Mathematical Prediction Model Of Computed Tomography Signs Is Superior To Intraoperative Frozen Section In The Diagnosis Of Ground-Glass Nodular Invasive Adenocarcinoma Of The Lung

Background At present, lobectomy is still the standard treatment for lung cancer. Judging whether a lesion is invasive adenocarcinoma (IA) has important guiding significance for determining the scope of surgical resection. The commonly used methods are intraoperative frozen sections and computed tomography (CT) signs. There is still controversy about the accuracy of both in judging the invasiveness of ground-glass nodules (GGNs). Methods The clinical data of patients with GGNs who underwent surgery were collected. According to the results of univariate analysis, the variables with statistical differences were selected and included in logistic regression multivariate analysis. The predictive variables were determined and the receiver operating characteristic (ROC) curve was drawn in order to achieve the area under the curve (AUC) value. Results According to the results of logistic regression analysis, the longest diameter and maximum CT value of nodules were independent risk factors for IA. The mathematical prediction model of CT signs was determined, and the ROC curves of CT signs and intraoperative frozen sections (FS) were drawn, respectively. The AUC values under the curves were calculated to be 0.873 and 0.807, respectively. The mathematical prediction model of intraoperative frozen section combined with CT signs was established. A ROC curve was drawn and the AUC was calculated to be 0.925. Conclusions The diagnostic accuracy of CT signs in judging whether nonbenign GGNs were IA was higher than that of intraoperative FS. Combined with CT signs and intraoperative FS to establish a mathematical prediction model, the diagnostic accuracy of judging whether nonbenign GGNs are IA is significantly improved.