The value of predicting the pathological aggressiveness of stage Ⅰ invasive lung adenocarcinoma based on high-resolution CT features and AI quantitative parameters
ZHANG Na
WANG Rong
LIU Zhenhe
PENG Wenting
XU Wanbo
XIE Bingkun
Abstract：Objective To explore the pathological invasiveness of stage Ⅰ invasive lung adenocarcinoma predicted by high-resolution CT features and AI quantitative parameters.Methods The imaging data of 95 patients with stage Ⅰ invasive lung ad-enocarcinoma were selected,and one or more pathological signs that met the criteria of vascular invasion,visceral pleural inva-sion,regional lymph node metastasis,and pulmonary alveolar dissemination were defined as the invasive group,and vice versa as the non-invasive group.The univariate and binary logistic regression analysis was used to analyze CT signs and AI quantitative parameters.Results There was statistical significance(P<0.05)in intergroup comparisons of nodule type,spiciness sign,pleural traction sign,nodule size,volume,mass,solid proportion,minimum CT value,average CT value,median,standard de-viation,skewness,kurtosis,etc.The minimum CT value,average CT value,proportion of solid volume,nodule type,and pleu-ral traction sign were independent risk factors for invasive lung adenocarcinoma,and the average CT value had the highest diag-nostic efficacy.The invasive group mainly showed solid nodules and pleural traction sign,with an average CT value higher than that of the non-invasive group.Conclusion CT features and AI quantitative parameters can predict the pathological invasive-ness of lung adenocarcinoma,providing assistance for clinical postoperative treatment plans.
Keywords：Lung adenocarcinomaPathologyInvasivenessTomographyX-ray computed
Publication Date：2024-10-28
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：5( 53-56,61 )