1
|
FitzMaurice TS, McCann C, Nazareth D, Hawkes S, Shaw M, McNamara PS, Walshaw M. Feasibility of dynamic chest radiography to calculate lung volumes in adult people with cystic fibrosis: a pilot study. BMJ Open Respir Res 2023; 10:e001309. [PMID: 37147023 PMCID: PMC10163553 DOI: 10.1136/bmjresp-2022-001309] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 04/21/2023] [Indexed: 05/07/2023] Open
Abstract
INTRODUCTION Dynamic chest radiography (DCR) is a novel, low-dose, real-time digital imaging system where software identifies moving thoracic structures and can automatically calculate lung areas. In an observational, prospective, non-controlled, single-centre pilot study, we compared it with whole-body plethysmography (WBP) in the measurement of lung volume subdivisions in people with cystic fibrosis (pwCF). METHODS Lung volume subdivisions were estimated by DCR using projected lung area (PLA) during deep inspiration, tidal breathing and full expiration, and compared with same-day WBP in 20 adult pwCF attending routine review. Linear regression models to predict lung volumes from PLA were developed. RESULTS Total lung area (PLA at maximum inspiration) correlated with total lung capacity (TLC) (r=0.78, p<0.001), functional residual lung area with functional residual capacity (FRC) (r=0.91, p<0.001), residual lung area with residual volume (RV) (r=0.82, p=0.001) and inspiratory lung area with inspiratory capacity (r=0.72, p=0.001). Despite the small sample size, accurate models were developed for predicting TLC, RV and FRC. CONCLUSION DCR is a promising new technology that can be used to estimate lung volume subdivisions. Plausible correlations between plethysmographic lung volumes and DCR lung areas were identified. Further studies are needed to build on this exploratory work in both pwCF and individuals without CF. TRIAL REGISTRATION NUMBER ISRCTN64994816.
Collapse
Affiliation(s)
- Thomas Simon FitzMaurice
- Department of Respiratory Medicine, Liverpool Heart and Chest Hospital NHS Foundation Trust, Liverpool, UK
- Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, UK
| | - Caroline McCann
- Department of Radiology, Liverpool Heart and Chest Hospital NHS Foundation Trust, Liverpool, UK
| | - Dilip Nazareth
- Department of Respiratory Medicine, Liverpool Heart and Chest Hospital NHS Foundation Trust, Liverpool, UK
- Institute of Infection and Global Health, University of Liverpool, Liverpool, UK
| | - Scott Hawkes
- Department of Pulmonary Physiology, Liverpool Heart and Chest Hospital NHS Foundation Trust, Liverpool, UK
| | - Matthew Shaw
- Research Department, Liverpool Heart and Chest Hospital NHS Foundation Trust, Liverpool, UK
| | - Paul Stephen McNamara
- Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, UK
- Department of Child Health (University of Liverpool), Institute in the Park, Alder Hey Children's Hospital NHS Foundation Trust, Liverpool, UK
| | - Martin Walshaw
- Department of Respiratory Medicine, Liverpool Heart and Chest Hospital NHS Foundation Trust, Liverpool, UK
- Institute of Infection and Global Health, University of Liverpool, Liverpool, UK
| |
Collapse
|
2
|
Kim H, Jin KN, Yoo SJ, Lee CH, Lee SM, Hong H, Witanto JN, Yoon SH. Deep Learning for Estimating Lung Capacity on Chest Radiographs Predicts Survival in Idiopathic Pulmonary Fibrosis. Radiology 2023; 306:e220292. [PMID: 36283113 DOI: 10.1148/radiol.220292] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Background Total lung capacity (TLC) has been estimated with use of chest radiographs based on time-consuming methods, such as planimetric techniques and manual measurements. Purpose To develop a deep learning-based, multidimensional model capable of estimating TLC from chest radiographs and demographic variables and validate its technical performance and clinical utility with use of multicenter retrospective data sets. Materials and Methods A deep learning model was pretrained with use of 50 000 consecutive chest CT scans performed between January 2015 and June 2017. The model was fine-tuned on 3523 pairs of posteroanterior chest radiographs and plethysmographic TLC measurements from consecutive patients who underwent pulmonary function testing on the same day. The model was tested with multicenter retrospective data sets from two tertiary care centers and one community hospital, including (a) an external test set 1 (n = 207) and external test set 2 (n = 216) for technical performance and (b) patients with idiopathic pulmonary fibrosis (n = 217) for clinical utility. Technical performance was evaluated with use of various agreement measures, and clinical utility was assessed in terms of the prognostic value for overall survival with use of multivariable Cox regression. Results The mean absolute difference and within-subject SD between observed and estimated TLC were 0.69 L and 0.73 L, respectively, in the external test set 1 (161 men; median age, 70 years [IQR: 61-76 years]) and 0.52 L and 0.53 L in the external test set 2 (113 men; median age, 63 years [IQR: 51-70 years]). In patients with idiopathic pulmonary fibrosis (145 men; median age, 67 years [IQR: 61-73 years]), greater estimated TLC percentage was associated with lower mortality risk (adjusted hazard ratio, 0.97 per percent; 95% CI: 0.95, 0.98; P < .001). Conclusion A fully automatic, deep learning-based model estimated total lung capacity from chest radiographs, and the model predicted survival in idiopathic pulmonary fibrosis. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Sorkness in this issue.
