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Deng X, Li W, Yang Y, Wang S, Zeng N, Xu J, Hassan H, Chen Z, Liu Y, Miao X, Guo Y, Chen R, Kang Y. COPD stage detection: leveraging the auto-metric graph neural network with inspiratory and expiratory chest CT images. Med Biol Eng Comput 2024; 62:1733-1749. [PMID: 38363487 DOI: 10.1007/s11517-024-03016-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 12/30/2023] [Indexed: 02/17/2024]
Abstract
Chronic obstructive pulmonary disease (COPD) is a common lung disease that can lead to restricted airflow and respiratory problems, causing a significant health, economic, and social burden. Detecting the COPD stage can provide a timely warning for prompt intervention in COPD patients. However, existing methods based on inspiratory (IN) and expiratory (EX) chest CT images are not sufficiently accurate and efficient in COPD stage detection. The lung region images are autonomously segmented from IN and EX chest CT images to extract the 1 , 781 × 2 lung radiomics and 13 , 824 × 2 3D CNN features. Furthermore, a strategy for concatenating and selecting features was employed in COPD stage detection based on radiomics and 3D CNN features. Finally, we combine all the radiomics, 3D CNN features, and factor risks (age, gender, and smoking history) to detect the COPD stage based on the Auto-Metric Graph Neural Network (AMGNN). The AMGNN with radiomics and 3D CNN features achieves the best performance at 89.7 % of accuracy, 90.9 % of precision, 89.5 % of F1-score, and 95.8 % of AUC compared to six classic machine learning (ML) classifiers. Our proposed approach demonstrates high accuracy in detecting the stage of COPD using both IN and EX chest CT images. This method can potentially establish an efficient diagnostic tool for patients with COPD. Additionally, we have identified radiomics and 3D CNN as more appropriate biomarkers than Parametric Response Mapping (PRM). Moreover, our findings indicate that expiration yields better results than inspiration in detecting the stage of COPD.
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Affiliation(s)
- Xingguang Deng
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518118, China
| | - Wei Li
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518118, China
| | - Yingjian Yang
- Department of radiology, Shenzhen Lanmage Medical Technology Co., Ltd, No.103, Baguang Service Center, Shenzhen, Guangdong, 518119, People's Republic of China
| | - Shicong Wang
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518118, China
- School of Applied Technology, Shenzhen University, Shenzhen, 518060, China
| | - Nanrong Zeng
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518118, China
- School of Applied Technology, Shenzhen University, Shenzhen, 518060, China
| | - Jiaxuan Xu
- The First Affiliated Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease, Nation Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The National Center for Respiratory Medicine, Guangzhou, 510120, China
| | - Haseeb Hassan
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518118, China
| | - Ziran Chen
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518118, China
| | - Yang Liu
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518118, China
| | - Xiaoqiang Miao
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518118, China
| | - Yingwei Guo
- School of Electrical and Information Engineering, Northeast Petroleum University, Daqing, 163318, China
| | - Rongchang Chen
- Department of Respiratory and Critical Care Medicine, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medical College of Jinan University, Shenzhen People's Hospital, Shenzhen Institute of Respiratory Disease, Shenzhen, 518001, China.
| | - Yan Kang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China.
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, 518118, China.
- Department of radiology, Shenzhen Lanmage Medical Technology Co., Ltd, No.103, Baguang Service Center, Shenzhen, Guangdong, 518119, People's Republic of China.
- Engineering Research Centre of Medical Imaging and Intelligent Analysis, Ministry of Education, Shenyang, 110169, China.
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Wielpütz MO, Mall MA. Therapeutic improvement of CFTR function and reversibility of bronchiectasis in cystic fibrosis. Eur Respir J 2024; 63:2400234. [PMID: 38548272 DOI: 10.1183/13993003.00234-2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 02/14/2024] [Indexed: 04/02/2024]
Affiliation(s)
- Mark O Wielpütz
- Department of Diagnostic and Interventional Radiology, University Hospital Heidelberg, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at University Hospital Heidelberg, Heidelberg, Germany
| | - Marcus A Mall
- Department of Pediatric Respiratory Medicine, Immunology and Critical Care Medicine, Charité-Universitätsmedizin Berlin, Berlin, Germany
- German Center for Lung Research (DZL), associated partner site, Berlin, Germany
- Berlin Institute of Health (BIH) at Charité-Universitätsmedizin Berlin, Berlin, Germany
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Shi Y, Xu Z, Pu S, Xu K, Wang Y, Zhang C. Association Between Serum Klotho and Chronic Obstructive Pulmonary Disease in US Middle-Aged and Older Individuals: A Cross-Sectional Study from NHANES 2013-2016. Int J Chron Obstruct Pulmon Dis 2024; 19:543-553. [PMID: 38435124 PMCID: PMC10906733 DOI: 10.2147/copd.s451859] [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: 11/26/2023] [Accepted: 02/20/2024] [Indexed: 03/05/2024] Open
Abstract
Purpose This study sought to examine the potential association between serum Klotho levels and the prevalence of COPD in the United States. Patients and Methods This study was a cross-sectional analysis involving 4361 adults aged 40-79 years participating in the US National Health and Nutrition Examination Survey (NHANES) conducted between 2013 and 2016. Our investigation utilized multivariate logistic regression and restricted cubic spline (RCS) regression to explore the potential correlation between serum Klotho concentrations and the prevalence of COPD. Additionally, we conducted stratified and interaction analyses to evaluate the consistency and potential modifiers of this relationship. Results In this study encompassing 4631 patients (with an average age of 57.6 years, 47.5% of whom were male), 445 individuals (10.2%) were identified as having COPD. In the fully adjusted model, ln-transformed serum Klotho was negatively associated with COPD (OR = 0.71; 95% CI: 0.51-0.99; p = 0.043). Meanwhile, compared with quartile 1, serum Klotho levels in quartiles 2-4 yielded odds ratios (ORs) (95% CI) for COPD were 0.84 (0.63~1.11), 0.76 (0.56~1.02), 0.84 (0.62~1.13), respectively. A negative relationship was observed between the ln-transformed serum Klotho and occurrence of COPD (nonlinear: p = 0.140). the association between ln-transformed serum Klotho and COPD were stable in stratified analyses. Conclusion Serum Klotho was negatively associated with the incidence of COPD, when ln-transformed Klotho concentration increased by 1 unit, the risk of COPD was 29% lower.
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Affiliation(s)
- Yushan Shi
- Department of Laboratory Medicine, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, 25000, People’s Republic of China
| | - Zhangmeng Xu
- School of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, 610075, People’s Republic of China
| | - Shuangshuang Pu
- Department of Laboratory Medicine, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, 25000, People’s Republic of China
| | - Kanghong Xu
- Department of Laboratory Medicine, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, 25000, People’s Republic of China
| | - Yanan Wang
- Department of Laboratory Medicine, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, 25000, People’s Republic of China
| | - Chunlai Zhang
- Department of Laboratory Medicine, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, 25000, People’s Republic of China
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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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Almeida SD, Norajitra T, Lüth CT, Wald T, Weru V, Nolden M, Jäger PF, von Stackelberg O, Heußel CP, Weinheimer O, Biederer J, Kauczor HU, Maier-Hein K. Prediction of disease severity in COPD: a deep learning approach for anomaly-based quantitative assessment of chest CT. Eur Radiol 2023:10.1007/s00330-023-10540-3. [PMID: 38150075 DOI: 10.1007/s00330-023-10540-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 11/13/2023] [Accepted: 12/11/2023] [Indexed: 12/28/2023]
Abstract
OBJECTIVES To quantify regional manifestations related to COPD as anomalies from a modeled distribution of normal-appearing lung on chest CT using a deep learning (DL) approach, and to assess its potential to predict disease severity. MATERIALS AND METHODS Paired inspiratory/expiratory CT and clinical data from COPDGene and COSYCONET cohort studies were included. COPDGene data served as training/validation/test data sets (N = 3144/786/1310) and COSYCONET as external test set (N = 446). To differentiate low-risk (healthy/minimal disease, [GOLD 0]) from COPD patients (GOLD 1-4), the self-supervised DL model learned semantic information from 50 × 50 × 50 voxel samples from segmented intact lungs. An anomaly detection approach was trained to quantify lung abnormalities related to COPD, as regional deviations. Four supervised DL models were run for comparison. The clinical and radiological predictive power of the proposed anomaly score was assessed using linear mixed effects models (LMM). RESULTS The proposed approach achieved an area under the curve of 84.3 ± 0.3 (p < 0.001) for COPDGene and 76.3 ± 0.6 (p < 0.001) for COSYCONET, outperforming supervised models even when including only inspiratory CT. Anomaly scores significantly improved fitting of LMM for predicting lung function, health status, and quantitative CT features (emphysema/air trapping; p < 0.001). Higher anomaly scores were significantly associated with exacerbations for both cohorts (p < 0.001) and greater dyspnea scores for COPDGene (p < 0.001). CONCLUSION Quantifying heterogeneous COPD manifestations as anomaly offers advantages over supervised methods and was found to be predictive for lung function impairment and morphology deterioration. CLINICAL RELEVANCE STATEMENT Using deep learning, lung manifestations of COPD can be identified as deviations from normal-appearing chest CT and attributed an anomaly score which is consistent with decreased pulmonary function, emphysema, and air trapping. KEY POINTS • A self-supervised DL anomaly detection method discriminated low-risk individuals and COPD subjects, outperforming classic DL methods on two datasets (COPDGene AUC = 84.3%, COSYCONET AUC = 76.3%). • Our contrastive task exhibits robust performance even without the inclusion of expiratory images, while voxel-based methods demonstrate significant performance enhancement when incorporating expiratory images, in the COPDGene dataset. • Anomaly scores improved the fitting of linear mixed effects models in predicting clinical parameters and imaging alterations (p < 0.001) and were directly associated with clinical outcomes (p < 0.001).
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Affiliation(s)
- Silvia D Almeida
- Division of Medical Image Computing, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany.
- Translational Lung Research Center Heidelberg (TLRC), Member of the German Lung Research Center (DZL), Heidelberg, Germany.
- Medical Faculty, Heidelberg University, Heidelberg, Germany.
