Predicting the invasiveness of lung adenocarcinoma:based on combined model utilizing clinical characteristics and plain CT imaging features
HONG Jingjing
WEN Ge
ZHANG Fei
GUO Jialiang
FENG Yanfang
HUANG Weikang
Abstract：Objective To evaluate the utility of a combined model integrating clinical characteristics and plain CT imaging features in predicting the invasiveness of primary lung adenocarcinoma with diameters less than 30 mm.Methods A retrospective analysis was conducted on 107 patients with pulmonary nodules who underwent preoperative chest CT scans at our hospital from January 2020 to December 2023.Patients were categorized into four groups based on pathological diagnosis:atypical adenomatous hyperplasia,adenocarcinoma in situ,minimally invasive adenocarcinoma,and invasive adenocarcinoma.Among them,73 patients with atypical adenomatous hyperplasia,adenocarcinoma in situ,and minimally invasive adenocarcinoma formed the non-invasive group(including 22 males,mean age 49.73±13.92),while 34 patients with invasive adenocarcinoma constituted the invasive group(including 14 males,mean age 57.53±12.00).Clinical data including age,gender,and imaging details were collected and compared between the two groups.Univariate and multivariate analyses were performed to identify independent predictors of lung adenocarcinoma invasiveness,leading to the development of a predictive model.Model performance was assessed using ROC curve analysis.Results Significant differences were observed between the two groups in terms of gender and age(P<0.05).Various CT imaging features such as nodule type,location,average diameter,shape,margin,spiculation,pleural indentation,presence of emphysema,air bronchogram,vacuole sign,and vascular type exhibited statistically significant differences between the groups(P<0.05).Average nodule diameter and vascular type were identified as independent predictors of lung adenocarcinoma invasiveness(P<0.05).The ROC curve analysis showed an AUC of 0.891(95%CI:0.816-0.956)for the combined model in diagnosing lung adenocarcinoma invasiveness.Using the optimal Youden index of 0.514,the cutoff value for average nodule diameter was determined to be 9.75 mm,with a sensitivity of 0.706 and specificity of 0.808.Conclusion Plain CT imaging features of pulmonary nodules play a crucial role in preoperatively assessing the invasiveness of lung adenocarcinoma.Average nodule diameter and vascular type independently predict lung adenocarcinoma invasiveness.
Keywords：lung adenocarcinomainvasivenessnodulecombined model
Publication Date：2024-11-28
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 1225-1230 )