ArticleThe British journal of radiology2021
Dependence of radiomic features on pixel size affects the diagnostic performance of radiomic signature for the invasiveness of pulmonary ground-glass nodule.
Article in The British journal of radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- Impact of deep learning image reconstruction on CT radiomics reproducibility in patients with pulmonary nodules.Insights into imaging · 2026Article
- Interpretable machine learning classification of cedar and cypress pollen on routine Durham slides for environmental exposure assessment.Frontiers in allergy · 2026Article
- Deep learning-based super-resolution US radiomics to differentiate testicular seminoma and non-seminoma: an international multicenter study.Insights into imaging · 2025Article
- Prediction of solid and micropapillary components in lung invasive adenocarcinoma: radiomics analysis from high-spatial-resolution CT data with 1024 matrix.Japanese journal of radiology · 2024Article
- A metabolomics study on carcinogenesis of ground-glass nodules.CytoJournal · 2024Article
- Longitudinal prediction of lung nodule invasiveness by sequential modelling with common clinical computed tomography (CT) measurements: a prediction accuracy study.Translational lung cancer research · 2022Article
- Prediction of future imagery of lung nodule as growth modeling with follow-up computed tomography scans using deep learning: a retrospective cohort study.Translational lung cancer research · 2022Article
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9 authors.
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Abstract
objectiveTo investigate the effect of reducing pixel size on the consistency of radiomic features and the diagnostic performance of the downstream radiomic signatures for the invasiveness for pulmonary ground-glass nodules (GGNs) on CTs.
methodsWe retrospectively collected the clinical data of 182 patients with GGNs on high resolution CT (HRCT). The CT images of different pixel sizes (0.8mm, 0.4mm, 0.18 mm) were obtained by reconstructing the single HRCT scan using three combinations of field of view and matrix size. For each pixel size setting, radiomic features were extracted for all GGNs and radiomic signatures for the invasiveness of GGNs were built through two modeling pipelines for comparison.
resultsThe study finally extracted 788 radiomic features. 87% radiomic features demonstrated inter pixel size variation. By either modeling pipeline, the radiomic signature under small pixel size performed significantly better than those under middle or large pixel sizes in predicting the invasiveness of GGNs (
conclusionsReducing pixel size could cause inconsistency in most radiomic features and improve the diagnostic performance of the downstream radiomic signatures. Particularly, super HRCTs with small pixel size resulted in more accurate radiomic signatures for the invasiveness of GGNs. ADVANCES IN KNOWLEDGE: The dependence of radiomic features on pixel size will affect the performance of the downstream radiomic signatures. The future radiomic studies should consider this effect of pixel size.
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