- National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and Heidelberg University Medical Center, Heidelberg, Germany.
| | - Tobias Norajitra
- Division of Medical Image Computing, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), Member of the German Lung Research Center (DZL), Heidelberg, Germany
| | - Carsten T Lüth
- Interactive Machine Learning Group (IML), German Cancer Research Center (DKFZ), Heidelberg, Germany
- Helmholtz Imaging, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Tassilo Wald
- Division of Medical Image Computing, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany
- Helmholtz Imaging, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Vivienn Weru
- Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Marco Nolden
- Division of Medical Image Computing, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany
- Pattern Analysis and Learning Group, Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Paul F Jäger
- Interactive Machine Learning Group (IML), German Cancer Research Center (DKFZ), Heidelberg, Germany
- Helmholtz Imaging, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Oyunbileg von Stackelberg
- Pattern Analysis and Learning Group, Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Claus Peter Heußel
- Translational Lung Research Center Heidelberg (TLRC), Member of the German Lung Research Center (DZL), Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany
- Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at University Hospital, Heidelberg, Germany
| | - Oliver Weinheimer
- Translational Lung Research Center Heidelberg (TLRC), Member of the German Lung Research Center (DZL), Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Jürgen Biederer
- Translational Lung Research Center Heidelberg (TLRC), Member of the German Lung Research Center (DZL), Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany
- Faculty of Medicine, University of Latvia, Raina Bulvaris 19, Riga, LV-1586, Latvia
- Faculty of Medicine, Christian-Albrechts-Universität zu Kiel, D-24098, Kiel, Germany
| | - Hans-Ulrich Kauczor
- Translational Lung Research Center Heidelberg (TLRC), Member of the German Lung Research Center (DZL), Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Klaus Maier-Hein
- Division of Medical Image Computing, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany.
- Translational Lung Research Center Heidelberg (TLRC), Member of the German Lung Research Center (DZL), Heidelberg, Germany.
- National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and Heidelberg University Medical Center, Heidelberg, Germany.
- Helmholtz Imaging, German Cancer Research Center (DKFZ), Heidelberg, Germany.
- Pattern Analysis and Learning Group, Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany.
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Zhou X, Pu Y, Zhang D, Guan Y, Lu Y, Zhang W, Fu C, Fang Q, Zhang H, Liu S, Fan L. Development of machine learning model to predict pulmonary function with low-dose CT-derived parameter response mapping in a community-based chest screening cohort. J Appl Clin Med Phys 2023; 24:e14171. [PMID: 37782241 PMCID: PMC10647993 DOI: 10.1002/acm2.14171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 09/14/2023] [Accepted: 09/20/2023] [Indexed: 10/03/2023] Open
Abstract
PURPOSE To construct and evaluate the performance of a machine learning-based low dose computed tomography (LDCT)-derived parametric response mapping (PRM) model for predicting pulmonary function test (PFT) results. MATERIALS AND METHODS A total of 615 subjects from a community-based screening population (40-74 years old) with PFT parameters, including the ratio of the first second forced expiratory volume to forced vital capacity (FEV1/FVC), the percentage of forced expiratory volume in the one second predicted (FEV1%), and registered inspiration-to-expiration chest CT scanning were enrolled retrospectively. Subjects were classified into a normal, high risk, and COPD group based on PFT. Data of 72 PRM-derived quantitative parameters were collected, including volume and volume percentage of emphysema, functional-small airways disease, and normal lung tissue. A machine-learning with random forest regression model and a multilayer perceptron (MLP) model were constructed and tested on PFT prediction, which was followed by evaluation of classification performance based on the PFT predictions. RESULTS The machine-learning model based on PRM parameters showed better performance for predicting PFT than MLP, with a coefficient of determination (R2 ) of 0.749 and 0.792 for FEV1/FVC and FEV1%, respectively. The Mean Squared Errors (MSE) for FEV1/FVC and FEV1% are 0.0030 and 0.0097 for the random forest model, respectively. The Root Mean Squared Errors (RMSE) for FEV1/FVC and FEV1% are 0.055 and 0.098, respectively. The sensitivity, specificity, and accuracy for differentiating between the normal group and high-risk group were 34/40 (85%), 65/72 (90%), and 99/112 (88%), respectively. For differentiating between the non-COPD group and COPD group, the sensitivity, specificity, and accuracy were 8/9 (89%), 112/112 (100%), 120/121 (99%), respectively. CONCLUSIONS The machine learning-based random forest model predicts PFT results in a community screening population based on PRM, and it identifies high risk COPD from normal populations with high sensitivity and reliably predicts of high-risk COPD.
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Affiliation(s)
- Xiuxiu Zhou
- Department of RadiologySecond Affiliated Hospital of PLA Naval Medical UniversityShanghaiChina
| | - Yu Pu
- Department of RadiologySecond Affiliated Hospital of PLA Naval Medical UniversityShanghaiChina
| | - Di Zhang
- Department of RadiologySecond Affiliated Hospital of PLA Naval Medical UniversityShanghaiChina
| | - Yu Guan
- Department of RadiologySecond Affiliated Hospital of PLA Naval Medical UniversityShanghaiChina
| | - Yang Lu
- Shanghai Aitrox Technology Corporation LimitedShanghaiChina
| | - Weidong Zhang
- Shanghai Aitrox Technology Corporation LimitedShanghaiChina
| | - Chi‐Cheng Fu
- Shanghai Aitrox Technology Corporation LimitedShanghaiChina
| | - Qu Fang
- Shanghai Aitrox Technology Corporation LimitedShanghaiChina
| | - Hanxiao Zhang
- Department of RadiologySecond Affiliated Hospital of PLA Naval Medical UniversityShanghaiChina
| | - Shiyuan Liu
- Department of RadiologySecond Affiliated Hospital of PLA Naval Medical UniversityShanghaiChina
| | - Li Fan
- Department of RadiologySecond Affiliated Hospital of PLA Naval Medical UniversityShanghaiChina
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Dettmer S, Weinheimer O, Sauer-Heilborn A, Lammers O, Wielpütz MO, Fuge J, Welte T, Wacker F, Ringshausen FC. Qualitative and quantitative evaluation of computed tomography changes in adults with cystic fibrosis treated with elexacaftor-tezacaftor-ivacaftor: a retrospective observational study. Front Pharmacol 2023; 14:1245885. [PMID: 37808186 PMCID: PMC10552920 DOI: 10.3389/fphar.2023.1245885] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 09/11/2023] [Indexed: 10/10/2023] Open
Abstract
Introduction: The availability of highly effective triple cystic fibrosis transmembrane conductance regulator (CFTR) modulator combination therapy with elexacaftor-tezacaftor-ivacaftor (ETI) has improved pulmonary outcomes and quality of life of people with cystic fibrosis (pwCF). The aim of this study was to assess computed tomography (CT) changes under ETI visually with the Brody score and quantitatively with dedicated software, and to correlate CT measures with parameters of clinical response. Methods: Twenty two adult pwCF with two consecutive CT scans before and after ETI treatment initiation were retrospectively included. CT was assessed visually employing the Brody score and quantitatively by YACTA, a well-evaluated scientific software computing airway dimensions and lung parenchyma with wall percentage (WP), wall thickness (WT), lumen area (LA), bronchiectasis index (BI), lung volume and mean lung density (MLD) as parameters. Changes in CT metrics were evaluated and the visual and quantitative parameters were correlated with each other and with clinical changes in sweat chloride concentration, spirometry [percent predicted of forced expiratory volume in one second (ppFEV1)] and body mass index (BMI). Results: The mean (SD) Brody score improved with ETI [55 (12) vs. 38 (15); p < 0.001], incl. sub-scores for mucus plugging, peribronchial thickening, and parenchymal changes (all p < 0.001), but not for bronchiectasis (p = 0.281). Quantitatve WP (p < 0.001) and WT (p = 0.004) were reduced, conversely LA increased (p = 0.003), and BI improved (p = 0.012). Lung volume increased (p < 0.001), and MLD decreased (p < 0.001) through a reduction of ground glass opacity areas (p < 0.001). Changes of the Brody score correlated with those of quantitative parameters, exemplarily WT with the sub-score for mucus plugging (r = 0.730, p < 0.001) and peribronchial thickening (r = 0.552, p = 0.008). Changes of CT parameters correlated with those of clinical response parameters, in particular ppFEV1 with the Brody score (r = -0.606, p = 0.003) and with WT (r = -0.538, p = 0.010). Discussion: Morphological treatment response to ETI can be assessed using the Brody score as well as quantitative CT parameters. Changes in CT correlated with clinical improvements. The quantitative analysis with YACTA proved to be an objective, reproducible and simple method for monitoring lung disease, particularly with regard to future interventional clinical trials.
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Affiliation(s)
- Sabine Dettmer
- Institute of Diagnostic and Interventional Radiology, Hannover Medical School, Hannover, Germany
- Biomedical Research in Endstage and Obstructive Lung Disease Hannover (BREATH), Member of the German Center for Lung Research (DZL), Hannover, Germany
| | - Oliver Weinheimer
- Department of Diagnostic and Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), University of Heidelberg, Heidelberg, Germany
| | - Annette Sauer-Heilborn
- Department of Respiratory Medicine and Infectious Diseases, Hannover Medical School, Hannover, Germany
| | - Oliver Lammers
- Institute of Diagnostic and Interventional Radiology, Hannover Medical School, Hannover, Germany
| | - Mark O. Wielpütz
- Department of Diagnostic and Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), University of Heidelberg, Heidelberg, Germany
| | - Jan Fuge
- Biomedical Research in Endstage and Obstructive Lung Disease Hannover (BREATH), Member of the German Center for Lung Research (DZL), Hannover, Germany
- Department of Respiratory Medicine and Infectious Diseases, Hannover Medical School, Hannover, Germany
| | - Tobias Welte
- Biomedical Research in Endstage and Obstructive Lung Disease Hannover (BREATH), Member of the German Center for Lung Research (DZL), Hannover, Germany
- Department of Respiratory Medicine and Infectious Diseases, Hannover Medical School, Hannover, Germany
| | - Frank Wacker
- Institute of Diagnostic and Interventional Radiology, Hannover Medical School, Hannover, Germany
- Biomedical Research in Endstage and Obstructive Lung Disease Hannover (BREATH), Member of the German Center for Lung Research (DZL), Hannover, Germany
| | - Felix C. Ringshausen
- Biomedical Research in Endstage and Obstructive Lung Disease Hannover (BREATH), Member of the German Center for Lung Research (DZL), Hannover, Germany
- Department of Respiratory Medicine and Infectious Diseases, Hannover Medical School, Hannover, Germany
- European Reference Network on Rare and Complex Respiratory Diseases (ERN-LUNG), Frankfurt, Germany
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Bodenberger AL, Konietzke P, Weinheimer O, Wagner WL, Stiller W, Weber TF, Heussel CP, Kauczor HU, Wielpütz MO. Quantification of airway wall contrast enhancement on virtual monoenergetic images from spectral computed tomography. Eur Radiol 2023; 33:5557-5567. [PMID: 36892642 PMCID: PMC10326154 DOI: 10.1007/s00330-023-09514-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 12/31/2022] [Accepted: 02/02/2023] [Indexed: 03/10/2023]
Abstract
OBJECTIVES Quantitative computed tomography (CT) plays an increasingly important role in phenotyping airway diseases. Lung parenchyma and airway inflammation could be quantified by contrast enhancement at CT, but its investigation by multiphasic examinations is limited. We aimed to quantify lung parenchyma and airway wall attenuation in a single contrast-enhanced spectral detector CT acquisition. METHODS For this cross-sectional retrospective study, 234 lung-healthy patients who underwent spectral CT in four different contrast phases (non-enhanced, pulmonary arterial, systemic arterial, and venous phase) were recruited. Virtual monoenergetic images were reconstructed from 40-160 keV, on which attenuations of segmented lung parenchyma and airway walls combined for 5th-10th subsegmental generations were assessed in Hounsfield Units (HU) by an in-house software. The spectral attenuation curve slope between 40 and 100 keV (λHU) was calculated. RESULTS Mean lung density was higher at 40 keV compared to that at 100 keV in all groups (p < 0.001). λHU of lung attenuation was significantly higher in the systemic (1.7 HU/keV) and pulmonary arterial phase (1.3 HU/keV) compared to that in the venous phase (0.5 HU/keV) and non-enhanced (0.2 HU/keV) spectral CT (p < 0.001). Wall thickness and wall attenuation were higher at 40 keV compared to those at 100 keV for the pulmonary and systemic arterial phase (p ≤ 0.001). λHU for wall attenuation was significantly higher in the pulmonary arterial (1.8 HU/keV) and systemic arterial (2.0 HU/keV) compared to that in the venous (0.7 HU/keV) and non-enhanced (0.3 HU/keV) phase (p ≤ 0.002). CONCLUSIONS Spectral CT may quantify lung parenchyma and airway wall enhancement with a single contrast phase acquisition, and may separate arterial and venous enhancement. Further studies are warranted to analyze spectral CT for inflammatory airway diseases. KEY POINTS • Spectral CT may quantify lung parenchyma and airway wall enhancement with a single contrast phase acquisition. • Spectral CT may separate arterial and venous enhancement of lung parenchyma and airway wall. • The contrast enhancement can be quantified by calculating the spectral attenuation curve slope from virtual monoenergetic images.
