We herein report a 66-year-old man with locally advanced non-small-cell lung cancer (NSCLC) who developed durvalumab-associated myocarditis. The patient underwent durvalumab administration every two ...weeks following concurrent chemoradiotherapy, without any adverse events or apparent disease progression. He presented with fatigue and dyspnea on exertion seven months after the first administration. Myocarditis was suspected based on laboratory data, an electrocardiogram, echocardiography, and magnetic resonance imaging findings. The definitive diagnosis was confirmed by a myocardial biopsy. Myocarditis was alleviated by cessation of durvalumab and corticosteroid therapy. This is a noteworthy case to describe late-onset myocarditis following the administration of durvalumab for NSCLC.
In patients with chronic obstructive pulmonary disease (COPD), emphysema, airway disease, and extrapulmonary comorbidities may cause various symptoms and impair physical activity. To investigate the ...relative associations of pulmonary and extrapulmonary manifestations with physical activity in symptomatic patients, this study enrolled 193 patients with COPD who underwent chest inspiratory/expiratory CT and completed COPD assessment test (CAT) and the Life-Space Assessment (LSA) questionnaires to evaluate symptom and physical activity. In symptomatic patients (CAT ≥ 10, n = 100), emphysema on inspiratory CT and air-trapping on expiratory CT were more severe and height-adjusted cross-sectional areas of pectoralis muscles (PM index) and adjacent subcutaneous adipose tissue (SAT index) on inspiratory CT were smaller in those with impaired physical activity (LSA < 60) than those without. In contrast, these findings were not observed in less symptomatic patients (CAT < 10). In multivariable analyses of the symptomatic patients, severe air-trapping and lower PM index and SAT index, but not CT-measured thoracic vertebrae bone density and coronary artery calcification, were associated with impaired physical activity. These suggest that increased air-trapping and decreased skeletal muscle and subcutaneous adipose tissue quantity are independently associated with impaired physical activity in symptomatic patients with COPD.
Interstitial lung abnormalities (ILAs) on CT may affect the clinical outcomes in patients with chronic obstructive pulmonary disease (COPD), but their quantification remains unestablished. This study ...examined whether artificial intelligence (AI)-based segmentation could be applied to identify ILAs using two COPD cohorts.
ILAs were diagnosed visually based on the Fleischner Society definition. Using an AI-based method, ground-glass opacities, reticulations, and honeycombing were segmented, and their volumes were summed to obtain the percentage ratio of interstitial lung disease-associated volume to total lung volume (ILDvol%). The optimal ILDvol% threshold for ILA detection was determined in cross-sectional data of the discovery and validation cohorts. The 5-year longitudinal changes in ILDvol% were calculated in discovery cohort patients who underwent baseline and follow-up CT scans.
ILAs were found in 32 (14%) and 15 (10%) patients with COPD in the discovery (n = 234) and validation (n = 153) cohorts, respectively. ILDvol% was higher in patients with ILAs than in those without ILA in both cohorts. The optimal ILDvol% threshold in the discovery cohort was 1.203%, and good sensitivity and specificity (93.3% and 76.3%) were confirmed in the validation cohort. 124 patients took follow-up CT scan during 5 ± 1 years. 8 out of 124 patients (7%) developed ILAs. In a multivariable model, an increase in ILDvol% was associated with ILA development after adjusting for age, sex, BMI, and smoking exposure.
AI-based CT quantification of ILDvol% may be a reproducible method for identifying and monitoring ILAs in patients with COPD.
The substantial heterogeneity of clinical presentations in patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumonia still requires robust chest computed tomography ...analysis to identify high-risk patients. While extension of ground-glass opacity and consolidation from peripheral to central lung fields on chest computed tomography (CT) might be associated with severely ill conditions, quantification of the central-peripheral distribution of ground glass opacity and consolidation in assessments of SARS-CoV-2 pneumonia remains unestablished. This study aimed to examine whether the central-peripheral distributions of ground glass opacity and consolidation were associated with severe outcomes in patients with SARS-CoV-2 pneumonia independent of the whole-lung extents of these abnormal shadows.
This multicenter retrospective cohort included hospitalized patients with SARS-CoV-2 pneumonia between January 2020 and August 2021. An artificial intelligence-based image analysis technology was used to segment abnormal shadows, including ground glass opacity and consolidation. The area ratio of ground glass opacity and consolidation to the whole lung (GGO%, CON%) and the ratio of ground glass opacity and consolidation areas in the central lungs to those in the peripheral lungs (GGO(C/P)) and (CON(C/P)) were automatically calculated. Severe outcome was defined as in-hospital death or requirement for endotracheal intubation.
Of 512 enrolled patients, the severe outcome was observed in 77 patients. GGO% and CON% were higher in patients with severe outcomes than in those without. Multivariable logistic models showed that GGO(C/P), but not CON(C/P), was associated with the severe outcome independent of age, sex, comorbidities, GGO%, and CON%.
