ArticleJournal of applied clinical medical physics2026
Estimation of pulmonary function from time-resolved dynamic chest radiography using machine learning in patients with respiratory disease.
Article in Journal of applied clinical medical physics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundPulmonary function tests (PFTs), particularly spirometry, are the reference standard for assessing airflow limitation in respiratory diseases such as chronic obstructive pulmonary disease (COPD) and interstitial pulmonary disease. However, spirometry requires substantial patient cooperation and may be unreliable in children, the elderly, and patients with cognitive impairment, and its use was further limited during the COVID-19 pandemic. Dynamic chest radiography (DCR), which captures sequential thoracic images during respiration at low radiation dose, has emerged as a promising modality for evaluating respiratory dynamics, but its potential to quantitatively estimate pulmonary function through radiomic analysis remains insufficiently explored. PURPOSE: This study aimed to determine the potential of radiomic features of the lung on DCR to predict pulmonary function (FEV
methodsWe retrospectively analysed data from 151 patients. The DCRs at end-inspiration (EI), end-expiration (EE), and the respiratory phase of maximum variation in the lung area from EI to EE (insp2exp
resultsWe constructed a predictive regression and classification model for FEV
conclusionsIn this single-center retrospective study, radiomic features extracted from DCR at multiple respiratory phases combined with respiratory motion maps showed promise for estimating pulmonary function, outperforming conventional demographic-based prediction. External validation in multi-center cohorts is warranted before clinical translation.
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