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Affiliation(s)
- Arndt Lukas Bodenberger
- Department of Diagnostic and Interventional Radiology, Heidelberg University Hospital, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120, Heidelberg, Germany
| | - Philip Konietzke
- Department of Diagnostic and Interventional Radiology, Heidelberg University Hospital, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120, Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology With Nuclear Medicine, Thoraxklinik at University of Heidelberg, Röntgenstraße 1, 69126, Heidelberg, Germany
| | - Oliver Weinheimer
- Department of Diagnostic and Interventional Radiology, Heidelberg University Hospital, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120, Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology With Nuclear Medicine, Thoraxklinik at University of Heidelberg, Röntgenstraße 1, 69126, Heidelberg, Germany
| | - Willi Linus Wagner
- Department of Diagnostic and Interventional Radiology, Heidelberg University Hospital, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120, Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology With Nuclear Medicine, Thoraxklinik at University of Heidelberg, Röntgenstraße 1, 69126, Heidelberg, Germany
| | - Wolfram Stiller
- Department of Diagnostic and Interventional Radiology, Heidelberg University Hospital, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120, Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology With Nuclear Medicine, Thoraxklinik at University of Heidelberg, Röntgenstraße 1, 69126, Heidelberg, Germany
| | - Tim Frederik Weber
- Department of Diagnostic and Interventional Radiology, Heidelberg University Hospital, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology With Nuclear Medicine, Thoraxklinik at University of Heidelberg, Röntgenstraße 1, 69126, Heidelberg, Germany
| | - Claus Peter Heussel
- Department of Diagnostic and Interventional Radiology, Heidelberg University Hospital, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120, Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology With Nuclear Medicine, Thoraxklinik at University of Heidelberg, Röntgenstraße 1, 69126, Heidelberg, Germany
| | - Hans-Ulrich Kauczor
- Department of Diagnostic and Interventional Radiology, Heidelberg University Hospital, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120, Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology With Nuclear Medicine, Thoraxklinik at University of Heidelberg, Röntgenstraße 1, 69126, Heidelberg, Germany
| | - Mark Oliver Wielpütz
- Department of Diagnostic and Interventional Radiology, Heidelberg University Hospital, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany.
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120, Heidelberg, Germany.
- Department of Diagnostic and Interventional Radiology With Nuclear Medicine, Thoraxklinik at University of Heidelberg, Röntgenstraße 1, 69126, Heidelberg, Germany.
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Konietzke P, Brunner C, Konietzke M, Wagner WL, Weinheimer O, Heußel CP, Herth FJF, Trudzinski F, Kauczor HU, Wielpütz MO. GOLD stage-specific phenotyping of emphysema and airway disease using quantitative computed tomography. Front Med (Lausanne) 2023; 10:1184784. [PMID: 37534319 PMCID: PMC10393128 DOI: 10.3389/fmed.2023.1184784] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Accepted: 06/22/2023] [Indexed: 08/04/2023] Open
Abstract
Background In chronic obstructive pulmonary disease (COPD) abnormal lung function is related to emphysema and airway obstruction, but their relative contribution in each GOLD-stage is not fully understood. In this study, we used quantitative computed tomography (QCT) parameters for phenotyping of emphysema and airway abnormalities, and to investigate the relative contribution of QCT emphysema and airway parameters to airflow limitation specifically in each GOLD stage. Methods Non-contrast computed tomography (CT) of 492 patients with COPD former GOLD 0 COPD and COPD stages GOLD 1-4 were evaluated using fully automated software for quantitative CT. Total lung volume (TLV), emphysema index (EI), mean lung density (MLD), and airway wall thickness (WT), total diameter (TD), lumen area (LA), and wall percentage (WP) were calculated for the entire lung, as well as for all lung lobes separately. Results from the 3rd-8th airway generation were aggregated (WT3-8, TD3-8, LA3-8, WP3-8). All subjects underwent whole-body plethysmography (FEV1%pred, VC, RV, TLC). Results EI was higher with increasing GOLD stages with 1.0 ± 1.8% in GOLD 0, 4.5 ± 9.9% in GOLD 1, 19.4 ± 15.8% in GOLD 2, 32.7 ± 13.4% in GOLD 3 and 41.4 ± 10.0% in GOLD 4 subjects (p < 0.001). WP3-8 showed no essential differences between GOLD 0 and GOLD 1, tended to be higher in GOLD 2 with 52.4 ± 7.2%, and was lower in GOLD 4 with 50.6 ± 5.9% (p = 0.010 - p = 0.960). In the upper lobes WP3-8 showed no significant differences between the GOLD stages (p = 0.824), while in the lower lobes the lowest WP3-8 was found in GOLD 0/1 with 49.9 ± 6.5%, while higher values were detected in GOLD 2 with 51.9 ± 6.4% and in GOLD 3/4 with 51.0 ± 6.0% (p < 0.05). In a multilinear regression analysis, the dependent variable FEV1%pred can be predicted by a combination of both the independent variables EI (p < 0.001) and WP3-8 (p < 0.001). Conclusion QCT parameters showed a significant increase of emphysema from GOLD 0-4 COPD. Airway changes showed a different spatial pattern with higher values of relative wall thickness in the lower lobes until GOLD 2 and subsequent lower values in GOLD3/4, whereas there were no significant differences in the upper lobes. Both, EI and WP5-8 are independently correlated with lung function decline.
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Affiliation(s)
- Philip Konietzke
- Department of Diagnostic and Interventional Radiology, University Hospital of Heidelberg, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), University of Heidelberg, Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at University of Heidelberg, Heidelberg, Germany
| | - Christian Brunner
- Department of Diagnostic and Interventional Radiology, University Hospital of Heidelberg, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), University of Heidelberg, Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at University of Heidelberg, Heidelberg, Germany
| | - Marilisa Konietzke
- Department of Diagnostic and Interventional Radiology, University Hospital of Heidelberg, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), University of Heidelberg, Heidelberg, Germany
| | - Willi Linus Wagner
- Department of Diagnostic and Interventional Radiology, University Hospital of Heidelberg, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), University of Heidelberg, Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at University of Heidelberg, Heidelberg, Germany
| | - Oliver Weinheimer
- Department of Diagnostic and Interventional Radiology, University Hospital of Heidelberg, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), University of Heidelberg, Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at University of Heidelberg, Heidelberg, Germany
| | - Claus Peter Heußel
- Department of Diagnostic and Interventional Radiology, University Hospital of Heidelberg, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), University of Heidelberg, Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at University of Heidelberg, Heidelberg, Germany
| | - Felix J. F. Herth
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), University of Heidelberg, Heidelberg, Germany
- Department of Pulmonology, Thoraxklinik at University of Heidelberg, Heidelberg, Germany
| | - Franziska Trudzinski
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), University of Heidelberg, Heidelberg, Germany
- Department of Pulmonology, Thoraxklinik at University of Heidelberg, Heidelberg, Germany
| | - Hans-Ulrich Kauczor
- Department of Diagnostic and Interventional Radiology, University Hospital of Heidelberg, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), University of Heidelberg, Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at University of Heidelberg, Heidelberg, Germany
| | - Mark Oliver Wielpütz
- Department of Diagnostic and Interventional Radiology, University Hospital of Heidelberg, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), University of Heidelberg, Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at University of Heidelberg, Heidelberg, Germany
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Wang R, Huang C, Yang W, Wang C, Wang P, Guo L, Cao J, Huang L, Song H, Zhang C, Zhang Y, Shi G. Respiratory microbiota and radiomics features in the stable COPD patients. Respir Res 2023; 24:131. [PMID: 37173744 PMCID: PMC10176953 DOI: 10.1186/s12931-023-02434-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 04/25/2023] [Indexed: 05/15/2023] Open
Abstract
BACKGROUNDS The respiratory microbiota and radiomics correlate with the disease severity and prognosis of chronic obstructive pulmonary disease (COPD). We aim to characterize the respiratory microbiota and radiomics features of COPD patients and explore the relationship between them. METHODS Sputa from stable COPD patients were collected for bacterial 16 S rRNA gene sequencing and fungal Internal Transcribed Spacer (ITS) sequencing. Chest computed tomography (CT) and 3D-CT analysis were conducted for radiomics information, including the percentages of low attenuation area below - 950 Hounsfield Units (LAA%), wall thickness (WT), and intraluminal area (Ai). WT and Ai were adjusted by body surface area (BSA) to WT/[Formula: see text] and Ai/BSA, respectively. Some key pulmonary function indicators were collected, which included forced expiratory volume in one second (FEV1), forced vital capacity (FVC), diffusion lung carbon monoxide (DLco). Differences and correlations of microbiomics with radiomics and clinical indicators between different patient subgroups were assessed. RESULTS Two bacterial clusters dominated by Streptococcus and Rothia were identified. Chao and Shannon indices were higher in the Streptococcus cluster than that in the Rothia cluster. Principal Co-ordinates Analysis (PCoA) indicated significant differences between their community structures. Higher relative abundance of Actinobacteria was detected in the Rothia cluster. Some genera were more common in the Streptococcus cluster, mainly including Leptotrichia, Oribacterium, Peptostreptococcus. Peptostreptococcus was positively correlated with DLco per unit of alveolar volume as a percentage of predicted value (DLco/VA%pred). The patients with past-year exacerbations were more in the Streptococcus cluster. Fungal analysis revealed two clusters dominated by Aspergillus and Candida. Chao and Shannon indices of the Aspergillus cluster were higher than that in the Candida cluster. PCoA showed distinct community compositions between the two clusters. Greater abundance of Cladosporium and Penicillium was found in the Aspergillus cluster. The patients of the Candida cluster had upper FEV1 and FEV1/FVC levels. In radiomics, the patients of the Rothia cluster had higher LAA% and WT/[Formula: see text] than those of the Streptococcus cluster. Haemophilus, Neisseria and Cutaneotrichosporon positively correlated with Ai/BSA, but Cladosporium negatively correlated with Ai/BSA. CONCLUSIONS Among respiratory microbiota in stable COPD patients, Streptococcus dominance was associated with an increased risk of exacerbation, and Rothia dominance was relevant to worse emphysema and airway lesions. Peptostreptococcus, Haemophilus, Neisseria and Cutaneotrichosporon probably affected COPD progression and potentially could be disease prediction biomarkers.