In addition to GGO% and CON% in the whole lung, the higher the ratio of ground glass opacity in the central regions to that in the peripheral regions was, the more severe the outcomes in patients with SARS-CoV-2 pneumonia were. The proposed method might be useful to reproducibly quantify the extension of ground glass opacity from peripheral to central lungs and to estimate prognosis.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Abstract
Background
Computed tomography (CT) imaging and artificial intelligence (AI)-based analyses have aided in the diagnosis and prediction of the severity of COVID-19. However, the potential of ...AI-based CT quantification of pneumonia in assessing patients with COVID-19 has not yet been fully explored. This study aimed to investigate the potential of AI-based CT quantification of COVID-19 pneumonia to predict the critical outcomes and clinical characteristics of patients with residual lung lesions.
Methods
This retrospective cohort study included 1,200 hospitalized patients with COVID-19 from four hospitals. The incidence of critical outcomes (requiring the support of high-flow oxygen or invasive mechanical ventilation or death) and complications during hospitalization (bacterial infection, renal failure, heart failure, thromboembolism, and liver dysfunction) was compared between the groups of pneumonia with high/low-percentage lung lesions, based on AI-based CT quantification. Additionally, 198 patients underwent CT scans 3 months after admission to analyze prognostic factors for residual lung lesions.
Results
The pneumonia group with a high percentage of lung lesions (N = 400) had a higher incidence of critical outcomes and complications during hospitalization than the low percentage group (N = 800). Multivariable analysis demonstrated that AI-based CT quantification of pneumonia was independently associated with critical outcomes (adjusted odds ratio aOR 10.5, 95% confidence interval CI 5.59–19.7), as well as with oxygen requirement (aOR 6.35, 95% CI 4.60–8.76), IMV requirement (aOR 7.73, 95% CI 2.52–23.7), and mortality rate (aOR 6.46, 95% CI 1.87–22.3). Among patients with follow-up CT scans (N = 198), the multivariable analysis revealed that the pneumonia group with a high percentage of lung lesions on admission (aOR 4.74, 95% CI 2.36–9.52), older age (aOR 2.53, 95% CI 1.16–5.51), female sex (aOR 2.41, 95% CI 1.13–5.11), and medical history of hypertension (aOR 2.22, 95% CI 1.09–4.50) independently predicted persistent residual lung lesions.
Conclusions
AI-based CT quantification of pneumonia provides valuable information beyond qualitative evaluation by physicians, enabling the prediction of critical outcomes and residual lung lesions in patients with COVID-19.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Pulmonologists in primary care clinics are positioned between those in tertiary hospitals and general practitioners in clinics and are anticipated to promote hospital-clinic collaboration for ...patients with chronic obstructive pulmonary disease (COPD). However, the clinical features of patients in primary respiratory clinics are unclear. This multicenter study cross-sectionally compared the characteristics of patients with COPD in a respiratory clinic (n = 69) with those in a university hospital (n = 124). More patients visited the clinic for symptom relief without a referral, whereas more visited the hospital for consultation of abnormal spirometry/computed tomography (CT). The patients in the clinic showed female predilection, higher prevalence of current smokers, severe dyspnea, and concomitant heart failure, and higher CT-measured cross-sectional area ratio of pectoralis muscle to adjacent subcutaneous adipose tissue compared to those in the hospital (all p < 0.05). The observed differences between the two groups suggest the need to establish the role of primary pulmonologists in hospital-clinic collaboration for better COPD management.
Chest computed tomography (CT) is used to screen for lung cancer and evaluate pulmonary and extra-pulmonary abnormalities such as emphysema and coronary artery calcification, particularly in smokers. ...In real-world practice, lung abnormalities are visually assessed using high-contrast thin-slice images which are generated from raw scan data using sharp reconstruction kernels with the sacrifice of increased image noise. In contrast, accurate CT quantification requires low-contrast thin-slice images with low noise, which are generated using soft reconstruction kernels. However, only sharp-kernel thin-slice images are archived in many medical facilities due to limited data storage space. This study aimed to establish deep neural network (DNN) models to convert sharp-kernel images to soft-kernel-like images with a final goal to reuse historical chest CT images for robust quantitative measurements, particularly in completed previous longitudinal studies. By using pairs of sharp-kernel (input) and soft-kernel (ground-truth) images from 30 patients with chronic obstructive pulmonary disease (COPD), DNN models were trained. Then, the accuracy of kernel conversion based on the established DNN models was evaluated using CT from independent 30 smokers with and without COPD. Consequently, differences in CT values between new images converted from sharp-kernel images using the established DNN models and ground-truth soft-kernel images were comparable with the inter-scans variability derived from repeated phantom scans (6 times), showing that the conversion error was the same level as the measurement error of the CT device. Moreover, the Dice coefficients to quantify the similarity between low attenuation voxels on given images and the ground-truth soft-kernel images were significantly higher on the DNN-converted images than the Gaussian-filtered, median-filtered, and sharp-kernel images (
< 0.001). There were good agreements in quantitative measurements of emphysema, intramuscular adipose tissue, and coronary artery calcification between the converted and the ground-truth soft-kernel images. These findings demonstrate the validity of the new DNN model for kernel conversion and the clinical applicability of soft-kernel-like images converted from archived sharp-kernel images in previous clinical studies. The presented method to evaluate the validity of the established DNN model using repeated scans of phantom could be applied to various deep learning-based image conversions for robust quantitative evaluation.