Collapse
Affiliation(s)
- Hyungjin Kim
- From the Department of Radiology (H.K., S.H.Y.), Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine (C.H.L., S.M.L.), and Medical Research Collaborating Center (H.H.), Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea; Department of Radiology, Seoul National University College of Medicine, Seoul, Korea (H.K., K.N.J., S.H.Y.); Department of Radiology, SMG-SNU Boramae Medical Center, Seoul, Korea (K.N.J.); Department of Radiology, Hanyang University Medical Center, Seoul, Korea (S.J.Y.); and MEDICAL IP, Seoul, Korea (J.N.W., S.H.Y.)
| | - Kwang Nam Jin
- From the Department of Radiology (H.K., S.H.Y.), Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine (C.H.L., S.M.L.), and Medical Research Collaborating Center (H.H.), Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea; Department of Radiology, Seoul National University College of Medicine, Seoul, Korea (H.K., K.N.J., S.H.Y.); Department of Radiology, SMG-SNU Boramae Medical Center, Seoul, Korea (K.N.J.); Department of Radiology, Hanyang University Medical Center, Seoul, Korea (S.J.Y.); and MEDICAL IP, Seoul, Korea (J.N.W., S.H.Y.)
| | - Seung-Jin Yoo
- From the Department of Radiology (H.K., S.H.Y.), Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine (C.H.L., S.M.L.), and Medical Research Collaborating Center (H.H.), Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea; Department of Radiology, Seoul National University College of Medicine, Seoul, Korea (H.K., K.N.J., S.H.Y.); Department of Radiology, SMG-SNU Boramae Medical Center, Seoul, Korea (K.N.J.); Department of Radiology, Hanyang University Medical Center, Seoul, Korea (S.J.Y.); and MEDICAL IP, Seoul, Korea (J.N.W., S.H.Y.)
| | - Chang Hoon Lee
- From the Department of Radiology (H.K., S.H.Y.), Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine (C.H.L., S.M.L.), and Medical Research Collaborating Center (H.H.), Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea; Department of Radiology, Seoul National University College of Medicine, Seoul, Korea (H.K., K.N.J., S.H.Y.); Department of Radiology, SMG-SNU Boramae Medical Center, Seoul, Korea (K.N.J.); Department of Radiology, Hanyang University Medical Center, Seoul, Korea (S.J.Y.); and MEDICAL IP, Seoul, Korea (J.N.W., S.H.Y.)
| | - Sang-Min Lee
- From the Department of Radiology (H.K., S.H.Y.), Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine (C.H.L., S.M.L.), and Medical Research Collaborating Center (H.H.), Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea; Department of Radiology, Seoul National University College of Medicine, Seoul, Korea (H.K., K.N.J., S.H.Y.); Department of Radiology, SMG-SNU Boramae Medical Center, Seoul, Korea (K.N.J.); Department of Radiology, Hanyang University Medical Center, Seoul, Korea (S.J.Y.); and MEDICAL IP, Seoul, Korea (J.N.W., S.H.Y.)
