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A 54-year-old woman presented with persistent productive cough, found to have an endobronchial tumor which obstructed the left upper lobe bronchus. Histopathological examination of a transbronchial biopsy of the endobronchial tumor suggested leiomyosarcoma. A positron emission tomography (PET)-CT revealed uterus tumor with moderate uptake of 18F-fluorodeoxyglucose, suggesting uterine malignancies. From the results of histological findings of the resected uterus and the biopsied bronchial specimen, she was diagnosed with uterine leiomyosarcoma and endobronchial metastasis. The systematic use of PET-CT could be useful for patients presenting with tumors that cause endobronchial metastasis of leiomyosarcomas.

Abstract Background The composite physiologic index (CPI) was developed to estimate the extent of interstitial lung disease (ILD) in idiopathic pulmonary fibrosis (IPF) patients based on pulmonary function tests (PFTs). The CALIPER-revised version of the CPI (CALIPER-CPI) was also developed to estimate the volume fraction of ILD measured by CALIPER, an automated quantitative CT postprocessing software. Recently, artificial intelligence-based quantitative CT image analysis software (AIQCT), which can be used to quantify the bronchial volume separately from the ILD volume, was developed and validated in IPF. The aim of this study was to develop AIQCT-derived CPI formulas to quantify CT abnormalities in IPF and to investigate the associations of these CPI formulas with survival. Methods The first cohort included 116 patients with IPF. In this cohort, ILD, bronchial, and hyperlucent volumes on CT were quantified using AIQCT. New CPI formulas were developed based on PFTs to estimate the volume fraction of ILD (ILD-CPI), the sum of the ILD and bronchial volume fractions (ILDB-CPI), and the sum of the ILD, bronchial and hyperlucent volume fractions (ILDBH-CPI). The associations of the original CPI, the CALIPER-CPI and the AIQCT-derived CPIs with survival were analyzed in the first cohort and in a second cohort of patients with IPF (n = 72). Results In the first cohort, over a median observation time of 92.8 months, 79 patients (68.1%) died, and one patient (0.9%) underwent living-donor lung transplantation. The original CPI, the CALIPER-CPI, and all AIQCT-derived CPIs were associated with overall survival (hazard ratios: 1.07–1.22). The C-index of the ILDB-CPI (0.759) was the highest among all AIQCT-derived CPIs and was comparable to that of the original CPI (0.765) and the CALIPER-CPI (0.749). The C-index of the ILDBH-CPI (0.729) was lower than that of the other CPI variables. The second cohort yielded similar C-indices as the first cohort for the original CPI (0.738), CALIPER-CPI (0.757) and ILDB-CPI (0.749). Conclusions The ILDB-CPI can predict the outcomes of IPF patients with a similar performance to that of the original CPI and the CALIPER-CPI. Adding the hyperlucent volume to the CPI formula did not improve its predictive accuracy for mortality. Trial registration None (no health care interventions were performed).

Abstract Background Little is known about whether central airway morphological changes beyond traction bronchiectasis develop and affect clinical outcomes in patients with idiopathic pulmonary fibrosis (IPF). This study aimed to compare central airway structure comprehensively between patients with IPF, subjects with interstitial lung abnormality (ILA), and those without ILA (control) using computed tomography (CT). We further examined the prognostic impact of IPF-specific CT airway parameters in patients with IPF. Methods This retrospective study included male patients with IPF, and male health checkup subjects divided into those with ILA and control based on lung cancer screening CT. Using an artificial intelligence-based segmentation technique, the extent of fibrotic regions in the lung was quantified. After airway tree segmentation, CT parameters for central airway morphology, including the lumen area of the extrapulmonary airways (LAextra), wall and lumen area of the segmental/subsegmental intrapulmonary airways (WAintra and LAintra), tracheal distortion (tortuosity and curvature) and bifurcation angle of the main carina, were calculated. Results There were 106 patients with IPF, 53 subjects with ILA, and 1295 controls. Multivariable models adjusted for age, height and smoking history revealed that LAintra and WAintra were larger in both ILA and IPF, and that tracheal tortuosity and curvature were higher in IPF, but not in ILA, than in the control, whereas the bifurcation angle did not differ between the 3 groups. According to multivariable Cox proportional hazards models including only patients with IPF, increased WAintra was significantly associated with greater mortality (standardized hazard ratio [95% confidence interval] = 1.58 [1.17, 2.14]), independent of the volume of fibrotic regions, normal-appearing regions, or the whole airway tree in the lung. Conclusion Increased lumen area and wall thickening of the central airways may be involved in the pathogenesis of ILA and IPF, and wall thickening may affect the prognosis of patients with IPF.

Abstract Background 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. Methods 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. Results 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. Conclusion AI-based CT quantification of ILDvol% may be a reproducible method for identifying and monitoring ILAs in patients with COPD.

Objective This study aimed to investigate the utility of CT quantification of lung volume for predicting critical outcomes in COVID-19 patients.Methods This 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.Results The 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

Abstract Background 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. Methods 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. Results 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%. Conclusion 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.

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.

Respiratory oscillometry allows measuring respiratory resistance and reactance during tidal breathing and may predict exacerbations in patients with chronic obstructive pulmonary disease (COPD). While the Global Initiative for Chronic Obstructive Lung Disease (GOLD) advocates the ABCD classification tool to determine therapeutic approach based on symptom and exacerbation history, we hypothesized that in addition to spirometry, respiratory oscillometry complemented the ABCD tool to identify patients with a high risk of exacerbations. This study enrolled male outpatients with stable COPD who were prospectively followed-up over 5 years after completing mMRC scale and COPD assessment test (CAT) questionnaires, post-bronchodilator spirometry and respiratory oscillometry to measure resistance, reactance, and resonant frequency (Fres), and emphysema quantitation on computed tomography. Total 134 patients were classified into the GOLD A, B, C, and D groups (n = 48, 71, 5, and 9) based on symptoms on mMRC and CAT and a history of exacerbations in the previous year. In univariable analysis, higher Fres was associated with an increased risk of exacerbation more strongly than other respiratory oscillometry indices. Fres was closely associated with forced expiratory volume in 1 sec (FEV1). In multivariable Cox-proportional hazard models of the GOLD A and B groups, either lower FEV1 group or higher Fres group was associated with a shorter time to the first exacerbation independent of the GOLD group (A vs B) and emphysema severity. Adding respiratory oscillometry to the ABCD tool may be useful for risk estimation of future exacerbations in COPD patients without frequent exacerbation history.