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Affiliation(s)
- Rong Wang
- Department of Pulmonary and Critical Care Medicine, the Affiliated Hospital of Kunming University of Science and Technology, the First People's Hospital of Yunnan Province, Kunming, 650032, People's Republic of China
- Medical School, Kunming University of Science and Technology, Kunming, 650500, People's Republic of China
- Department of Pulmonary and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine. Institute of Respiratory Diseases, Shanghai Jiao Tong University School of Medicine. Shanghai Key Laboratory of Emergency Prevention, Diagnosis and Treatment of Respiratory Infectious Diseases, Shanghai, 200025, People's Republic of China
| | - Chunrong Huang
- Department of Pulmonary and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine. Institute of Respiratory Diseases, Shanghai Jiao Tong University School of Medicine. Shanghai Key Laboratory of Emergency Prevention, Diagnosis and Treatment of Respiratory Infectious Diseases, Shanghai, 200025, People's Republic of China
| | - Wenjie Yang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, People's Republic of China
| | - Cui Wang
- Department of Pulmonary and Critical Care Medicine, the Third People's Hospital of Kunshan, Suzhou, 215300, People's Republic of China
| | - Ping Wang
- Department of Pulmonary and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine. Institute of Respiratory Diseases, Shanghai Jiao Tong University School of Medicine. Shanghai Key Laboratory of Emergency Prevention, Diagnosis and Treatment of Respiratory Infectious Diseases, Shanghai, 200025, People's Republic of China
| | - Leixin Guo
- Department of Pulmonary and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine. Institute of Respiratory Diseases, Shanghai Jiao Tong University School of Medicine. Shanghai Key Laboratory of Emergency Prevention, Diagnosis and Treatment of Respiratory Infectious Diseases, Shanghai, 200025, People's Republic of China
| | - Jin Cao
- Department of Pulmonary and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine. Institute of Respiratory Diseases, Shanghai Jiao Tong University School of Medicine. Shanghai Key Laboratory of Emergency Prevention, Diagnosis and Treatment of Respiratory Infectious Diseases, Shanghai, 200025, People's Republic of China
| | - Lin Huang
- Department of Pulmonary and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine. Institute of Respiratory Diseases, Shanghai Jiao Tong University School of Medicine. Shanghai Key Laboratory of Emergency Prevention, Diagnosis and Treatment of Respiratory Infectious Diseases, Shanghai, 200025, People's Republic of China
| | - Hejie Song
- Department of Pulmonary and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine. Institute of Respiratory Diseases, Shanghai Jiao Tong University School of Medicine. Shanghai Key Laboratory of Emergency Prevention, Diagnosis and Treatment of Respiratory Infectious Diseases, Shanghai, 200025, People's Republic of China
| | - Chenhong Zhang
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, People's Republic of China.
| | - Yunhui Zhang
- Department of Pulmonary and Critical Care Medicine, the Affiliated Hospital of Kunming University of Science and Technology, the First People's Hospital of Yunnan Province, Kunming, 650032, People's Republic of China.
| | - Guochao Shi
- Department of Pulmonary and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine. Institute of Respiratory Diseases, Shanghai Jiao Tong University School of Medicine. Shanghai Key Laboratory of Emergency Prevention, Diagnosis and Treatment of Respiratory Infectious Diseases, Shanghai, 200025, People's Republic of China.
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Wucherpfennig L, Triphan SM, Weinheimer O, Eichinger M, Wege S, Eberhardt R, Puderbach MU, Kauczor HU, Heussel CP, Heussel G, Wielpütz MO. Reproducibility of pulmonary magnetic resonance angiography in adults with muco-obstructive pulmonary disease. Acta Radiol 2023; 64:1038-1046. [PMID: 35876445 DOI: 10.1177/02841851221111486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Recent studies support magnetic resonance angiography (MRA) as a diagnostic tool for pulmonary arterial disease. PURPOSE To determine MRA image quality and reproducibility, and the dependence of MRA image quality and reproducibility on disease severity in patients with chronic obstructive pulmonary disease (COPD) and cystic fibrosis (CF). MATERIAL AND METHODS Twenty patients with COPD (mean age 66.5 ± 8.9 years; FEV1% = 42.0 ± 13.3%) and 15 with CF (mean age 29.3 ± 9.3 years; FEV1% = 66.6 ± 15.8%) underwent morpho-functional chest magnetic resonance imaging (MRI) including time-resolved MRA twice one month apart (MRI1, MRI2), and COPD patients underwent non-contrast computed tomography (CT). Image quality was assessed visually using standardized subjective 5-point scales. Contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) were measured by regions of interest. Disease severity was determined by spirometry, a well-evaluated chest MRI score, and by computational CT emphysema index (EI) for COPD. RESULTS Subjective image quality was diagnostic for all MRA at MRI1 and MRI2 (mean score = 4.7 ± 0.6). CNR and SNR were 4 43.8 ± 8.7 and 50.5 ± 8.7, respectively. Neither image quality score nor CNR or SNR correlated with FEV1% or chest MRI score for COPD and CF (r = 0.239-0.248). CNR and SNR did not change from MRI1 to MRI2 (P = 0.434-0.995). Further, insignificant differences in CNR and SNR between MRA at MRI1 and MRI2 did not correlate with FEV1% nor chest MRI score in COPD and CF (r = -0.238-0.183), nor with EI in COPD (r = 0.100-0.111). CONCLUSION MRA achieved diagnostic quality in COPD and CF patients and was highly reproducible irrespective of disease severity. This supports MRA as a robust alternative to CT in patients with underlying muco-obstructive lung disease.
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Affiliation(s)
- Lena Wucherpfennig
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, 27178University Hospital Heidelberg, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Lung Research Center (DZL), Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik, 27178University Hospital Heidelberg, Heidelberg, Germany
| | - Simon Mf Triphan
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, 27178University Hospital Heidelberg, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Lung Research Center (DZL), Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik, 27178University Hospital Heidelberg, Heidelberg, Germany
| | - Oliver Weinheimer
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, 27178University Hospital Heidelberg, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Lung Research Center (DZL), Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik, 27178University Hospital Heidelberg, Heidelberg, Germany
| | - Monika Eichinger
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, 27178University Hospital Heidelberg, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Lung Research Center (DZL), Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik, 27178University Hospital Heidelberg, Heidelberg, Germany
| | - Sabine Wege
- Department of Pulmonology and Respiratory Medicine, Thoraxklinik, 27178University Hospital Heidelberg, Heidelberg, Germany
| | - Ralf Eberhardt
- Department of Pulmonology and Respiratory Medicine, Thoraxklinik, 27178University Hospital Heidelberg, Heidelberg, Germany
- Department of Pulmonology and Internal intensive care, Asklepios Clinic Barmbek, Hamburg, Germany
| | - Michael U Puderbach
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, 27178University Hospital Heidelberg, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Lung Research Center (DZL), Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik, 27178University Hospital Heidelberg, Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology, Hufeland Hospital, Bad Langensalza, Germany
| | - Hans-Ulrich Kauczor
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, 27178University Hospital Heidelberg, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Lung Research Center (DZL), Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik, 27178University Hospital Heidelberg, Heidelberg, Germany
| | - Claus P Heussel
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, 27178University Hospital Heidelberg, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Lung Research Center (DZL), Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik, 27178University Hospital Heidelberg, Heidelberg, Germany
| | - Gudula Heussel
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik, 27178University Hospital Heidelberg, Heidelberg, Germany
| | - Mark O Wielpütz
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, 27178University Hospital Heidelberg, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Lung Research Center (DZL), Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik, 27178University Hospital Heidelberg, Heidelberg, Germany
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Quantitative CT analysis of lung parenchyma to improve malignancy risk estimation in incidental pulmonary nodules. Eur Radiol 2022; 33:3908-3917. [PMID: 36538071 PMCID: PMC10181968 DOI: 10.1007/s00330-022-09334-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 11/18/2022] [Accepted: 11/29/2022] [Indexed: 12/24/2022]
Abstract
Abstract
Objectives
To assess the value of quantitative computed tomography (QCT) of the whole lung and nodule-bearing lobe regarding pulmonary nodule malignancy risk estimation.
Methods
A total of 251 subjects (median [IQR] age, 65 (57–73) years; 37% females) with pulmonary nodules on non-enhanced thin-section CT were retrospectively included. Twenty percent of the nodules were malignant, the remainder benign either histologically or at least 1-year follow-up. CT scans were subjected to in-house software, computing parameters such as mean lung density (MLD) or peripheral emphysema index (pEI). QCT variable selection was performed using logistic regression; selected variables were integrated into the Mayo Clinic and the parsimonious Brock Model.
Results
Whole-lung analysis revealed differences between benign vs. malignant nodule groups in several parameters, e.g. the MLD (−766 vs. −790 HU) or the pEI (40.1 vs. 44.7 %). The proposed QCT model had an area-under-the-curve (AUC) of 0.69 (95%-CI, 0.62−0.76) based on all available data. After integrating MLD and pEI into the Mayo Clinic and Brock Model, the AUC of both clinical models improved (AUC, 0.91 to 0.93 and 0.88 to 0.91, respectively). The lobe-specific analysis revealed that the nodule-bearing lobes had less emphysema than the rest of the lung regarding benign (EI, 0.5 vs. 0.7 %; p < 0.001) and malignant nodules (EI, 1.2 vs. 1.7 %; p = 0.001).
Conclusions
Nodules in subjects with higher whole-lung metrics of emphysema and less fibrosis are more likely to be malignant; hereby the nodule-bearing lobes have less emphysema. QCT variables could improve the risk assessment of incidental pulmonary nodules.
Key Points
• Nodules in subjects with higher whole-lung metrics of emphysema and less fibrosis are more likely to be malignant.
• The nodule-bearing lobes have less emphysema compared to the rest of the lung.
• QCT variables could improve the risk assessment of incidental pulmonary nodules.
