{ "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.", "abstract_shown": true, "affiliations": [ "Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, Tennessee, United States of America", "Department of Computer Science, Vanderbilt University, Nashville, Tennessee, United States of America", "Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, United States of America", "Insitro, South San Francisco, California, United States of America", "Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America", "Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America", "Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America", "Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America", "Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America" ], "article_type": "Research Article", "arxiv": null, "author_total": 15, "authors": [ { "affs": [ 0 ], "name": "Aravind R. Krishnan" }, { "affs": [ 1 ], "name": "Michael E. Kim" }, { "affs": [ 2 ], "name": "Chenyu Gao" }, { "affs": [ 3 ], "name": "Gaurav Rudravaram" }, { "affs": [ 4 ], "name": "Trent Schwartz" }, { "affs": [ 5 ], "name": "Andre T. S. Hucke" }, { "affs": [ 6 ], "name": "Lucas W. Remedios" }, { "affs": [ 7 ], "name": "Thomas Z. Li" }, { "affs": [ 8 ], "name": "Kaiwen Xu" }, { "name": "Kim L. Sandler" }, { "name": "Yency Forero Martinez" }, { "name": "Sanja Antic" }, { "name": "Fabien Maldonado" }, { "name": "Lianrui Zuo" }, { "name": "Bennett A. Landman" } ], "bibtex": "https://research.draconitic.com/paper/fmrsktkpak95e.bib", "cited_by": 0, "doi": "10.1371/journal.pone.0352075", "fulltext_url": null, "id": "fmrsktkpak95e", "issns": [ "1932-6203" ], "landing_url": "https://doi.org/10.1371/journal.pone.0352075", "language": null, "licence": "CC-BY-4.0", "licence_label": "CC BY 4.0", "licence_source": "journal", "licence_url": "https://creativecommons.org/licenses/by/4.0/", "metadata_licence": null, "native_subjects": [ [ "openalex", "2740|Lung Cancer Diagnosis and Treatment" ], [ "plos", "/Biology and life sciences/Anatomy/Biological tissue/Connective tissue/Adipose tissue" ], [ "plos", "/Biology and life sciences/Anatomy/Musculoskeletal system/Muscles/Skeletal muscles" ], [ "plos", "/Biology and life sciences/Biochemistry/Biomarkers" ], [ "plos", "/Biology and life sciences/Neuroscience/Neuroimaging/Computed axial tomography" ], [ "plos", "/Medicine and health sciences/Anatomy/Biological tissue/Connective tissue/Adipose tissue" ], [ "plos", "/Medicine and health sciences/Anatomy/Musculoskeletal system/Muscles/Skeletal muscles" ], [ "plos", "/Medicine and health sciences/Diagnostic medicine/Diagnostic radiology/Pulmonary imaging" ], [ "plos", "/Medicine and health sciences/Diagnostic medicine/Diagnostic radiology/Tomography/Computed axial tomography" ], [ "plos", "/Medicine and health sciences/Oncology/Cancers and neoplasms/Lung and intrathoracic tumors" ], [ "plos", "/Medicine and health sciences/Pulmonology/Chronic obstructive pulmonary disease/Emphysema" ], [ "plos", "/Medicine and health sciences/Radiology and imaging/Diagnostic radiology/Pulmonary imaging" ], [ "plos", "/Medicine and health sciences/Radiology and imaging/Diagnostic radiology/Tomography/Computed axial tomography" ], [ "plos", "/Research and analysis methods/Imaging techniques/Diagnostic radiology/Pulmonary imaging" ], [ "plos", "/Research and analysis methods/Imaging techniques/Diagnostic radiology/Tomography/Computed axial tomography" ], [ "plos", "/Research and analysis methods/Imaging techniques/Neuroimaging/Computed axial tomography" ], [ "plos", "/Social sciences/Economics/Commerce/Vendors" ] ], "openalex": "W7220400496", "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0352075&type=printable", "pmcid": null, "pmid": null, "published": "2026-10-05", "publisher": "Public Library of Science", "ris": "https://research.draconitic.com/paper/fmrsktkpak95e.ris", "sources": [ { "first_seen": "2026-10-30T12:17:37Z", "native_id": "10.1371/journal.pone.0352075", "source": "plos", "url": "https://doi.org/10.1371/journal.pone.0352075" } ], "subjects": [ { "field": "medicine", "origin": "plos:diagnostic radiology", "subfield": "imaging" }, { "field": "medicine", "origin": "plos:anatomy", "subfield": "physiology" } ], "title": "Harmonizing the National Lung Screening Trial: Kernel resolution and field of view compensation", "title_text": "Harmonizing the National Lung Screening Trial: Kernel resolution and field of view compensation", "type": "peer-reviewed", "updated_at": "2026-10-30T12:17:37Z", "url": "https://research.draconitic.com/paper/fmrsktkpak95e", "venue": "PLOS One", "versions": [] }