ArticleAmerican journal of translational research2024
Combining high-resolution CT parameters and inflammatory markers to predict spread through air spaces in lung cancer.
Article in American journal of translational research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Impact of robotic and non-robotic approach on spread through air spaces in early-stage lung cancer.Journal of robotic surgery · 2025Article
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3 authors.
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Abstract
objectiveTo explore the predictive value of high-resolution computed tomography (CT) parameters and inflammatory markers for spread through air spaces (STAS) in lung cancer patients.
methodsA retrospective analysis was conducted on 72 lung cancer patients with STAS and 128 STAS-negative patients treated during the same period. Differences in high-resolution CT indicators and inflammatory markers between the two groups were assessed. Binary logistic regression was used to analyze the relationship between these indicators and STAS positivity. Receiver operating characteristic (ROC) curve analysis was performed to assess the predictive efficacy of these indicators for STAS positivity.
resultsPatients in the STAS-positive group exhibited a higher prevalence of leaf signs, pleural traction signs, and blurred tumor-lung boundaries than the STAS-negative group (P<0.05). Additionally, the STAS-positive group had elevated levels of the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), interleukin-6 (IL-6), and C-reactive protein (CRP), alongside a lower lymphocyte-to-monocyte ratio (LMR) (P<0.05). The combined predictive model incorporating pleural traction sign, LMR, NLR, PLR, SII, IL-6, and CRP yielded an area under the curve (AUC) of 0.977, with a sensitivity of 94.4% and a specificity of 90.8%.
conclusionThe integration of high-resolution CT parameters with inflammatory markers demonstrates significant value in predicting STAS positivity in lung cancer patients, with the combined predictive model showing superior performance.
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