ArticlePloS one2026
Harmonizing the National Lung Screening Trial: Kernel resolution and field of view compensation.
Article in PloS one, 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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15 authors.
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Abstract
Low-dose chest computed tomography scans acquired in multi-centre studies exhibit heterogeneity in the form of varying spatial resolution, reconstruction kernel and semantic field-of-view that hinder the reproducibility of quantitative measures and generalizability of downstream analyses. The National Lung Screening Trial (NLST) provides a large, open collection of low-dose chest CT scans characterized by heterogeneous spatial resolution, reconstruction kernels and field-of-view configurations. This diversity presents an opportunity to standardize the data, enabling reproducible biomarker estimation and broader reuse of the NLST as a homogeneous quantitative imaging resource. In this work, we present a standardization pipeline that was applied to 105,191 scans from 23,669 participants in the NLST. The pipeline addresses the following elements: (1) Resampling through-axial-plane (z) spatial resolution to 1.0 mm (2) Harmonization of reconstruction kernels across intra-vendor (paired) and inter-vendor (unpaired) settings using an anatomy guided multipath cycleGAN where scans were mapped to a reference Siemens B30f (soft) and Siemens B50f (hard) kernel (3) Semantic field-of-view extension across all scans. Quantitative consistency across percent emphysema and body composition assessment (skeletal muscle and subcutaneous adipose tissue volumes) was evaluated in paired, cross-sectional and longitudinal settings. For paired data, harmonization improved agreement of quantitative measures relative to the soft kernel reference. Cross-sectional analyses demonstrated reduced inter-kernel variability, with small to medium effect sizes (Cohen's d = 0.2 to 0.5) for percent emphysema and improved correlations with anthropometric measures post field-of-view extension. Longitudinal analysis using linear mixed effect models, showed reduced kernel bias and improved longitudinal consistency of biomarkers reflected by the increase in intraclass correlation coefficients. This work provides a harmonized NLST imaging resource that enables consistent and reliable quantitative biomarker estimation for lung cancer screening, with open source code released at https://github.com/MASILab/Harmonized_NLST_dataset and the harmonized dataset to be made publicly available once data return to the NLST is completed.
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