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

Masakuni Ueyama, Seishu Hashimoto, Atsushi Takeda, Naoto Maruguchi, Ryo Yamamoto, Kazuki Matsumura, Satoshi Nakamura, Satoru Terada, Takashi Inao, Yusuke Kaji, Takehiro Yasuda, Takashi Hajiro, Eisaku Tanaka, Yoshio Taguchi, Satoshi Noma

2021European Journal of Radiology25 citationsDOIOpen Access PDF

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.

Topics & Concepts

MedicineRadiographyVital capacityInterstitial lung diseaseRadiologyLung diseaseLungDiffusing capacityLung functionInternal medicineInterstitial Lung Diseases and Idiopathic Pulmonary FibrosisChronic Obstructive Pulmonary Disease (COPD) ResearchRadiomics and Machine Learning in Medical Imaging
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