Introduction
Airway eosinophilic inflammation is a pathological feature in a subgroup of patients with COPD and in some smokers with a high COPD risk. Although blood eosinophil count is used to ...define eosinophilic COPD, the association between blood eosinophil count and airway eosinophilic inflammation remains controversial. This cross-sectional study tested this association in smokers with and without COPD while considering potential confounders, such as smoking status and comorbidities.
Methods
Lung specimens were obtained from smokers with and without COPD and non-COPD never-smokers undergoing lung lobectomy. Those with any asthma history were excluded. The infiltration of eosinophils into the small airway wall was quantified on histological sections stained with major basic protein (MBP).
Results
The number of airway MBP-positive cells was greater in smokers (n=60) than in never-smokers (n=14). Smokers with and without COPD (n=30 each) exhibited significant associations between blood eosinophil count and airway MBP-positive cells (ρ=0.45 and 0.71). When smokers were divided into the high and low airway MBP groups based on their median value, blood eosinophil count was higher in the high-MBP group, with no difference in age, smoking status, comorbidities, emphysema or coronary artery calcification on computed tomography, and inhaled corticosteroid (ICS) use. The association between greater blood eosinophil count and the high-MBP group was confirmed in multivariable models adjusted for smoking status, airflow limitation and ICS use.
Conclusion
The blood eosinophil count may reflect eosinophilic inflammation in the small airways in smokers with and without COPD.
Centrilobular emphysema (CLE) and paraseptal emphysema (PSE) are observed in smokers with preserved ratio impaired spirometry (PRISm, defined as the ratio of forced expiratory volume in 1 s (FEV
1
) ...to forced vital capacity (FVC) ≥0.7 and FEV
1
<80%), but their prevalence and physiological impacts remain unestablished. This multicentre study aimed to investigate its prevalence and to test whether emphysema subtypes are differently associated with physiological impairments in smokers with PRISm.
Both never- and ever-smokers aged ≥40 years who underwent computed tomography (CT) for lung cancer screening and spirometry were retrospectively and consecutively enrolled at three hospitals and a clinic. Emphysema subtypes were visually classified according to the Fleischner system. Air-trapping was assessed as the ratio of FVC to total lung capacity on CT (TLC
CT
).
In 1046 never-smokers and 772 smokers with ≥10 pack-years, the prevalence of PRISm was 8.2% and 11.3%, respectively. The prevalence of PSE and CLE in smokers with PRISm was comparable to that in smokers with normal spirometry (PSE 43.7%
versus
36.2%, p=1.00; CLE 46.0%
versus
31.8%, p=0.21), but higher than that in never-smokers with PRISm (PSE 43.7%
versus
1.2%, p<0.01; CLE 46%
versus
4.7%, p<0.01) and lower than that in smokers with airflow limitation (PSE 43.7%
versus
71.0%, p<0.01; CLE 46%
versus
79.3%, p<0.01). The presence of CLE, but not PSE, was independently associated with reduced FVC/TLC
CT
in smokers with PRISm.
Both PSE and CLE were common, but only CLE was associated with air-trapping in smokers with PRISm, suggesting different physiological roles of these emphysema subtypes.
ObjectiveThis study aimed to investigate the utility of CT quantification of lung volume for predicting critical outcomes in COVID-19 patients.MethodsThis retrospective cohort study included 1200 ...hospitalised patients with COVID-19 from 4 hospitals. Lung fields were extracted using artificial intelligence-based segmentation, and the percentage of the predicted (%pred) total lung volume (TLC (%pred)) was calculated. The incidence of critical outcomes and posthospitalisation complications was compared between patients with low and high CT lung volumes classified based on the median percentage of predicted TLCct (n=600 for each). Prognostic factors for residual lung volume loss were investigated in 208 patients with COVID-19 via a follow-up CT after 3 months.ResultsThe incidence of critical outcomes was higher in the low TLCct (%pred) group than in the high TLCct (%pred) group (14.2% vs 3.3%, p<0.0001). Multivariable analysis of previously reported factors (age, sex, body mass index and comorbidities) demonstrated that CT-derived lung volume was significantly associated with critical outcomes. The low TLCct (%pred) group exhibited a higher incidence of bacterial infection, heart failure, thromboembolism, liver dysfunction and renal dysfunction than the high TLCct (%pred) group. TLCct (%pred) at 3 months was similarly divided into two groups at the median (71.8%). Among patients with follow-up CT scans, lung volumes showed a recovery trend from the time of admission to 3 months but remained lower in critical cases at 3 months.ConclusionLower CT lung volume was associated with critical outcomes, posthospitalisation complications and slower improvement of clinical conditions in COVID-19 patients.
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