences (P=0.005; P=0.04) between COPD patients with mild and moderate to severe airflow limitation.

Conclusion

Strain-related parameters derived from dynamic-ventilation CT data covering the whole lung associated with lung function changes in COPD, reflecting the severity of airflow limitation in some degree, even though its utility in severe COPD patients remains to be investigated.

Diagnostic efficacy of visual subtypes and low attenuation area based on HRCT in the diagnosis of COPD

Background

Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease. Current gold standard criteria, pulmonary function tests (PFTs) may result in underdiagnosis of potential COPD patients. Therefore, we hypothesize that the combination of high-resolution computed tomography (HRCT) and clinical basic characteristics will enable the identification of more COPD patients.

Methods

A total of 284 patients with respiratory symptoms who were current or former smokers were included in the study, and were further divided into 5 groups of GOLD grade I–IV and non-COPD according to PFTs. All patients underwent inspiratory HRCT scanning and low attenuation area (LAA) was measured. Then they were divided into seven visual subtypes according to the Fleischner Society classification system. Non-parametric tests were used for exploring differences in basic characteristics and PFTs between different groups of enrolled patients and visual subtypes. Binary logistic regression was to find the influencing factors that affected the patients’ outcome (non-COPD vs GOLD I-IV). The area under the receiver operating characteristic curve (AUC-ROC) was to explore the diagnostic efficacy of LAA, visual subtypes, and combined basic characteristics related to COPD for COPD diagnosis. Finally, based on the cut-off values of ROC analysis, exploring HRCT features in patients who do not meet the diagnostic criteria but clinically suspected COPD.

Results

With the worsening severity of COPD, the visual subtypes gradually progressed (p < 0.01). There was a significant difference in LAA between GOLD II–IV and non-COPD (p < 0.0001). The diagnostic efficacy of LAA, visual subtypes, and LAA combined with visual subtypes for COPD were 0.742, 0.682 and 0.730 respectively. The diagnostic efficacy increased to 0.923–0.943 when basic characteristics were added (all p < 0.001). Based on the cut-off value of ROC analysis, LAA greater than 5.6, worsening of visual subtypes, combined with positive basic characteristics can help identify some potential COPD patients.

Conclusion

The heterogeneous phenotype of COPD requires a combination of multiple evaluation methods. The diagnostic efficacy of combining LAA, visual subtypes, and basic characteristics achieves good consistency with current diagnostic criteria.

Quantitative CT in mortality prediction in pulmonary fibrosis with or without emphysema

Aim:

We aimed to evaluate the quantitative CT analysis of patients with CPFE in comparison with IPF and emphysema.

Methods:

Patients with CPFE(n:36), IPF(n:38) and emphysema(n:32) were retrospectively included in the study with the approval of the ethics committee.

Results:

There was a positive correlation between total lung volume and FVC%, TLCO% and 6 MWT, and negative correlation between mMRC and mortality. Negative correlation was found between right, left lung density and FVC%, TLCO% and 6 MWT, and positive correlation between mortality. Also, total lung volume, right and left lung densities were significant in predicting mortality and cut-off values are ≤3831,> -778 and> -775, respectively (p = 0.040, 0.020, 0.013).

Conclusion:

Quantitative CT are guiding in predicting mortality of the disease.

A Comparative Evaluation of Computed Tomography Images for the Classification of Spirometric Severity of the Chronic Obstructive Pulmonary Disease with Deep Learning

Recently, deep learning applications in medical imaging have been widely applied. However, whether it is sufficient to simply input the entire image or whether it is necessary to preprocess the setting of the supervised image has not been sufficiently studied. This study aimed to create a classifier trained with and without preprocessing for the Global Initiative for Chronic Obstructive Lung Disease (GOLD) classification using CT images and to evaluate the classification accuracy of the GOLD classification by confusion matrix. According to former GOLD 0, GOLD 1, GOLD 2, and GOLD 3 or 4, eighty patients were divided into four groups (n = 20). The classification models were created by the transfer learning of the ResNet50 network architecture. The created models were evaluated by confusion matrix and AUC. Moreover, the rearranged confusion matrix for former stages 0 and ≥1 was evaluated by the same procedure. The AUCs of original and threshold images for the four-class analysis were 0.61 ± 0.13 and 0.64 ± 0.10, respectively, and the AUCs for the two classifications of former GOLD 0 and GOLD ≥ 1 were 0.64 ± 0.06 and 0.68 ± 0.12, respectively. In the two-class classification by threshold image, recall and precision were over 0.8 in GOLD ≥ 1, and in the McNemar–Bowker test, there was some symmetry. The results suggest that the preprocessed threshold image can be possibly used as a screening tool for GOLD classification without pulmonary function tests, rather than inputting the normal image into the convolutional neural network (CNN) for CT image learning.