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Kahnert K, Jörres RA, Jobst B, Wielpütz MO, Seefelder A, Hackl CM, Trudzinski FC, Watz H, Bals R, Behr J, Rabe KF, Vogelmeier CF, Alter P, Welte T, Herth F, Kauczor H, Biederer J. Association of coronary artery calcification with clinical and physiological characteristics in patients with COPD: Results from COSYCONET. Respir Med 2022; 204:107014. [DOI: 10.1016/j.rmed.2022.107014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Revised: 09/28/2022] [Accepted: 10/13/2022] [Indexed: 10/31/2022]
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Palm V, Norajitra T, von Stackelberg O, Heussel CP, Skornitzke S, Weinheimer O, Kopytova T, Klein A, Almeida SD, Baumgartner M, Bounias D, Scherer J, Kades K, Gao H, Jäger P, Nolden M, Tong E, Eckl K, Nattenmüller J, Nonnenmacher T, Naas O, Reuter J, Bischoff A, Kroschke J, Rengier F, Schlamp K, Debic M, Kauczor HU, Maier-Hein K, Wielpütz MO. AI-Supported Comprehensive Detection and Quantification of Biomarkers of Subclinical Widespread Diseases at Chest CT for Preventive Medicine. Healthcare (Basel) 2022; 10:2166. [PMID: 36360507 PMCID: PMC9690402 DOI: 10.3390/healthcare10112166] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 10/26/2022] [Accepted: 10/27/2022] [Indexed: 08/12/2023] Open
Abstract
Automated image analysis plays an increasing role in radiology in detecting and quantifying image features outside of the perception of human eyes. Common AI-based approaches address a single medical problem, although patients often present with multiple interacting, frequently subclinical medical conditions. A holistic imaging diagnostics tool based on artificial intelligence (AI) has the potential of providing an overview of multi-system comorbidities within a single workflow. An interdisciplinary, multicentric team of medical experts and computer scientists designed a pipeline, comprising AI-based tools for the automated detection, quantification and characterization of the most common pulmonary, metabolic, cardiovascular and musculoskeletal comorbidities in chest computed tomography (CT). To provide a comprehensive evaluation of each patient, a multidimensional workflow was established with algorithms operating synchronously on a decentralized Joined Imaging Platform (JIP). The results of each patient are transferred to a dedicated database and summarized as a structured report with reference to available reference values and annotated sample images of detected pathologies. Hence, this tool allows for the comprehensive, large-scale analysis of imaging-biomarkers of comorbidities in chest CT, first in science and then in clinical routine. Moreover, this tool accommodates the quantitative analysis and classification of each pathology, providing integral diagnostic and prognostic value, and subsequently leading to improved preventive patient care and further possibilities for future studies.
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Affiliation(s)
- Viktoria Palm
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Tobias Norajitra
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Division of Medical Imaging Computing, German Cancer Research Center Heidelberg, Im Neuenheimer Feld 223, 69120 Heidelberg, Germany
- Pattern Analysis and Learning Group, Department of Radiation Oncology, University Hospital of Heidelberg, Im Neuenheimer Feld 672, 69120 Heidelberg, Germany
| | - Oyunbileg von Stackelberg
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Claus P. Heussel
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Stephan Skornitzke
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Oliver Weinheimer
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Taisiya Kopytova
- Division of Medical Imaging Computing, German Cancer Research Center Heidelberg, Im Neuenheimer Feld 223, 69120 Heidelberg, Germany
| | - Andre Klein
- Division of Medical Imaging Computing, German Cancer Research Center Heidelberg, Im Neuenheimer Feld 223, 69120 Heidelberg, Germany
- Medical Faculty, University of Heidelberg, Im Neuenheimer Feld 672, 69120 Heidelberg, Germany
| | - Silvia D. Almeida
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Division of Medical Imaging Computing, German Cancer Research Center Heidelberg, Im Neuenheimer Feld 223, 69120 Heidelberg, Germany
- Medical Faculty, University of Heidelberg, Im Neuenheimer Feld 672, 69120 Heidelberg, Germany
| | - Michael Baumgartner
- Division of Medical Imaging Computing, German Cancer Research Center Heidelberg, Im Neuenheimer Feld 223, 69120 Heidelberg, Germany
| | - Dimitrios Bounias
- Division of Medical Imaging Computing, German Cancer Research Center Heidelberg, Im Neuenheimer Feld 223, 69120 Heidelberg, Germany
- Medical Faculty, University of Heidelberg, Im Neuenheimer Feld 672, 69120 Heidelberg, Germany
| | - Jonas Scherer
- Division of Medical Imaging Computing, German Cancer Research Center Heidelberg, Im Neuenheimer Feld 223, 69120 Heidelberg, Germany
- Medical Faculty, University of Heidelberg, Im Neuenheimer Feld 672, 69120 Heidelberg, Germany
| | - Klaus Kades
- Division of Medical Imaging Computing, German Cancer Research Center Heidelberg, Im Neuenheimer Feld 223, 69120 Heidelberg, Germany
| | - Hanno Gao
- Division of Medical Imaging Computing, German Cancer Research Center Heidelberg, Im Neuenheimer Feld 223, 69120 Heidelberg, Germany
| | - Paul Jäger
- Interactive Machine Learning Research Group, German Cancer Research Center Heidelberg, Im Neuenheimer Feld 223, 69120 Heidelberg, Germany
| | - Marco Nolden
- Division of Medical Imaging Computing, German Cancer Research Center Heidelberg, Im Neuenheimer Feld 223, 69120 Heidelberg, Germany
- Pattern Analysis and Learning Group, Department of Radiation Oncology, University Hospital of Heidelberg, Im Neuenheimer Feld 672, 69120 Heidelberg, Germany
| | - Elizabeth Tong
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Kira Eckl
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Johanna Nattenmüller
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine Freiburg, University of Freiburg, Hugstetter Str. 55, 79106 Freiburg, Germany
| | - Tobias Nonnenmacher
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Omar Naas
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Julia Reuter
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Arved Bischoff
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Jonas Kroschke
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Fabian Rengier
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Kai Schlamp
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Manuel Debic
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Hans-Ulrich Kauczor
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
| | - Klaus Maier-Hein
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Division of Medical Imaging Computing, German Cancer Research Center Heidelberg, Im Neuenheimer Feld 223, 69120 Heidelberg, Germany
- Pattern Analysis and Learning Group, Department of Radiation Oncology, University Hospital of Heidelberg, Im Neuenheimer Feld 672, 69120 Heidelberg, Germany
| | - Mark O. Wielpütz
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120 Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120 Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126 Heidelberg, Germany
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15
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Weikert T, Friebe L, Wilder-Smith A, Yang S, Sperl JI, Neumann D, Balachandran A, Bremerich J, Sauter AW. Automated quantification of airway wall thickness on chest CT using retina U-Nets - Performance evaluation and application to a large cohort of chest CTs of COPD patients. Eur J Radiol 2022; 155:110460. [PMID: 35963191 DOI: 10.1016/j.ejrad.2022.110460] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Revised: 07/17/2022] [Accepted: 07/31/2022] [Indexed: 12/24/2022]
Abstract
PURPOSE Airway wall thickening is a consequence of chronic inflammatory processes and usually only qualitatively described in CT radiology reports. The purpose of this study is to automatically quantify airway wall thickness in multiple airway generations and assess the diagnostic potential of this parameter in a large cohort of patients with Chronic Obstructive Pulmonary Disease (COPD). MATERIALS AND METHODS This retrospective, single-center study included a series of unenhanced chest CTs. Inclusion criteria were the mentioning of an explicit COPD GOLD stage in the written radiology report and time period (01/2019-12/2021). A control group included chest CTs with completely unremarkable lungs according to the report. The DICOM images of all cases (axial orientation; slice-thickness: 1 mm; soft-tissue kernel) were processed by an AI algorithm pipeline consisting of (A) a 3D-U-Net for det detection and tracing of the bronchial tree centerlines (B) extraction of image patches perpendicular to the centerlines of the bronchi, and (C) a 2D U-Net for segmentation of airway walls on those patches. The performance of centerline detection and wall segmentation was assessed. The imaging parameter average wall thickness was calculated for bronchus generations 3-8 (AWT3-8) across the lungs. Mean AWT3-8 was compared between five groups (control, COPD Gold I-IV) using non-parametric statistics. Furthermore, the established emphysema score %LAV-950 was calculated and used to classify scans (normal vs. COPD) alone and in combination with AWT3-8. RESULTS: A total of 575 chest CTs were processed. Algorithm performance was very good (airway centerline detection sensitivity: 86.9%; airway wall segmentation Dice score: 0.86). AWT3-8 was statistically significantly greater in COPD patients compared to controls (2.03 vs. 1.87 mm, p < 0.001) and increased with COPD stage. The classifier that combined %LAV-950 and AWT3-8 was superior to the classifier using only %LAV-950 (AUC = 0.92 vs. 0.79). CONCLUSION Airway wall thickness increases in patients suffering from COPD and is automatically quantifiable. AWT3-8 could become a CT imaging parameter in COPD complementing the established emphysema biomarker %LAV-950. CLINICAL RELEVANCE STATEMENT Quantitative measurements considering the complete visible bronchial tree instead of qualitative description could enhance radiology reports, allow for precise monitoring of disease progression and diagnosis of early stages of disease.
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Affiliation(s)
- Thomas Weikert
- Department of Radiology, University Hospital Basel, University of Basel, Petersgraben 4, 4031 Basel, Switzerland.
| | - Liene Friebe
- Department of Radiology, University Hospital Basel, University of Basel, Petersgraben 4, 4031 Basel, Switzerland
| | - Adrian Wilder-Smith
- Department of Radiology, University Hospital Basel, University of Basel, Petersgraben 4, 4031 Basel, Switzerland
| | - Shan Yang
- Department of Radiology, University Hospital Basel, University of Basel, Petersgraben 4, 4031 Basel, Switzerland
| | | | - Dominik Neumann
- Siemens Healthineers, Henkestrasse 127, 91052 Erlangen, Germany
| | | | - Jens Bremerich
- Department of Radiology, University Hospital Basel, University of Basel, Petersgraben 4, 4031 Basel, Switzerland
| | - Alexander W Sauter
- Department of Radiology, University Hospital Basel, University of Basel, Petersgraben 4, 4031 Basel, Switzerland
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16
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Deep Learning-Based Computed Tomography Features in Evaluating Early Screening and Risk Factors for Chronic Obstructive Pulmonary Disease. CONTRAST MEDIA & MOLECULAR IMAGING 2022; 2022:5951418. [PMID: 36051929 PMCID: PMC9410847 DOI: 10.1155/2022/5951418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 07/06/2022] [Accepted: 07/09/2022] [Indexed: 11/18/2022]
Abstract
This research aimed to investigate the diagnostic effect of computed tomography (CT) images based on a deep learning double residual convolution neural network (DRCNN) model on chronic obstructive pulmonary disease (COPD) and the related risk factors for COPD. The questionnaire survey was conducted among 980 permanent residents aged ≥ 40 years old. Among them, 84 patients who were diagnosed with COPD and volunteered to participate in the experiment and 25 healthy people were selected as the research subjects, and all of them underwent CT imaging scans. At the same time, an image noise reduction model based on the DRCNN was proposed to process CT images. The results showed that 84 of 980 subjects were diagnosed with COPD, and the overall prevalence of COPD in this epidemiological survey was 8.57%. Multivariate logistic regression model analysis showed that the regression coefficients of COPD with age, family history of COPD, and smoking were 0.557, 0.513, and 0.717, respectively (P < 0.05). The diagnostic sensitivity, specificity, and accuracy of DRCNN-based CT for COPD were greatly superior to those of single CT and the difference was considerable (P < 0.05). In summary, advanced age, family history of COPD, and smoking were independent risk factors for COPD. CT based on the DRCNN model can improve the diagnostic accuracy of simple CT images for COPD and has good performance in the early screening of COPD.
