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Published on: December 19, 2020
Combining high-resolution CT parameters and inflammatory markers to predict spread through air spaces in lung cancer
Zhihua Chen1, Yang Guo1, Keyang Zhou1
1Department of Radiology, The Second Affiliated Hospital, Hengyang Medical School, University of South China Hengyang 421001, Hunan, China.
Objective:
To explore the predictive value of high-resolution computed tomography (CT) parameters and inflammatory markers for spread through air spaces (STAS) in lung cancer patients.
Methods:
A 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.
Results:
Patients 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%.
Conclusion:
The 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.
