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Vukoja M, Dragisic D, Vujasinovic G, Djekic Malbasa J, Andrijevic I, Stojanovic G, Kopitovic I. The Prevalence of Emphysema in Patients Undergoing Lung Cancer Screening in a Middle-Income Country. Diseases 2025; 13:146. [PMID: 40422578 DOI: 10.3390/diseases13050146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2025] [Revised: 04/14/2025] [Accepted: 04/15/2025] [Indexed: 05/28/2025] Open
Abstract
Background: Chronic obstructive pulmonary disease (COPD) and lung cancer are the leading causes of death globally, which share common risk factors such as age and smoking exposure. In high-income countries, low-dose computed tomography (LDCT) lung cancer screening programs have decreased lung cancer mortality and facilitated the detection of emphysema, a key radiological indicator of COPD. This study aimed to assess the prevalence of emphysema during a pilot LDCT screening program for lung cancer in a middle-income country with a high smoking prevalence. Methods: A secondary analysis of the Lung Cancer Screening Database of the Autonomous Province of Vojvodina, Serbia, from 20 September 2020 to 30 May 2022. Persons aged 50-74 years, with a smoking history of ≥30 pack-years/or ≥20 pack-years with additional risks (chronic lung disease, prior pneumonia, malignancy other than lung cancer, family history of lung cancer, and professional exposure to carcinogens) were offered LDCT. Results: Of 1288 participants, mean age of 62.1 ± 6.7 years and 535 males (41.5%), 386 (30.0%) had emphysema. The majority of patients with emphysema (301/386, 78.0%) had no prior history of chronic lung diseases. Compared to the patients without emphysema, the patients with emphysema reported more shortness of breath (140/386, 36.3% vs. 276/902, 30.6%, p = 0.046), chronic cough (117/386, 30.3% vs. 209/902, 23.17% p = 0.007), purulent sputum expectoration (70/386, 18.1% vs. 95/902, 10.53%, p < 0.001), and weight loss (45/386, 11.7% vs. 63/902, 7.0%, p = 0.005). The patients with emphysema had more exposure to smoking (pack/years, 43.8 ± 18.8 vs. 39.3 ± 18.1, p < 0.001) and higher prevalence of solid or semisolid lung nodules (141/386, 36.5% vs. 278/902 30.8%, p = 0.04). Conclusions: Almost one-third of the patients who underwent the LDCT screening program in a middle-income country had emphysema that was commonly undiagnosed despite being associated with a significant symptom burden. Spirometry screening should be considered in high-risk populations.
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Affiliation(s)
- Marija Vukoja
- Institute for Pulmonary Diseases of Vojvodina, 21204 Sremska Kamenica, Serbia
- Faculty of Medicine, University of Novi Sad, 21000 Novi Sad, Serbia
| | - Dragan Dragisic
- Institute for Pulmonary Diseases of Vojvodina, 21204 Sremska Kamenica, Serbia
| | - Gordana Vujasinovic
- Institute for Pulmonary Diseases of Vojvodina, 21204 Sremska Kamenica, Serbia
| | - Jelena Djekic Malbasa
- Institute for Pulmonary Diseases of Vojvodina, 21204 Sremska Kamenica, Serbia
- Faculty of Medicine, University of Novi Sad, 21000 Novi Sad, Serbia
| | - Ilija Andrijevic
- Institute for Pulmonary Diseases of Vojvodina, 21204 Sremska Kamenica, Serbia
- Faculty of Medicine, University of Novi Sad, 21000 Novi Sad, Serbia
| | - Goran Stojanovic
- Institute for Pulmonary Diseases of Vojvodina, 21204 Sremska Kamenica, Serbia
- Faculty of Pharmacy, University Business Academy in Novi Sad, 21000 Novi Sad, Serbia
| | - Ivan Kopitovic
- Institute for Pulmonary Diseases of Vojvodina, 21204 Sremska Kamenica, Serbia
- Faculty of Medicine, University of Novi Sad, 21000 Novi Sad, Serbia
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Kerber B, Ensle F, Kroschke J, Strappa C, Larici AR, Frauenfelder T, Jungblut L. Assessment of Emphysema on X-ray Equivalent Dose Photon-Counting Detector CT: Evaluation of Visual Scoring and Automated Quantification Algorithms. Invest Radiol 2025; 60:291-298. [PMID: 39729642 DOI: 10.1097/rli.0000000000001128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2024]
Abstract
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.