| | - Hyunsook Hong
- From the Department of Radiology (H.K., S.H.Y.), Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine (C.H.L., S.M.L.), and Medical Research Collaborating Center (H.H.), Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea; Department of Radiology, Seoul National University College of Medicine, Seoul, Korea (H.K., K.N.J., S.H.Y.); Department of Radiology, SMG-SNU Boramae Medical Center, Seoul, Korea (K.N.J.); Department of Radiology, Hanyang University Medical Center, Seoul, Korea (S.J.Y.); and MEDICAL IP, Seoul, Korea (J.N.W., S.H.Y.)
| | - Joseph Nathanael Witanto
- From the Department of Radiology (H.K., S.H.Y.), Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine (C.H.L., S.M.L.), and Medical Research Collaborating Center (H.H.), Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea; Department of Radiology, Seoul National University College of Medicine, Seoul, Korea (H.K., K.N.J., S.H.Y.); Department of Radiology, SMG-SNU Boramae Medical Center, Seoul, Korea (K.N.J.); Department of Radiology, Hanyang University Medical Center, Seoul, Korea (S.J.Y.); and MEDICAL IP, Seoul, Korea (J.N.W., S.H.Y.)
| | - Soon Ho Yoon
- From the Department of Radiology (H.K., S.H.Y.), Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine (C.H.L., S.M.L.), and Medical Research Collaborating Center (H.H.), Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea; Department of Radiology, Seoul National University College of Medicine, Seoul, Korea (H.K., K.N.J., S.H.Y.); Department of Radiology, SMG-SNU Boramae Medical Center, Seoul, Korea (K.N.J.); Department of Radiology, Hanyang University Medical Center, Seoul, Korea (S.J.Y.); and MEDICAL IP, Seoul, Korea (J.N.W., S.H.Y.)
| |
Collapse
|
3
|
Sogancioglu E, Murphy K, Th Scholten E, Boulogne LH, Prokop M, van Ginneken B. Automated estimation of total lung volume using chest radiographs and deep learning. Med Phys 2022; 49:4466-4477. [PMID: 35388486 PMCID: PMC9545721 DOI: 10.1002/mp.15655] [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: 09/07/2021] [Revised: 02/04/2022] [Accepted: 03/14/2022] [Indexed: 11/11/2022] Open
Abstract
Background Total lung volume is an important quantitative biomarker and is used for the assessment of restrictive lung diseases. Purpose In this study, we investigate the performance of several deep‐learning approaches for automated measurement of total lung volume from chest radiographs. Methods About 7621 posteroanterior and lateral view chest radiographs (CXR) were collected from patients with chest CT available. Similarly, 928 CXR studies were chosen from patients with pulmonary function test (PFT) results. The reference total lung volume was calculated from lung segmentation on CT or PFT data, respectively. This dataset was used to train deep‐learning architectures to predict total lung volume from chest radiographs. The experiments were constructed in a stepwise fashion with increasing complexity to demonstrate the effect of training with CT‐derived labels only and the sources of error. The optimal models were tested on 291 CXR studies with reference lung volume obtained from PFT. Mean absolute error (MAE), mean absolute percentage error (MAPE), and Pearson correlation coefficient (Pearson's r) were computed. Results The optimal deep‐learning regression model showed an MAE of 408 ml and an MAPE of 8.1% using both frontal and lateral chest radiographs as input. The predictions were highly correlated with the reference standard (Pearson's r = 0.92). CT‐derived labels were useful for pretraining but the optimal performance was obtained by fine‐tuning the network with PFT‐derived labels. Conclusion We demonstrate, for the first time, that state‐of‐the‐art deep‐learning solutions can accurately measure total lung volume from plain chest radiographs. The proposed model is made publicly available and can be used to obtain total lung volume from routinely acquired chest radiographs at no additional cost. This deep‐learning system can be a useful tool to identify trends over time in patients referred regularly for chest X‐ray.