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17
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Quantitative Computed Tomography: What Clinical Questions Can it Answer in Chronic Lung Disease? Lung 2022; 200:447-455. [PMID: 35751660 PMCID: PMC9378468 DOI: 10.1007/s00408-022-00550-1] [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: 04/01/2022] [Accepted: 06/07/2022] [Indexed: 01/27/2023]
Abstract
Quantitative computed tomography (QCT) has recently gained an important role in the functional assessment of chronic lung disease. Its capacity in diagnostic, staging, and prognostic evaluation in this setting is similar to that of traditional pulmonary function testing. Furthermore, it can demonstrate lung injury before the alteration of pulmonary function test parameters, and it enables the classification of disease phenotypes, contributing to the customization of therapy and performance of comparative studies without the intra- and inter-observer variation that occurs with qualitative analysis. In this review, we address technical issues with QCT analysis and demonstrate the ability of this modality to answer clinical questions encountered in daily practice in the management of patients with chronic lung disease.
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18
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Zhu L, Duerr J, Zhou-Suckow Z, Wagner WL, Weinheimer O, Salomon JJ, Leitz D, Konietzke P, Yu H, Ackermann M, Stiller W, Kauczor HU, Mall MA, Wielpütz MO. µCT to quantify muco-obstructive lung disease and effects of neutrophil elastase knockout in mice. Am J Physiol Lung Cell Mol Physiol 2022; 322:L401-L411. [PMID: 35080183 DOI: 10.1152/ajplung.00341.2021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Muco-obstructive lung diseases are characterized by airway obstruction and hyperinflation, which can be quantified by imaging. Our aim was to evaluate µCT for longitudinal quantification of muco-obstructive lung disease in β-epithelial Na+ channel overexpressing (Scnn1b-TG) mice and of the effects of neutrophil elastase (NE) knockout on its progression. Lungs from wild-type (WT), NE-/-, Scnn1b-TG, and Scnn1b-TG/NE-/- mice were scanned with 9 µm resolution at 0, 5, 14 and 60 days of age, and airway and parenchymal disease was quantified. Mucus adhesion lesions (MAL) were persistently increased in Scnn1b-TG compared to WT mice from 0 days (20.25±6.50 vs. 9.60±2.07, P<0.05), and this effect was attenuated in Scnn1b-TG/NE-/- mice (5.33±3.67, P<0.001). Airway wall area percentage (WA%) was increased in Scnn1b-TG mice compared to WT from 14 days onward (59.2±6.3% vs. 49.8±9.0%, P<0.001) but was similar in Scnn1b-TG/NE-/- compared to WT at 60 days (46.4±9.2% vs. 45.4±11.5%, P=0.97). Air proportion (Air%) and mean linear intercept (Lm) were persistently increased in Scnn1b-TG compared to WT from 5 days on (53.9±4.5% vs. 30.0±5.5% and 78.82±8.44µm vs. 65.66±4.15µm, respectively, P<0.001), whereas in Scnn1b-TG/NE-/- Air% and Lm were similar to WT from birth (27.7±5.5% vs.27.2±5.9%, P =0.92 and 61.48±9.20µm vs. 61.70±6.73µm, P=0.93, respectively). Our results suggest that µCT is sensitive to detect the onset and progression of muco-obstructive lung disease and effects of genetic deletion of NE on morphology of airways and lung parenchyma in Scnn1b-TG mice, and that it may serve as a sensitive endpoint for preclinical studies of novel therapeutic interventions for muco-obstructive lung diseases.
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Affiliation(s)
- Lin Zhu
- Department of Diagnostic and Interventional Radiology, University Hospital of Heidelberg, Heidelberg, Germany.,Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Heidelberg, Germany.,Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Heidelberg, Germany
| | - Julia Duerr
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Heidelberg, Germany.,Department of Translational Pulmonology, University Hospital Heidelberg, Heidelberg, Germany.,Department of Pediatric Respiratory Medicine, Immunology and Critical Care Medicine, Charité-Universitätsmedizin Berlin, Berlin, Germany.,German Center for Lung Research (DZL), associated partner Berlin, Germany
| | - Zhe Zhou-Suckow
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Heidelberg, Germany.,Department of Translational Pulmonology, University Hospital Heidelberg, Heidelberg, Germany
| | - Willi L Wagner
- Department of Diagnostic and Interventional Radiology, University Hospital of Heidelberg, Heidelberg, Germany.,Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Heidelberg, Germany.,Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Heidelberg, Germany
| | - Oliver Weinheimer
- Department of Diagnostic and Interventional Radiology, University Hospital of Heidelberg, Heidelberg, Germany.,Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Heidelberg, Germany.,Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Heidelberg, Germany
| | - Johanna Jessica Salomon
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Heidelberg, Germany.,Department of Translational Pulmonology, University Hospital Heidelberg, Heidelberg, Germany
| | - Dominik Leitz
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Heidelberg, Germany.,Department of Translational Pulmonology, University Hospital Heidelberg, Heidelberg, Germany.,Department of Pediatric Respiratory Medicine, Immunology and Critical Care Medicine, Charité-Universitätsmedizin Berlin, Berlin, Germany.,German Center for Lung Research (DZL), associated partner Berlin, Germany
| | - Philip Konietzke
- Department of Diagnostic and Interventional Radiology, University Hospital of Heidelberg, Heidelberg, Germany.,Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Heidelberg, Germany.,Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Heidelberg, Germany
| | - Hong Yu
- Department of Radiology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Maximilian Ackermann
- Institute of Functional and Clinical Anatomy, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany.,Institute of Pathology and Department of Molecular Pathology, Helios University Clinic Wuppertal, University of Witten-Herdecke, Wuppertal, Germany
| | - Wolfram Stiller
- Department of Diagnostic and Interventional Radiology, University Hospital of Heidelberg, Heidelberg, Germany.,Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Heidelberg, Germany
| | - Hans-Ulrich Kauczor
- Department of Diagnostic and Interventional Radiology, University Hospital of Heidelberg, Heidelberg, Germany.,Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Heidelberg, Germany.,Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Heidelberg, Germany
| | - Marcus A Mall
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Heidelberg, Germany.,Department of Translational Pulmonology, University Hospital Heidelberg, Heidelberg, Germany.,Department of Pediatric Respiratory Medicine, Immunology and Critical Care Medicine, Charité-Universitätsmedizin Berlin, Berlin, Germany.,German Center for Lung Research (DZL), associated partner Berlin, Germany.,Berlin Institute of Health (BIH), Berlin, Germany
| | - Mark Oliver Wielpütz
- Department of Diagnostic and Interventional Radiology, University Hospital of Heidelberg, Heidelberg, Germany.,Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Heidelberg, Germany.,Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Heidelberg, Germany
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19
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Liu S, Ben X, Liang H, Fei Q, Guo X, Weng X, Wu Y, Wen L, Wang R, Chen J, Jing C. Association of acrylamide hemoglobin biomarkers with chronic obstructive pulmonary disease in the general population in the US: NHANES 2013-2016. Food Funct 2021; 12:12765-12773. [PMID: 34851334 DOI: 10.1039/d1fo02612g] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Background: Acrylamide is a well-known potential carcinogenic compound formed as an intermediate in the Maillard reaction during heat treatment, mainly from high-temperature frying, and is found in baked goods and coffee, as well as resulting from water treatment, textiles and paper processing. The effects of acrylamide on lung disease in humans remains unclear. We aimed to investigate the association between blood acrylamide and glycidamide and chronic obstructive pulmonary disease (COPD) in the United States of America (U.S.) population using PROC logistic regression models. Results: 2744 participants aged 20 to 80 from the 2013-2016 National Health and Nutrition Examination Survey (NHANES) were enrolled. After adjusting for demographic data, health factors and serum cotinine, the ratio of HbGA to HbAA (HbGA/HbAA) significantly increased the risk of COPD (P for trend = 0.022). The odds ratio (OR) with a 95% confidence interval (95% CI) for HbGA/HbAA in the third tile was 2.45 (1.12-5.31), compared with the lowest tile. The restricted cubic spline (RCS) curve showed a positive linear correlation between the log (HbGA/HbAA) and the risk of COPD (P = 0.030). Conclusion: The ratio of glycidamide and acrylamide (HbGA/HbAA) was associated with COPD. This association was more prominent in males, obese individuals, people with a poverty income ratio (PIR) < 1.85 or people who never exercise. However, null associations were observed between HbAA, HbGA and HbAA + HbGA, and COPD.
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Affiliation(s)
- Shan Liu
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, No. 601 Huangpu Ave West, Guangzhou 510632, Guangdong, China.
| | - Xiaosong Ben
- Department of Thoracic Surgery, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Huanzhu Liang
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, No. 601 Huangpu Ave West, Guangzhou 510632, Guangdong, China.
| | - Qiaoyuan Fei
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, No. 601 Huangpu Ave West, Guangzhou 510632, Guangdong, China.
| | - Xinrong Guo
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, No. 601 Huangpu Ave West, Guangzhou 510632, Guangdong, China.
| | - Xueqiong Weng
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, No. 601 Huangpu Ave West, Guangzhou 510632, Guangdong, China.
| | - Yingying Wu
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, No. 601 Huangpu Ave West, Guangzhou 510632, Guangdong, China.
| | - Lin Wen
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, No. 601 Huangpu Ave West, Guangzhou 510632, Guangdong, China.
| | - Ruihua Wang
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, No. 601 Huangpu Ave West, Guangzhou 510632, Guangdong, China.
| | - Jingmin Chen
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, No. 601 Huangpu Ave West, Guangzhou 510632, Guangdong, China.