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Affiliation(s)
- Bjarne Kerber
- From the Institute for Diagnostic and Interventional Radiology, University Hospital Zurich, University Zurich, Zurich, Switzerland (B.K., F.E., J.K., T.F., L.J.); Advanced Radiology Center, Department of Diagnostic Imaging and Oncological Radiotherapy, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy (C.S., A.R.L.); and Section of Radiology, Department of Radiological and Hematological Sciences, Università Cattolica del Sacro Cuore, Rome, Italy (A.R.L.)
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Prakaikietikul P, Tajarenmuang P, Losuriya P, Ina N, Ketpueak T, Kanthawang T. Non-cancerous CT findings as predictors of survival outcome in advanced non-small cell lung cancer patients treated with first-generation EGFR-TKIs. PLoS One 2025; 20:e0313577. [PMID: 39908320 PMCID: PMC11798445 DOI: 10.1371/journal.pone.0313577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2024] [Accepted: 10/26/2024] [Indexed: 02/07/2025] Open
Abstract
PURPOSE To identify non-cancerous factors from baseline CT chest affecting survival in advanced non-small cell lung cancer (NSCLC) treated with first-generation Epidermal Growth Factor Receptor-Tyrosine Kinase Inhibitors (EGFR-TKIs). METHODS Retrospective study of 172 advanced NSCLC patients treated with first-generation EGFR-TKIs as a first-line systemic treatment (January 2012 to September 2022). Baseline CT chest assessed visceral/subcutaneous fat (L1 level), sarcopenia, and myosteatosis (multiple levels), main pulmonary artery (MPA) size, MPA to aorta ratio, emphysema, and bone mineral density. Cox regression analyzed prognostic factors at 18-month outcome. RESULTS Median overall survival was 17.57 months (14.87-20.10) with 76 (44.19%) patients died at 18 months. Deceased had lower baseline BMI (21.10 ± 3.44) vs. survived (23.25 ± 4.45) (p < 0.001). Univariable analysis showed 5 significant prognostic factors: low total adiposity with/without cutoff [HR 2.65 (1.68-4.18), p < 0.001; 1.00 (0.99-1.00), p = 0.006;], low subcutaneous adipose tissue (SAT) with/without cutoff [HR 1.95 (1.23-3.11), p = 0.005; 0.99 (0.98-0.99), p = 0.005], low SAT index (SATI) with/without cutoff [1.74 (1.10-2.78), p = 0.019; 0.98 (0.97-0.99), p = 0.003], high VSR [1.67 (1.06-2.62), p = 0.026], and high MPA size with/without cutoff [2.23 (1.23-4.04), p = 0.005; 1.09 (1.04-1.16), p = 0.001]. MPA size, MPA size > 29 mm, and total adiposity ≤85 cm2 remained significant in multivariable analysis, adjusted by BMI [HR 1.14 (1.07-1.21), p < 0.001; 3.10 (1.81-5.28), p < 0.001; 3.91 (1.63-9.40), p = 0.002]. There was no significant difference of sarcopenic and myosteatotic parameters between the two groups. CONCLUSION In advanced EGFR-mutated NSCLC patients, assessing pre-treatment prognosis is warranted to predict the survival outcome and guide decision regarding EGFR-TKI therapy. Enlarged MPA size, low total adiposity, and low subcutaneous fat (lower SAT, lower SATI, and higher VSR) are indicators of poor survival. Large MPA size (>29 mm) or low total adiposity (≤85 cm2) alone predict 18-month death.