Collapse
Affiliation(s)
- Ecem Sogancioglu
- Radboud university medical center, Institute for Health Sciences, Department of Medical Imaging, Nijmegen, The Netherlands
| | - Keelin Murphy
- Radboud university medical center, Institute for Health Sciences, Department of Medical Imaging, Nijmegen, The Netherlands
| | - Ernst Th Scholten
- Radboud university medical center, Institute for Health Sciences, Department of Medical Imaging, Nijmegen, The Netherlands
| | - Luuk H Boulogne
- Radboud university medical center, Institute for Health Sciences, Department of Medical Imaging, Nijmegen, The Netherlands
| | - Mathias Prokop
- Radboud university medical center, Institute for Health Sciences, Department of Medical Imaging, Nijmegen, The Netherlands
| | - Bram van Ginneken
- Radboud university medical center, Institute for Health Sciences, Department of Medical Imaging, Nijmegen, The Netherlands
| |
Collapse
|
4
|
Ueyama M, Hashimoto S, Takeda A, Maruguchi N, Yamamoto R, Matsumura K, Nakamura S, Terada S, Inao T, Kaji Y, Yasuda T, Hajiro T, Tanaka E, Taguchi Y, Noma S. Prediction of forced vital capacity with dynamic chest radiography in interstitial lung disease. Eur J Radiol 2021; 142:109866. [PMID: 34365304 DOI: 10.1016/j.ejrad.2021.109866] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 07/06/2021] [Accepted: 07/13/2021] [Indexed: 10/20/2022]
Abstract
PURPOSE The pulmonary function test (PFT) has played an essential role in diagnosing and managing interstitial lung disease (ILD) but has its contraindications and difficult conditions to perform. Therefore, the present study aimed to evaluate dynamic chest radiography (DCR) ability to predict forced vital capacity (FVC) and other PFT parameters of ILD patients. METHOD The prospective observational study included 97 patients who underwent DCR at Tenri Hospital (Tenri, Japan) between June 2019 and April 2020. Twenty-five patients with stable disease status underwent DCR twice to evaluate test-retest reliability using the intraclass correlation coefficient. From the lung field areas measured by DCR, lung volumes at maximum inspiration (V.ins) and expiration (V.exp) were estimated. Correlation coefficients between the measured values of DCR and PFT parameters were calculated. Multilinear models for predicting FVC and other PFT parameters were developed. RESULTS Intraclass correlation coefficients between first and second measurements of V.ins and V.exp were 0.94 (95% CI: 0.89-0.97, p < 0.001) and 0.88 (95% CI: 0.78-0.94, p < 0.001), respectively. The correlation coefficient between V.ins and FVC was 0.86 (95% CI: 0.79-0.90, p < 0.001). A multilinear model for predicting FVC was developed using V.ins, V.exp, age, sex, and body mass index as predictor variables, wherein the adjusted coefficient of determination was 0.814. CONCLUSIONS Lung volumes measured by DCR correlated with the lung function of ILD patients. Prediction models with high predictive power and internal validity were developed, suggesting that DCR can predict FVC and other PFT parameters of ILD patients.
Collapse
Affiliation(s)
- Masakuni Ueyama
- Department of Respiratory Medicine, Tenri Hospital, 200 Mishima-cho Tenri-shi, Nara 632-8552, Japan.
| | - Seishu Hashimoto
- Department of Respiratory Medicine, Tenri Hospital, 200 Mishima-cho Tenri-shi, Nara 632-8552, Japan
| | - Atsushi Takeda
- Department of Respiratory Medicine, Tenri Hospital, 200 Mishima-cho Tenri-shi, Nara 632-8552, Japan
| | - Naoto Maruguchi
- Department of Respiratory Medicine, Tenri Hospital, 200 Mishima-cho Tenri-shi, Nara 632-8552, Japan
| | - Ryo Yamamoto
- Department of Respiratory Medicine, Tenri Hospital, 200 Mishima-cho Tenri-shi, Nara 632-8552, Japan
| | - Kazuki Matsumura
- Department of Respiratory Medicine, Tenri Hospital, 200 Mishima-cho Tenri-shi, Nara 632-8552, Japan
| | - Satoshi Nakamura
- Department of Respiratory Medicine, Tenri Hospital, 200 Mishima-cho Tenri-shi, Nara 632-8552, Japan
| | - Satoru Terada
- Department of Respiratory Medicine, Kyoto University, Yoshida-honmachi Sakyo-ku Kyoto-shi, Kyoto 606-8501, Japan
| | - Takashi Inao
- Department of Respiratory Medicine, Shinko Hospital, 1-4-47 Wakinohama-cho Chuo-ku Kobe-shi, Hyogo 651-0072, Japan
| | - Yusuke Kaji
- Department of Respiratory Medicine, Tenri Hospital, 200 Mishima-cho Tenri-shi, Nara 632-8552, Japan
| | - Takehiro Yasuda
- Department of Respiratory Medicine, Tenri Hospital, 200 Mishima-cho Tenri-shi, Nara 632-8552, Japan
| | - Takashi Hajiro
- Department of Respiratory Medicine, Tenri Hospital, 200 Mishima-cho Tenri-shi, Nara 632-8552, Japan
| | - Eisaku Tanaka
