Quantitative CT analysis in lung cancer research: a bibliometric analysis based on Web of Science [2005-2025]

Quantitative computed tomography (CT) analysis plays an increasingly critical role in the diagnosis, treatment response assessment, and prognostic stratification of lung cancer. However, existing narrative and systematic reviews have not systematically delineated the evolutionary trajectories, knowledge gaps, or quantitative trends in this rapidly growing field. A bibliometric analysis is therefore timely to provide an objective, visual, and reproducible synthesis of research developments, collaboration networks, and emerging frontiers. This study aims to summarize and identify key research areas and developmental trends in quantitative CT analysis for lung cancer, thereby providing a reference for future research.

Articles on quantitative CT analysis of lung cancer published between 1 January 2005 and 3 October 2025 were retrieved from the Web of Science Core Collection (WoSCC). The search strategy employed was: (TS = ("Lung Neoplasms" OR "neoplasm* pulmonary" OR "pulmonary neoplasm*" OR "neoplasm* lung" OR "lung neoplasm" OR "lung cancer*" OR "cancer* lung" OR "cancer of lung" OR "pulmonary cancer*" OR "cancer* pulmonary" OR "cancer of the lung")) AND TS = ("Quantitative CT" OR "Quantitative Computed Tomography" OR "QCT" OR "CT densitometry" OR "Quantitative analysis" OR "Quantitative measurement" OR "Quantitative assessment" OR "Quantitative imaging" OR "3D Quantitative CT"). Inclusion criteria were publications relevant to the topic and published in open access. Exclusion criteria included conference proceedings, meeting abstracts, book chapters, early access publications, editorial material, letters, retracted publications, corrections, retractions, and non‑English publications. Two independent reviewers performed data screening and extraction. Visualization and bibliometric analyses (publication output, author collaboration networks, journal citations, and keywords) were conducted using CiteSpace and bioinformatics platforms.

A total of 1,618 publications were included. The United States and China were the most productive countries. Robert J. Gillies served as a key bridge connecting different research groups. The European Journal of Nuclear Medicine and Molecular Imaging had the highest average citations per article. Temporal evolution revealed three distinct phases: traditional volume measurement [2005-2010], manual image‑based machine learning [2011-2017], and deep learning automation [2018-2025]. Applications of quantitative CT analysis in lung cancer have been intensely focused on treatment response prediction, metastasis assessment, and clinical trial validation.

Quantitative CT analysis in lung cancer has evolved into a mature, multidisciplinary field. Despite rapid growth, major limitations remain: most studies are single‑centre and retrospective; CT acquisition and radiomic feature extraction protocols are not standardized; and external validation of artificial intelligence (AI) models is scarce. To address these gaps, future research should prioritize prospective multicentre trials, harmonization of scanning and analysis protocols, integration of quantitative CT with multi‑omics data, development of explainable and generalizable AI models, and standardized reporting of model performance. These steps will accelerate clinical translation toward precision diagnostics and therapeutics.