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Affiliation(s)
- Pakorn Prakaikietikul
- Department of Radiology, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
| | - Pattraporn Tajarenmuang
- Division of Pulmonary, Critical Care, and Allergy, Department of Internal Medicine, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
| | - Phumiphat Losuriya
- Division of Pulmonary, Critical Care, and Allergy, Department of Internal Medicine, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
| | - Natee Ina
- Radiological Technology Division, Department of Radiology, Faculty of Medicine, Prince of Songkla University, Songkla, Thailand
| | - Thanika Ketpueak
- Division of Oncology, Department of Internal Medicine, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
| | - Thanat Kanthawang
- Department of Radiology, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
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Fat M, Andersen T, Fazio JC, Park SC, Abtin F, Buhr RG, Phillips JE, Belperio J, Tashkin DP, Cooper CB, Barjaktarevic I. Association of bronchial disease on CT imaging and clinical definitions of chronic bronchitis in a single-center COPD phenotyping study. Respir Med 2024; 231:107733. [PMID: 38986793 DOI: 10.1016/j.rmed.2024.107733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 06/09/2024] [Accepted: 07/04/2024] [Indexed: 07/12/2024]
Abstract
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.
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Affiliation(s)
- Marisa Fat
- Graduate Education, University of California, Los Angeles (UCLA), Los Angeles, CA, USA; Anne Burnett Marion School of Medicine at TCU, Fort Worth, TX, USA
| | - Tyler Andersen
- Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Jane C Fazio
- Division of Pulmonary and Critical Care Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Seon Cheol Park
- Division of Pulmonology, Department of Internal Medicine, National Health Insurance Service Ilsan Hospital, Goyang, Republic of Korea
| | | | - Russell G Buhr
- Division of Pulmonary and Critical Care Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | | | - John Belperio
- Division of Pulmonary and Critical Care Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Donald P Tashkin
- Division of Pulmonary and Critical Care Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Christopher B Cooper
- Division of Pulmonary and Critical Care Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA; Exercise Physiology Research Laboratory, Department of Physiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Igor Barjaktarevic
- Division of Pulmonary and Critical Care Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
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Wiedbrauck D, Karczewski M, Schoenberg SO, Fink C, Kayed H. Artificial Intelligence-Based Emphysema Quantification in Routine Chest Computed Tomography: Correlation With Spirometry and Visual Emphysema Grading. J Comput Assist Tomogr 2024; 48:388-393. [PMID: 38110294 DOI: 10.1097/rct.0000000000001572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2023]
Abstract
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.
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Affiliation(s)
| | - Maciej Karczewski
- Department of Applied Mathematics, Wrocław University of Environmental and Life Sciences, Wroclaw, Poland
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Park H, Hwang EJ, Goo JM. Deep Learning-Based Kernel Adaptation Enhances Quantification of Emphysema on Low-Dose Chest CT for Predicting Long-Term Mortality. Invest Radiol 2024; 59:278-286. [PMID: 37428617 DOI: 10.1097/rli.0000000000001003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/12/2023]
Abstract
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.
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Affiliation(s)
- Hyungin Park
- From the Department of Radiology, Seoul National University Hospital, Seoul, South Korea (H.P., E.J.H., J.M.G.); and Department of Radiology, Seoul National University College of Medicine, Seoul, South Korea (J.M.G.)
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Zhang W, Zhao Y, Tian Y, Liang X, Piao C. Early Diagnosis of High-Risk Chronic Obstructive Pulmonary Disease Based on Quantitative High-Resolution Computed Tomography Measurements. Int J Chron Obstruct Pulmon Dis 2023; 18:3099-3114. [PMID: 38162987 PMCID: PMC10757779 DOI: 10.2147/copd.s436803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 12/15/2023] [Indexed: 01/03/2024] Open
Abstract
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.
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Affiliation(s)
- Wenxiu Zhang
- Institute of Research and Clinical Innovations, Neusoft Medical Systems Co, Ltd, Shanghai, People’s Republic of China
| | - Yu Zhao
- Radiology Department, Second Affiliated Hospital of Shenyang Medical College, Shenyang, Liaoning, People’s Republic of China
| | - Yuchi Tian
- Institute of Research and Clinical Innovations, Neusoft Medical Systems Co, Ltd, Shanghai, People’s Republic of China
| | - Xiaoyun Liang
- Institute of Research and Clinical Innovations, Neusoft Medical Systems Co, Ltd, Shanghai, People’s Republic of China
| | - Chenghao Piao
- Radiology Department, Second Affiliated Hospital of Shenyang Medical College, Shenyang, Liaoning, People’s Republic of China
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Colombi D, Petrini M, Risoli C, Mangia A, Milanese G, Silva M, Franco C, Sverzellati N, Michieletti E. Quantitative CT at Follow-Up of COVID-19 Pneumonia: Relationship with Pulmonary Function Tests. Diagnostics (Basel) 2023; 13:3328. [PMID: 37958224 PMCID: PMC10648873 DOI: 10.3390/diagnostics13213328] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 10/20/2023] [Accepted: 10/26/2023] [Indexed: 11/15/2023] Open
Abstract
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.