- Department of Respiratory Medicine, Tenri Hospital, 200 Mishima-cho Tenri-shi, Nara 632-8552, Japan
| | - Yoshio Taguchi
- Department of Respiratory Medicine, Tenri Hospital, 200 Mishima-cho Tenri-shi, Nara 632-8552, Japan
| | - Satoshi Noma
- Department of Radiology, Tenri Hospital, 200 Mishima-cho Tenri-shi, Nara 632-8552, Japan
| |
Collapse
|
5
|
Orbach MR, Servaes SE, Mayer OH, Cahill PJ, Balasubramanian S. Quantifying lung and diaphragm morphology using radiographs in normative pediatric subjects, and predicting CT-derived lung volume. Pediatr Pulmonol 2021; 56:2177-2185. [PMID: 33860632 DOI: 10.1002/ppul.25429] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Revised: 04/03/2021] [Accepted: 04/11/2021] [Indexed: 11/09/2022]
Abstract
OBJECTIVE To quantify the effect of age on two-dimensional (2D) radiographic lung and diaphragm morphology and determine if 2D radiographic lung measurements can be used to estimate computer tomography (CT)-derived lung volume in normative pediatric subjects. MATERIALS AND METHODS Digitally reconstructed radiographs (DRRs) were created using retrospective chest CT scans from 77 pediatric male and female subjects aged birth to 19 years. 2D lung and diaphragm measurements were made on the DRRs using custom MATLAB code, and Spearman correlations and exponential regression equations were used to relate 2D measurements with age. In addition, 3D lung volumes were segmented using CT scans, and power regression equations were fitted to predict each lung's CT-derived volume from 2D lung measurements. The coefficient of determination (R2 ) and standard error of the estimate (SEE) were used to assess the precision of the predictive equations with p < .05 indicating statistical significance. RESULTS All 2D radiographic lung and diaphragm measurements showed statistically significant positive correlations with age (p < .01), including lung major axis (Spearman rho ≥ 0.90). Precise estimations of CT-derived lung volumes can be made using 2D lung measurements (R2 ≥ 0.95), including lung major axis (R2 ≥ 0.97). INTERPRETATIONS The reported pediatric age-specific reference data on 2D lung and diaphragm morphology and growth rates could be clinically used to identify lung and diaphragm pathologies during chest X-ray evaluations. The simple, precise, and clinically adaptable radiographic method for estimating CT-derived lung volumes may be used when pulmonary function tests are not readily available or difficult to perform.
Collapse
Affiliation(s)
- Mattan R Orbach
- School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, Pennsylvania, USA
| | - Sabah E Servaes
- Department of Radiology, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Oscar H Mayer
- Division of Pulmonary Medicine, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Patrick J Cahill
- Division of Orthopaedic Surgery, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Sriram Balasubramanian
- School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, Pennsylvania, USA
| |
Collapse
|
6
|
Olsen JE, Auråen H, Durheim MT, Fiane AE, Holm AM. Predictors of long intensive care need after lung transplantation. Clin Transplant 2020; 35:e14152. [PMID: 33185913 DOI: 10.1111/ctr.14152] [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: 06/26/2020] [Revised: 10/23/2020] [Accepted: 10/29/2020] [Indexed: 11/30/2022]
Abstract
BACKGROUND While expected need for intensive care after lung transplantation (LTx) does not normally affect organ allocation, it would be useful to estimate whether intensive care capacity is limited. The aim of this study was to assess factors available before LTx to identify predictors of prolonged intensive care unit (ICU) length of stay (LOS) after LTx. METHODS All bilateral LTx recipients excluding re-transplantation and multi-organ transplantation at Oslo University Hospital from 2000 to 2013 were included (n = 277). Predictive factors for ICU LOS were identified using pre- and perioperative variables. RESULTS Univariate analyses showed that recipients with pulmonary arterial hypertension, young age, female gender, low body height, low pretransplant actual total lung capacity (aTLC), and recipients who received an oversized donor lung were at risk for long ICU LOS. Patients with emphysema had lower risk of long ICU LOS. In multivariate analyses, a lower aTLC (p < .001) and a higher mean pulmonary artery pressure (mPAP) (p = .004) predicted prolonged ICU LOS. CONCLUSIONS We found that small recipient lung volume and high mPAP were predictors for prolonged ICU LOS. Our observations may be useful in planning use of resources in LTx, particularly in times of limited intensive care resources.
