Prediction of anatomical lung volume using planimetric measurements on chest radiographs

Feasibility of dynamic chest radiography to calculate lung volumes in adult people with cystic fibrosis: a pilot study

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.

Deep Learning for Estimating Lung Capacity on Chest Radiographs Predicts Survival in Idiopathic Pulmonary Fibrosis

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.

Automated estimation of total lung volume using chest radiographs and deep learning

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.

Prediction of forced vital capacity with dynamic chest radiography in interstitial lung disease

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.

Quantifying lung and diaphragm morphology using radiographs in normative pediatric subjects, and predicting CT-derived lung volume

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.

Predictors of long intensive care need after lung transplantation

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.

Projected lung areas using dynamic X-ray (DXR)

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The right projected lung area (PLA) was significantly larger than left one.

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PLA had correlation with height, weight, BMI, vital capacity (VC), and forced expiratory volume in one second (FEV₁).

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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.

The repeatability of computed tomography lung volume measurements: Comparisons in healthy subjects, patients with obstructive lung disease, and patients with restrictive lung disease

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.