基于高分辨率CT征象联合临床特征预测磨玻璃结节型 肺腺癌浸润性的临床价值

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【关键词】肺腺癌；磨玻璃结节；高分辨率CT；浸润性腺癌；预测模型 DOI：10.19522/j.cnki.1671-5098.2025.11.008

Clinical value of high-resolution CTcombined with clinical features on predicting the invasionof ground glassnodule type of lungadenocarcinoma

Zheng Yi DepartmentofRadiology，HonghuHospitalofTraditionalhineseMedicine，Honghu4332oo，China

【Abstract】 Objective To studythe valueof high-resolution computed tomography （HRCT） imaging features and clinical features in non-invasive diffrential diagnosis of invasion of ground-glass nodule （GGN） type of lung adenocarcinoma.MethodsThe data of 233 patients with GGN type of lung adenocarcinoma who received surgical treatmentand pathological diagnosis in the hospital fromJune 2O23 toApril 2025 were retrospectivelycollected. According to thepathological results，theyweredivided into non-invasivegroupand invasivegroup.Potential predictors were screened by univariate analysis.Multivariate logistic regresion analysis was used to determine the influencing factors of invasion of GGN type of lung adenocarcinoma.Receiver operating characteristic （ROC） curve was drawnand thearea underthe curve（AUC）was calculated to evaluate the predictive eficiencyof the model. ResultsCompared with non-invasive group，the GGN maximum diameter，average CT value and maximum CT value in invasive group were higher，and the possibilities of smoking history，irregular shape，lobulation sign and spicule sign were greater （ ）. Logistic regression results showed that the above features were independent risk factors of invasion of GGN type of lung adenocarcinoma（ P<0.05） .A combined prediction equation was constructed as γ=0.739+0.067× smoking history assignment + （204号 0.332×GGN maximum diameter （cm） + 0.008 × average CT value （Hu）+0.013× maximum CT value （Hu）+0.034×GGN shape assignment + 0. 0.36× spicule sign assignment + 0.032× lobulation sign assignment.The AUC of the model for prediction was O.991，with sensitivity of 96.43% （204 and specificity of 94.63% .Conclusion Multi-dimensional prediction model of HRCT imaging features combined with baseline clinical features can enhance the predictive eficiency of non-invasive identification of GGN lung adenocarcinoma invasion.

【Key words】 Lung adenocarcinoma;Ground glass nodule; High resolution CT; Invasive adenocarcinoma;Prediction model

DOI：10.19522/j.cnki.1671-5098.2025.11.008

近年来，随着低剂量螺旋X射线断层成像（computedto-mography，CT）筛查及高分辨CT（high-resolution computed to-mography，HRCT）技术在胸部疾病筛查中的普及应用，肺结节检出率明显提高，磨玻璃结节（ground-glassnodule，GGN）型肺腺癌的检出率也随之提升。（剩余7422字）