Collapse
Affiliation(s)
- Jul Eirik Olsen
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Henrik Auråen
- Department of Gastroenterological Surgery, Oslo University Hospital, Ullevål, Oslo, Norway
| | - Michael T Durheim
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway.,Department of Respiratory Medicine, Oslo University Hospital, Rikshospitalet, Oslo, Norway
| | - Arnt E Fiane
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway.,Department of Cardiothoracic Surgery, Oslo University Hospital, Rikshospitalet, Oslo, Norway
| | - Are M Holm
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway.,Department of Respiratory Medicine, Oslo University Hospital, Rikshospitalet, Oslo, Norway
| |
Collapse
|
7
|
Hino T, Hata A, Hida T, Yamada Y, Ueyama M, Araki T, Kamitani T, Nishino M, Kurosaki A, Jinzaki M, Ishigami K, Honda H, Hatabu H, Kudoh S. Projected lung areas using dynamic X-ray (DXR). Eur J Radiol Open 2020; 7:100263. [PMID: 32953949 PMCID: PMC7486627 DOI: 10.1016/j.ejro.2020.100263] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Revised: 07/31/2020] [Accepted: 08/24/2020] [Indexed: 11/30/2022] Open
Abstract
The right projected lung area (PLA) was significantly larger than left one. PLA had correlation with height, weight, BMI, vital capacity (VC), and forced expiratory volume in one second (FEV1). Multivariate analysis showed that body mass index (BMI), sex and VC were considered independent correlation factors, respectively.
Background Dynamic X-ray (DXR) provides images of multiple phases of breath with less radiation exposure than CT. The exact images at end-inspiratory or end-expiratory phases can be chosen accurately. Purpose To investigate the correlation of the projected lung area (PLA) by dynamic chest X-ray with pulmonary functions. Material and Methods One hundred sixty-two healthy volunteers who received medical check-ups for health screening were included in this study. All subjects underwent DXR in both posteroanterior (PA) and lateral views and pulmonary function tests on the same day. All the volunteers took several tidal breaths before one forced breath as instructed. The outlines of lungs were contoured manually on the workstation with reference to the motion of diaphragm and the graph of pixel values. The PLAs were calculated automatically, and correlations with pulmonary functions and demographic data were analyzed statistically. Results The PLAs have correlation with physical characteristics, including height, weight and BMI, and pulmonary functions such as vital capacity (VC) and forced expiratory volume in one second (FEV1). VC and FEV1 revealed moderate correlation with the PLAs of PA view in forced inspiratory phase (VC: right, r = 0.65; left, r = 0.69. FEV1: right, r = 0.54; left, r = 0.59). Multivariate analysis showed that body mass index (BMI), sex and VC were considered independent correlation factors, respectively. Conclusion PLA showed statistically significant correlation with pulmonary functions. Our results indicate DXR has a possibility to serve as an alternate method for pulmonary function tests in subjects requiring contact inhibition including patients with suspected or confirmed covid-19.
Collapse
Key Words
- %FEV1, percent predicted FEV1
- %VC, percent vital capacity
- BMI, body mass index
- COPD, chronic obstructive pulmonary disease
- Chest radiograph
- DXR, dynamic X-ray
- FEV1%, forced expiratory volume percent in one second divided by FVC
- FEV1, forced expiratory volume in one second
- FPD, flat-panel detector
- FVC, forced vital capacity
- Health screening cohort
- IPF, idiopathic pulmonary fibrosis
- PA, posteroanterior
- PFTs, pulmonary function tests
- PLA, projected lung area
- Projected lung area
- Pulmonary function
- TLC, total lung capacity
- TV, tidal volume
- VC, vital capacity
- dynamic X-ray
Collapse
Affiliation(s)
- Takuya Hino
- Center for Pulmonary Functional Imaging, Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, 75 Francis St., Boston, MA, USA
- Corresponding author at: Center for Pulmonary Functional Imaging, Department of Radiology, Brigham and Women’s Hospital and Harvard Medical School, 75 Francis Street, Boston, MA 02115, USA.