| | - Chunxia Jing
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, No. 601 Huangpu Ave West, Guangzhou 510632, Guangdong, China. .,Guangdong Key Laboratory of Environmental Exposure and Health, Jinan University, Guangzhou 510632, Guangdong, China
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20
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Schiwek M, Triphan SMF, Biederer J, Weinheimer O, Eichinger M, Vogelmeier CF, Jörres RA, Kauczor HU, Heußel CP, Konietzke P, von Stackelberg O, Risse F, Jobst BJ, Wielpütz MO. Quantification of pulmonary perfusion abnormalities using DCE-MRI in COPD: comparison with quantitative CT and pulmonary function. Eur Radiol 2021; 32:1879-1890. [PMID: 34553255 PMCID: PMC8831348 DOI: 10.1007/s00330-021-08229-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 06/29/2021] [Accepted: 07/26/2021] [Indexed: 12/05/2022]
Abstract
Objectives Pulmonary perfusion abnormalities are prevalent in patients with chronic obstructive pulmonary disease (COPD), are potentially reversible, and may be associated with emphysema development. Therefore, we aimed to evaluate the clinical meaningfulness of perfusion defects in percent (QDP) using DCE-MRI. Methods We investigated a subset of baseline DCE-MRIs, paired inspiratory/expiratory CTs, and pulmonary function testing (PFT) of 83 subjects (age = 65.7 ± 9.0 years, patients-at-risk, and all GOLD groups) from one center of the “COSYCONET” COPD cohort. QDP was computed from DCE-MRI using an in-house developed quantification pipeline, including four different approaches: Otsu’s method, k-means clustering, texture analysis, and 80th percentile threshold. QDP was compared with visual MRI perfusion scoring, CT parametric response mapping (PRM) indices of emphysema (PRMEmph) and functional small airway disease (PRMfSAD), and FEV1/FVC from PFT. Results All QDP approaches showed high correlations with the MRI perfusion score (r = 0.67 to 0.72, p < 0.001), with the highest association based on Otsu’s method (r = 0.72, p < 0.001). QDP correlated significantly with all PRM indices (p < 0.001), with the strongest correlations with PRMEmph (r = 0.70 to 0.75, p < 0.001). QDP was distinctly higher than PRMEmph (mean difference = 35.85 to 40.40) and PRMfSAD (mean difference = 15.12 to 19.68), but in close agreement when combining both PRM indices (mean difference = 1.47 to 6.03) for all QDP approaches. QDP correlated moderately with FEV1/FVC (r = − 0.54 to − 0.41, p < 0.001). Conclusion QDP is associated with established markers of disease severity and the extent corresponds to the CT-derived combined extent of PRMEmph and PRMfSAD. We propose to use QDP based on Otsu’s method for future clinical studies in COPD. Key Points • QDP quantified from DCE-MRI is associated with visual MRI perfusion score, CT PRM indices, and PFT. • The extent of QDP from DCE-MRI corresponds to the combined extent of PRMEmph and PRMfSAD from CT. • Assessing pulmonary perfusion abnormalities using DCE-MRI with QDP improved the correlations with CT PRM indices and PFT compared to the quantification of pulmonary blood flow and volume. Supplementary Information The online version contains supplementary material available at 10.1007/s00330-021-08229-6.
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Affiliation(s)
- Marilisa Schiwek
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany.,Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Strasse 65, 88397, Biberach an der Riß, Germany.,Translational Lung Research Center Heidelberg (TLRC), German Lung Research Center (DZL), Im Neuenheimer Feld 156, 69120, Heidelberg, Germany
| | - Simon M F Triphan
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany.,Translational Lung Research Center Heidelberg (TLRC), German Lung Research Center (DZL), Im Neuenheimer Feld 156, 69120, Heidelberg, Germany
| | - Jürgen Biederer
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany.,Translational Lung Research Center Heidelberg (TLRC), German Lung Research Center (DZL), Im Neuenheimer Feld 156, 69120, Heidelberg, Germany.,Faculty of Medicine, University of Latvia, Raina bulvaris 19, Riga, 1586, Latvia.,Faculty of Medicine, Christian-Albrechts-Universität Zu Kiel, 24098, Kiel, Germany
| | - Oliver Weinheimer
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany.,Translational Lung Research Center Heidelberg (TLRC), German Lung Research Center (DZL), Im Neuenheimer Feld 156, 69120, Heidelberg, Germany
| | - Monika Eichinger
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany.,Translational Lung Research Center Heidelberg (TLRC), German Lung Research Center (DZL), Im Neuenheimer Feld 156, 69120, Heidelberg, Germany.,Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126, Heidelberg, Germany
| | - Claus F Vogelmeier
- Department of Medicine, Pulmonary and Critical Care Medicine, Philipps-University of Marburg (UMR), Marburg, Germany
| | - Rudolf A Jörres
- Institute and Outpatient Clinic for Occupational, Social and Environmental Medicine, University Hospital, Ludwig Maximilians University (LMU) Munich, Comprehensive Pneumology Center Munich (CPC-M), Munich, Germany
| | - Hans-Ulrich Kauczor
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany.,Translational Lung Research Center Heidelberg (TLRC), German Lung Research Center (DZL), Im Neuenheimer Feld 156, 69120, Heidelberg, Germany
| | - Claus P Heußel
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany.,Translational Lung Research Center Heidelberg (TLRC), German Lung Research Center (DZL), Im Neuenheimer Feld 156, 69120, Heidelberg, Germany.,Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at the University Hospital of Heidelberg, Röntgenstr. 1, 69126, Heidelberg, Germany
| | - Philip Konietzke
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany.,Translational Lung Research Center Heidelberg (TLRC), German Lung Research Center (DZL), Im Neuenheimer Feld 156, 69120, Heidelberg, Germany
| | - Oyunbileg von Stackelberg
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany.,Translational Lung Research Center Heidelberg (TLRC), German Lung Research Center (DZL), Im Neuenheimer Feld 156, 69120, Heidelberg, Germany
| | - Frank Risse
- Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Strasse 65, 88397, Biberach an der Riß, Germany
| | - Bertram J Jobst
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany.,Translational Lung Research Center Heidelberg (TLRC), German Lung Research Center (DZL), Im Neuenheimer Feld 156, 69120, Heidelberg, Germany
| | - Mark O Wielpütz
- Department of Diagnostic and Interventional Radiology, Subdivision of Pulmonary Imaging, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany. .,Translational Lung Research Center Heidelberg (TLRC), German Lung Research Center (DZL), Im Neuenheimer Feld 156, 69120, Heidelberg, Germany.
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21
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Li T, Zhou HP, Zhou ZJ, Guo LQ, Zhou L. Computed tomography-identified phenotypes of small airway obstructions in chronic obstructive pulmonary disease. Chin Med J (Engl) 2021; 134:2025-2036. [PMID: 34517376 PMCID: PMC8440009 DOI: 10.1097/cm9.0000000000001724] [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: 04/05/2021] [Indexed: 12/02/2022] Open
Abstract
ABSTRACT Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease characteristic of small airway inflammation, obstruction, and emphysema. It is well known that spirometry alone cannot differentiate each separate component. Computed tomography (CT) is widely used to determine the extent of emphysema and small airway involvement in COPD. Compared with the pulmonary function test, small airway CT phenotypes can accurately reflect disease severity in patients with COPD, which is conducive to improving the prognosis of this disease. CT measurement of central airway morphology has been applied in clinical, epidemiologic, and genetic investigations as an inference of the presence and severity of small airway disease. This review will focus on presenting the current knowledge and methodologies in chest CT that aid in identifying discrete COPD phenotypes.
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Affiliation(s)
- Tao Li
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital, Nanjing Medical University, Nanjing, Jiangsu 210029, China
- Department of Respiratory Medicine, Xuzhou First People's Hospital, Xuzhou, Jiangsu 221116, China
| | - Hao-Peng Zhou
- Department of Medicine, Jiangsu University School of Medicine, Zhenjiang, Jiangsu 212013, China
| | - Zhi-Jun Zhou
- Institute of Radio Frequency & Optical Electronics-Integrated Circuits, School of Information and Engineering, Southeast University, Nanjing, Jiangsu 210096, China
| | - Li-Quan Guo
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu 215163, China
| | - Linfu Zhou
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital, Nanjing Medical University, Nanjing, Jiangsu 210029, China
- Institute of Integrative Medicine, Nanjing Medical University, Nanjing, Jiangsu 210029, China
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22
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Pu J, Sechrist J, Meng X, Leader JK, Sciurba FC. A pilot study: Quantify lung volume and emphysema extent directly from two-dimensional scout images. Med Phys 2021; 48:4316-4325. [PMID: 34077564 DOI: 10.1002/mp.15019] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2020] [Revised: 05/26/2021] [Accepted: 05/26/2021] [Indexed: 12/12/2022] Open
Abstract
PURPOSE The potential to compute volume metrics of emphysema from planar scout images was investigated in this study. The successful implementation of this concept will have a wide impact in different fields, and specifically, maximize the diagnostic potential of the planar medical images. METHODS We investigated our premise using a well-characterized chronic obstructive pulmonary disease (COPD) cohort. In this cohort, planar scout images from computed tomography (CT) scans were used to compute lung volume and percentage of emphysema. Lung volume and percentage of emphysema were quantified on the volumetric CT images and used as the "ground truth" for developing the models to compute the variables from the corresponding scout images. We trained two classical convolutional neural networks (CNNs), including VGG19 and InceptionV3, to compute lung volume and the percentage of emphysema from the scout images. The scout images (n = 1,446) were split into three subgroups: (1) training (n = 1,235), (2) internal validation (n = 99), and (3) independent test (n = 112) at the subject level in a ratio of 8:1:1. The mean absolute difference (MAD) and R-square (R2) were the performance metrics to evaluate the prediction performance of the developed models. RESULTS The lung volumes and percentages of emphysema computed from a single planar scout image were significantly linear correlated with the measures quantified using volumetric CT images (VGG19: R2 = 0.934 for lung volume and R2 = 0.751 for emphysema percentage, and InceptionV3: R2 = 0.977 for lung volume and R2 = 0.775 for emphysema percentage). The mean absolute differences (MADs) for lung volume and percentage of emphysema were 0.302 ± 0.247L and 2.89 ± 2.58%, respectively, for VGG19, and 0.366 ± 0.287L and 3.19 ± 2.14, respectively, for InceptionV3. CONCLUSIONS Our promising results demonstrated the feasibility of inferring volume metrics from planar images using CNNs.
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Affiliation(s)
- Jiantao Pu
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jacob Sechrist
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Xin Meng
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Joseph K Leader
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Frank C Sciurba
- Department of Medicine, Division of Pulmonary, Allergy and Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, USA
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23
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Hoang QTM, Nguyen VK, Oberacher H, Fuchs D, Hernandez-Vargas EA, Borucki K, Waldburg N, Wippermann J, Schreiber J, Bruder D, Veluswamy P. Serum Concentration of the Phytohormone Abscisic Acid Is Associated With Immune-Regulatory Mediators and Is a Potential Biomarker of Disease Severity in Chronic Obstructive Pulmonary Disease. Front Med (Lausanne) 2021; 8:676058. [PMID: 34169084 PMCID: PMC8217626 DOI: 10.3389/fmed.2021.676058] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Accepted: 04/26/2021] [Indexed: 12/27/2022] Open
Abstract
COPD and asthma are two distinct but sometimes overlapping diseases exhibiting varying degrees and types of inflammation on different stages of the disease. Although several biomarkers are defined to estimate the inflammatory endotype and stages in these diseases, there is still a need for new markers and potential therapeutic targets. We investigated the levels of a phytohormone, abscisic acid (ABA) and its receptor, LANCL2, in COPD patients and asthmatics. In addition, PPAR-γ that is activated by ABA in a ligand-binding domain-independent manner was also included in the study. In this study, we correlated ABA with COPD-propagating factors to define the possible role of ABA, in terms of immune regulation, inflammation, and disease stages. We collected blood from 101 COPD patients, 52 asthmatics, and 57 controls. Bronchoscopy was performed on five COPD patients and 29 controls. We employed (i) liquid chromatography–tandem mass spectrometry and HPLC to determine the ABA and indoleamine 2,3-dioxygenase levels, respectively; (ii) real-time PCR to quantify the gene expression of LANCL2 and PPAR-γ; (iii) Flow cytometry to quantify adipocytokines; and (iv) immunoturbidimetry and ELISA to measure CRP and cytokines, respectively. Finally, a multinomial regression model was used to predict the probability of using ABA as a biomarker. Blood ABA levels were significantly reduced in COPD patients and asthmatics compared to age- and gender-matched normal controls. However, PPAR-γ was elevated in COPD patients. Intriguingly, ABA was positively correlated with immune-regulatory factors and was negatively correlated with inflammatory markers, in COPD. Of note, ABA was increased in advanced COPD stages. We thereby conclude that ABA might be involved in regulation of COPD pathogenesis and might be regarded as a potential biomarker for COPD stages.
