Preoperative CT radiomic model combined with clinical and CT imaging features to predict the spread through air spaces in T1 invasive lung adenocarcinoma.

Abstract

Purpose: This study aimed to explore the effectiveness of preoperative computed tomography (CT) radiomic models combined with clinical and CT imaging features for predicting spread through air spaces (STAS) in patients with T1 lung adenocarcinoma.

Methods: The preoperative CT and clinical data of 219 patients with T1 invasive lung adenocarcinoma confirmed by surgery were retrospectively analyzed and randomly divided into training and test sets at a ratio of 7:3. Univariable and multivariable logistic analyses were performed on the clinical and CT manifestations to screen independent predictive factors for STAS (+), and a clinical model was constructed. Radiomic features were extracted from the tumor (T), peritumoral (P) and tumor-peritumoral (TP) regions to construct radiomic models (Model T, Model P and Model TP), and the optimal radiomic model was identified. A combined model was then built on the basis of the best radiomic score (Radscore) and clinically independent predictors. For each model, the effectiveness in predicting STAS (+) was assessed with receiver operating characteristic (ROC) curve analysis, including calculation of the area under the curve (AUC), and a nomogram was created. Calibration curve analysis was used to assess model calibration, and decision curve analysis (DCA) was used to evaluate the clinical value of the model.

Results: Emphysema, the preoperative carcinoembryonic antigen (CEA) level, and the consolidation tumor ratio (CTR) were identified as independent predictors of STAS (+) (all P < 0.01). Model T was considered the optimal radiomic model. In the training set, the AUC of the combined model was greater than that of the clinical model (0.93 vs. 0.85, P < 0.01). However, no significant difference in the AUC was found between the combined model and Model T (0.93 vs. 0.92, P > 0.05). In the test set, the AUC of the combined model was greater than that of the clinical model (0.92 vs. 0.85, P < 0.05), but there was no significant difference compared to the AUC of Model T (0.92 vs. 0.90, P = 0.13). The AUC of Model T was greater than that of the clinical model in the training set (0.92 vs. 0.85, P < 0.01), but this difference was not significant in the test set (0.90 vs. 0.85, P = 0.35). The clinical model, radiomic Model T, and combined model all had high degrees of calibration. Finally, the clinical net benefit of the combined model was greater than that of the other two models with the threshold ranged from 0.10 to 0.40.

Conclusion: The preoperative CT radiomics model combined with clinical and CT imaging features can effectively predict STAS in T1 invasive lung adenocarcinoma patients.