| | - Akinori Hata
- Center for Pulmonary Functional Imaging, Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, 75 Francis St., Boston, MA, USA
| | - Tomoyuki Hida
- Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, Fukuoka, Japan
| | - Yoshitake Yamada
- Department of Diagnostic Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, Japan
| | - Masako Ueyama
- Department of Health Care, Fukujuji Hospital, Japan Anti-Tuberculosis Association, 3-1-24 Matsuyama, Kiyose, Tokyo, Japan
| | - Tetsuro Araki
- Center for Pulmonary Functional Imaging, Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, 75 Francis St., Boston, MA, USA
| | - Takeshi Kamitani
- Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, Fukuoka, Japan
| | - Mizuki Nishino
- Center for Pulmonary Functional Imaging, Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, 75 Francis St., Boston, MA, USA
| | - Atsuko Kurosaki
- Department of Diagnostic Radiology, Fukujuji Hospital, Japan Anti-Tuberculosis Association, 3-1-24 Matsuyama, Kiyose, Tokyo, Japan
| | - Masahiro Jinzaki
- Department of Diagnostic Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, Japan
| | - Kousei Ishigami
- Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, Fukuoka, Japan
| | - Hiroshi Honda
- Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, Fukuoka, Japan
| | - Hiroto Hatabu
- Center for Pulmonary Functional Imaging, Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, 75 Francis St., Boston, MA, USA
| | - Shoji Kudoh
- Japan Anti-Tuberculosis Association, 1-3-12 Kanda-Misakicho, Chiyoda-ku, Tokyo, Japan
| |
Collapse
|
8
|
|
9
|
Shin JM, Kim TH, Haam S, Han K, Byun MK, Chang YS, Kim HJ, Park CH. The repeatability of computed tomography lung volume measurements: Comparisons in healthy subjects, patients with obstructive lung disease, and patients with restrictive lung disease. PLoS One 2017; 12:e0182849. [PMID: 28796825 PMCID: PMC5552110 DOI: 10.1371/journal.pone.0182849] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2017] [Accepted: 07/25/2017] [Indexed: 11/18/2022] Open
Abstract
In this study, we examined the repeatability of computed tomography (CT) lung volume measurements in healthy individuals and patients with obstructive and restrictive lung diseases. To do this, we retrospectively enrolled 200 healthy individuals (group 1), 100 patients with obstructive lung disease (group 2), and 100 patients with restrictive lung disease (group 3) who underwent two consecutive chest CT scans within a 1-year period. The CT lung volume was measured using a threshold-based, three-dimensional auto-segmentation technique at a default range from -200 to -1024 HU. The within-subject standard deviation, repeatability coefficient, within-subject coefficient variability, and intraclass correlation coefficient were evaluated. No significant differences were identified between the two consecutive CT lung volume measurements in any of the groups (p> 0.05). The within-subject standard deviations for groups 1, 2, and 3 were 441.1, 387.0, and 288.6, respectively, while the repeatability coefficients were 1222.6, 1072.6, and 800.1, respectively. The within-subject coefficient variabilities for groups 1, 2, and 3 were 0.097, 0.083, and 0.090, respectively, while the intraclass correlation coefficients were 0.818, 0.881, and 0.910, respectively. The two CT lung volume measurements showed excellent agreement in healthy individuals and patients with obstructive or restrictive lung disease. However, the repeatability was lower in healthy individuals than it was in patients with lung diseases.
Collapse
Affiliation(s)
- Jae Min Shin
- Department of Radiology and the Research Institute of Radiological Science, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Tae Hoon Kim
- Department of Radiology and the Research Institute of Radiological Science, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Seokjin Haam
- Department of Thoracic and Cardiovascular Surgery, Ajou University School of Medicine, Suwon, Republic of Korea
| | - Kyunghwa Han
- Department of Radiology, Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Min Kwang Byun
- Division of Pulmonology, Department of Internal Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Yoon Soo Chang
- Division of Pulmonology, Department of Internal Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Hyung Jung Kim
- Division of Pulmonology, Department of Internal Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Chul Hwan Park
- Department of Radiology and the Research Institute of Radiological Science, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
- * E-mail:
| |
Collapse
|