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Affiliation(s)
- Davide Colombi
- Radiology Unit, Department of Radiological Functions, AUSL Piacenza, Via Taverna 49, 29121 Piacenza, Italy; (M.P.); (C.R.)
| | - Marcello Petrini
- Radiology Unit, Department of Radiological Functions, AUSL Piacenza, Via Taverna 49, 29121 Piacenza, Italy; (M.P.); (C.R.)
| | - Camilla Risoli
- Radiology Unit, Department of Radiological Functions, AUSL Piacenza, Via Taverna 49, 29121 Piacenza, Italy; (M.P.); (C.R.)
| | - Angelo Mangia
- Pulmonology Unit, Department of Emergency, AUSL Piacenza, Via Taverna 49, 29121 Piacenza, Italy; (A.M.); (C.F.)
| | - Gianluca Milanese
- Scienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, Via Gramsci 14, 43126 Parma, Italy; (G.M.); (M.S.)
| | - Mario Silva
- Scienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, Via Gramsci 14, 43126 Parma, Italy; (G.M.); (M.S.)
| | - Cosimo Franco
- Pulmonology Unit, Department of Emergency, AUSL Piacenza, Via Taverna 49, 29121 Piacenza, Italy; (A.M.); (C.F.)
| | - Nicola Sverzellati
- Scienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, Via Gramsci 14, 43126 Parma, Italy; (G.M.); (M.S.)
| | - Emanuele Michieletti
- Radiology Unit, Department of Radiological Functions, AUSL Piacenza, Via Taverna 49, 29121 Piacenza, Italy; (M.P.); (C.R.)
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Lee JP, Na JB, Choi HC, Choi HY, Kim JE, Shin HS, Won JH, Jo SH, Hong SJ, Yang WJ, Kim YW, Koo BJ, Jang IS, Park MJ. 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. PLoS One 2023; 18:e0281715. [PMID: 36787324 PMCID: PMC9928128 DOI: 10.1371/journal.pone.0281715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 01/30/2023] [Indexed: 02/15/2023] Open
Abstract
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.
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Affiliation(s)
- Jeong Pyo Lee
- Department of Radiology, Gyeongsang National University School of Medicine, Gyeongsang National University Hospital, Jinju, Korea
| | - Jae Bum Na
- Department of Radiology, Gyeongsang National University School of Medicine, Gyeongsang National University Hospital, Jinju, Korea
| | - Ho Cheol Choi
- Department of Radiology, Gyeongsang National University School of Medicine, Gyeongsang National University Hospital, Jinju, Korea
| | - Hye Young Choi
- Department of Radiology, Gyeongsang National University School of Medicine, Gyeongsang National University Hospital, Jinju, Korea
| | - Ji Eun Kim
- Department of Radiology, Gyeongsang National University School of Medicine, Gyeongsang National University Hospital, Jinju, Korea
| | - Hwa Seon Shin
- Department of Radiology, Gyeongsang National University School of Medicine, Gyeongsang National University Hospital, Jinju, Korea
| | - Jung Ho Won
- Department of Radiology, Gyeongsang National University School of Medicine, Gyeongsang National University Hospital, Jinju, Korea
| | - Sa Hong Jo
- Department of Radiology, Gyeongsang National University School of Medicine, Gyeongsang National University Hospital, Jinju, Korea
| | - Seok Jin Hong
- Department of Radiology, Gyeongsang National University School of Medicine, Gyeongsang National University Hospital, Jinju, Korea
| | - Won Jeong Yang
- Department of Radiology, Gyeongsang National University School of Medicine, Gyeongsang National University Hospital, Jinju, Korea
| | - Yang Won Kim
- Department of Radiology, Gyeongsang National University School of Medicine, Gyeongsang National University Hospital, Jinju, Korea
| | - Byeong Ju Koo
- Department of Radiology, Gyeongsang National University School of Medicine, Gyeongsang National University Hospital, Jinju, Korea
| | - In Seok Jang
- Department of Cardiothoracic Surgery, Gyeongsang National University School of Medicine, Gyeongsang National University Hospital, Jinju, Korea
| | - Mi Jung Park
- Department of Radiology, Gyeongsang National University School of Medicine, Gyeongsang National University Hospital, Jinju, Korea
- * E-mail:
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10
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Elicker BM. Chronic Obstructive Pulmonary Disease and Small Airways Diseases. Semin Respir Crit Care Med 2022; 43:825-838. [PMID: 36252610 DOI: 10.1055/s-0042-1755567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
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.
