ArticleQuantitative imaging in medicine and surgery2023
Idiopathic pulmonary fibrosis disease progression: a dynamic quantitative chest computed tomography follow-up analysis.
Article in Quantitative imaging in medicine and surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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2 citing papers in PubMed, 1 citations in OpenAlex.
- Artificial intelligence-driven quantitative analysis of CT morphological differences between chronic thromboembolic pulmonary hypertension and chronic thromboembolic disease.Quantitative imaging in medicine and surgery · 2025Article
- Disease progression in idiopathic pulmonary fibrosis under anti-fibrotic treatment.Sarcoidosis, vasculitis, and diffuse lung diseases : official journal of WASOG · 2023Article
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8 authors at 4 institutions in 1 country.
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Abstract
Background: To clarify whether dynamic quantification of variables derived from chest high-resolution computed tomography (HRCT) can assess the progression of idiopathic pulmonary fibrosis (IPF). Methods: Patients with IPF who underwent serial computed tomography (CT) imaging were retrospectively enrolled. Several structural abnormalities seen on HRCT in IPF were segmented and quantified. Patients were divided into 2 groups according to their pulmonary function test (PFT) results: those with disease stabilization and those with disease progression, and differences between the groups were analyzed. Results: There were no statistically significant differences between the 2 patient groups for the following parameters: baseline PFTs, total lesion extent, lesion extent at different sites in the lungs, and pulmonary vessel-related parameters (with P values ranging from 0.057 to 0.894). Median changes in total lung volume, total lesion volume, and total lesion ratio were significantly higher in patients with worsening disease compared with those with stable disease (P<0.001). There was a significant increase in total lesion volume of 214.73 mL [interquartile range (IQR), 68.26 to 501.46 mL] compared with 3.67 mL (IQR, -71.70 to 85.33 mL) in the disease progression group compared with the disease stability group (P=0.001). The decline in pulmonary vessel volume and number of pulmonary vessel branches was more pronounced in the group with functional worsening compared with the group with functional stability. Moreover, changes in lesion volume ratio were negatively correlated with changes in diffusing capacity of the lungs for carbon monoxide (DLco) during follow-up (R=-0.57, P<0.001), and changes in pulmonary vessel-related parameters demonstrated positive correlation with DLco (with R ranging from 0.27 to 0.53, P<0.001) and forced vital capacity (FVC) (with R ranging from 0.44 to 0.61, P<0.001). Conclusions: Changes in CT-related parameters during follow-up may have better predictive performance compared with baseline imaging parameters and PFTs for disease progression in IPF.
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