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Affiliation(s)
- Quynh Trang Mi Hoang
- Department of Pneumonology, Otto-von-Guericke-University Magdeburg, University Hospital, Magdeburg, Germany.,Infection Immunology Group, Institute of Medical Microbiology and Hospital Hygiene, Health Campus Immunology, Infectiology and Inflammation, Otto-von-Guericke University Hospital, Magdeburg, Germany
| | - Van Kinh Nguyen
- Department of Infectious Diseases Epidemiology, Imperial College, London, United Kingdom
| | - Herbert Oberacher
- Institute of Legal Medicine and Core Facility Metabolomics, Medical University of Innsbruck, Innsbruck, Austria
| | - Dietmar Fuchs
- Institute of Biological Chemistry, Biocenter, Medical University of Innsbruck, Innsbruck, Austria
| | - Esteban A Hernandez-Vargas
- Systems Medicine for Infectious Diseases, Frankfurt Institute for Advanced Studies, Frankfurt, Germany.,Instituto de Matematicas, Universidad Nacional Autónoma de México (UNAM), Queretaro, Mexico
| | - Katrin Borucki
- Institute of Clinical Chemistry and Pathobiochemistry, Otto-von-Guericke University, Magdeburg, Germany
| | | | - Jens Wippermann
- Heart Surgery Research, Department of Cardiothoracic Surgery, Otto-von-Guericke University Hospital, Magdeburg, Germany
| | - Jens Schreiber
- Department of Pneumonology, Otto-von-Guericke-University Magdeburg, University Hospital, Magdeburg, Germany
| | - Dunja Bruder
- Infection Immunology Group, Institute of Medical Microbiology and Hospital Hygiene, Health Campus Immunology, Infectiology and Inflammation, Otto-von-Guericke University Hospital, Magdeburg, Germany.,Immune Regulation Group, Helmholtz Center for Infection Research, Braunschweig, Germany
| | - Priya Veluswamy
- Infection Immunology Group, Institute of Medical Microbiology and Hospital Hygiene, Health Campus Immunology, Infectiology and Inflammation, Otto-von-Guericke University Hospital, Magdeburg, Germany.,Heart Surgery Research, Department of Cardiothoracic Surgery, Otto-von-Guericke University Hospital, Magdeburg, Germany
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24
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Ross BD, Chenevert TL, Meyer CR. Retrospective Registration in Molecular Imaging. Mol Imaging 2021. [DOI: 10.1016/b978-0-12-816386-3.00080-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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25
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Schmitt VH, Schmitt C, Hollemann D, Mamilos A, Wagner W, Weinheimer O, Brochhausen C. Comparison of histological and computed tomographic measurements of pig lung bronchi. ERJ Open Res 2020; 6:00500-2020. [PMID: 33313303 PMCID: PMC7720685 DOI: 10.1183/23120541.00500-2020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Accepted: 09/14/2020] [Indexed: 12/03/2022] Open
Abstract
Aim Light microscopy is used as template in the evaluation and further development of medical imaging methods. Tissue shrinkage caused by histological processing is known to influence lung tissue dimensions. In diagnosis of COPD, computed tomography (CT) is widely used for automated airway measurement. The aim of this study was to compare histological and computed tomographic measurements of pig lung bronchi. Methods Airway measurements of pig lungs were performed after freezing under controlled inflation pressure in a liquid nitrogen bath. The wall thickness of seven bronchi was measured via Micro-CT and CT using the integral-based method (IBM) and the full-width-at-half-maximum method (FWHM) automatically and histologically on frozen and paraffin sections. Statistical analysis was performed using the Wilcoxon test, Pearson's correlation coefficient with a significance level at p<0.05, scatter plots and Bland–Altman plots. Results Bronchial wall thickness was smallest in frozen sections (median 0.71 mm) followed by paraffin sections (median 0.75 mm), Micro-CT (median 0.84 mm), and CT measurements using IBM (median 0.68 mm) and FWHM (median 1.69 mm). Statistically significant differences were found among all tested groups (p<0.05) except for CT IBM and paraffin and frozen sections and Micro-CT. There was high correlation between all parameters with statistical significance (p<0.05). Conclusions Significant differences in airway measurement were found among the different methods. The absolute measurements with CT IBM were closest to the histological results followed by Micro-CT, whereas CT FWHM demonstrated a distinct divergence from the other groups. Automated measurement techniques advance diagnosis of lung diseases. Pig bronchi wall size varies between Micro-CT, CT IBM, CT FWHM and histology. CT IBM is closest to histological results, followed by Micro-CT. CT FWHM differs highly from all other groups.https://bit.ly/3iRXSrv
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Affiliation(s)
- Volker H Schmitt
- Dept of Cardiology, University Medical Centre, Johannes Gutenberg University of Mainz, Mainz, Germany.,German Center for Cardiovascular Research (DZHK), Partner Site Rhine Main, Mainz, Germany.,Joint first authors
| | - Christine Schmitt
- Practice Dr Wolf and Colleagues, Mainz, Germany.,Joint first authors
| | - David Hollemann
- Institute of Clinical and Molecular Pathology, State Hospital Horn, Horn, Austria
| | - Andreas Mamilos
- REPAIR-lab, Institute of Pathology, University of Regensburg, Regensburg, Germany
| | - Willi Wagner
- Dept of Diagnostic and Interventional Radiology, University Hospital Heidelberg, Heidelberg, Germany.,Translational Lung Research Centre Heidelberg (TLRC), German Lung Research Centre (DZL), Heidelberg, Germany
| | - Oliver Weinheimer
- Dept of Diagnostic and Interventional Radiology, University Hospital Heidelberg, Heidelberg, Germany.,Translational Lung Research Centre Heidelberg (TLRC), German Lung Research Centre (DZL), Heidelberg, Germany.,Joint senior authors
| | - Christoph Brochhausen
- REPAIR-lab, Institute of Pathology, University of Regensburg, Regensburg, Germany.,Central Biobank Regensburg, University Regensburg and University Hospital Regensburg, Regensburg, Germany.,Joint senior authors
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26
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Konietzke P, Weinheimer O, Wagner WL, Wuennemann F, Hintze C, Biederer J, Heussel CP, Kauczor HU, Wielpütz MO. Optimizing airway wall segmentation and quantification by reducing the influence of adjacent vessels and intravascular contrast material with a modified integral-based algorithm in quantitative computed tomography. PLoS One 2020; 15:e0237939. [PMID: 32813730 PMCID: PMC7437894 DOI: 10.1371/journal.pone.0237939] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Accepted: 08/05/2020] [Indexed: 11/18/2022] Open
Abstract
Introduction Quantitative analysis of multi-detector computed tomography (MDCT) plays an increasingly important role in assessing airway disease. Depending on the algorithms used, airway dimensions may be over- or underestimated, primarily if contrast material was used. Therefore, we tested a modified integral-based method (IBM) to address this problem. Methods Temporally resolved cine-MDCT was performed in seven ventilated pigs in breath-hold during iodinated contrast material (CM) infusion over 60s. Identical slices in non-enhanced (NE), pulmonary-arterial (PA), systemic-arterial (SA), and venous phase (VE) were subjected to an in-house software using a standard and a modified IBM. Total diameter (TD), lumen area (LA), wall area (WA), and wall thickness (WT) were measured for ten extra- and six intrapulmonary airways. Results The modified IBM significantly reduced TD by 7.6%, LA by 12.7%, WA by 9.7%, and WT by 3.9% compared to standard IBM on non-enhanced CT (p<0.05). Using standard IBM, CM led to a decrease of all airway parameters compared to NE. For example, LA decreased from 80.85±49.26mm2 at NE, to 75.14±47.96mm2 (-7.1%) at PA (p<0.001), 74.96±48.55mm2 (-7.3%) at SA (p<0.001), and to 78.95±48.94mm2 (-2.4%) at VE (p = 0.200). Using modified IBM, the differences were reduced to -3.1% at PA, -2.9% at SA and -0.7% at VE (p<0.001; p<0.001; p = 1.000). Conclusions The modified IBM can optimize airway wall segmentation and reduce the influence of CM on quantitative CT. This allows a more precise measurement as well as potentially the comparison of enhanced with non-enhanced scans in inflammatory airway disease.
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Affiliation(s)
- Philip Konietzke
- Department of Diagnostic and Interventional Radiology, University Hospital of Heidelberg, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at University of Heidelberg, Heidelberg, Germany
- * E-mail:
| | - Oliver Weinheimer
- Department of Diagnostic and Interventional Radiology, University Hospital of Heidelberg, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at University of Heidelberg, Heidelberg, Germany
| | - Willi L. Wagner
- Department of Diagnostic and Interventional Radiology, University Hospital of Heidelberg, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at University of Heidelberg, Heidelberg, Germany
| | - Felix Wuennemann
- Department of Diagnostic and Interventional Radiology, University Hospital of Heidelberg, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at University of Heidelberg, Heidelberg, Germany
| | - Christian Hintze
- Department of Diagnostic Radiology, University Hospital Schleswig-Holstein, Kiel, Germany
- Department of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Radiologie Rein-Nahe, Bingen, Germany
| | - Juergen Biederer
- Department of Diagnostic and Interventional Radiology, University Hospital of Heidelberg, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at University of Heidelberg, Heidelberg, Germany
| | - Claus P. Heussel
- Department of Diagnostic and Interventional Radiology, University Hospital of Heidelberg, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at University of Heidelberg, Heidelberg, Germany
| | - Hans-Ulrich Kauczor
- Department of Diagnostic and Interventional Radiology, University Hospital of Heidelberg, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at University of Heidelberg, Heidelberg, Germany
| | - Mark O. Wielpütz
- Department of Diagnostic and Interventional Radiology, University Hospital of Heidelberg, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at University of Heidelberg, Heidelberg, Germany
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