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Affiliation(s)
- Brett M Elicker
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California
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11
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Leppig JA, Song L, Voigt DC, Feldhaus FW, Ruwwe-Gloesenkamp C, Saccomanno J, Lassen-Schmidt BC, Neumann K, Leitner K, Hubner RH, Doellinger F. 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. Int J Chron Obstruct Pulmon Dis 2022; 17:2553-2566. [PMID: 36304970 PMCID: PMC9596192 DOI: 10.2147/copd.s367667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 09/17/2022] [Indexed: 11/05/2022] Open
Abstract
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.
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Affiliation(s)
- Jonas Alexander Leppig
- Department of Radiology, Charité Universitätsmedizin Berlin, Berlin, Germany,Correspondence: Jonas Alexander Leppig, Department of Radiology, Charité Universitätsmedizin Berlin, Charité Campus Virchow-Klinikum, Augustenburger Platz 1, Berlin, 13353, Germany, Tel + 49 30 450 627 283, Fax + 49 30 450 527 911, Email
| | - Lan Song
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China
| | - Dorothea C Voigt
- Department of Radiology, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Felix W Feldhaus
- Department of Radiology, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Christoph Ruwwe-Gloesenkamp
- Department of Internal Medicine/Infectious Diseases and Respiratory Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Jacopo Saccomanno
- Department of Internal Medicine/Infectious Diseases and Respiratory Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
| | | | - Konrad Neumann
- Institute of Biometrics and Clinical Epidemiology, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Katja Leitner
- Department of Internal Medicine, Kantonsspital Aarau AG, Aarau, Switzerland
| | - Ralf H Hubner
- Department of Internal Medicine/Infectious Diseases and Respiratory Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Felix Doellinger
- Department of Radiology, Charité Universitätsmedizin Berlin, Berlin, Germany
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12
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Nagaraj Y, Wisselink HJ, Rook M, Cai J, Nagaraj SB, Sidorenkov G, Veldhuis R, Oudkerk M, Vliegenthart R, van Ooijen P. AI-Driven Model for Automatic Emphysema Detection in Low-Dose Computed Tomography Using Disease-Specific Augmentation. J Digit Imaging 2022; 35:538-550. [PMID: 35182291 PMCID: PMC9156637 DOI: 10.1007/s10278-022-00599-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 01/03/2022] [Accepted: 01/29/2022] [Indexed: 12/15/2022] Open
Abstract
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.
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Affiliation(s)
- Yeshaswini Nagaraj
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands ,DASH, Machine Learning Lab, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Hendrik Joost Wisselink
- Department of Radiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Mieneke Rook
- Department of Radiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands ,Department of Radiology, Martini Hospital Groningen, Groningen, The Netherlands
| | - Jiali Cai
- Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Sunil Belur Nagaraj
- Department of Clinical Pharmacy and Pharmacology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Grigory Sidorenkov
- Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Raymond Veldhuis
- Faculty of Electrical Engineering, Mathematics Computer Science (EWI), Data Management Biometrics (DMB), University of Twente, Enschede, The Netherlands
| | - Matthijs Oudkerk
- Faculty of Medical Sciences, University of Groningen, Groningen, The Netherlands ,Institute for DiagNostic Accuracy Research B.V., Groningen, The Netherlands
| | - Rozemarijn Vliegenthart
- Department of Radiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Peter van Ooijen
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands ,DASH, Machine Learning Lab, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
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