Deep learning model for predicting spread through air spaces of lung adenocarcinoma based on transfer learning mechanism

Spread through air space (STAS) is a novel invasive pattern of lung adenocarcinoma (LUAD) associated with poor prognosis. Preoperative predicting of STAS helps choose an appropriate surgical and therapeutic strategy. This study aimed to develop and validate an STAS prediction model in LUAD based on deep learning algorithms. A dataset of 290 patients with preoperative chest computed tomography (CT) images and confirmed STAS status was retrospectively selected. Optimal semantic features were selected by logistic regression. Image features were learned from cubic patches containing lung tumors and the area around the tumor within 5/10/15 mm extracted from CT scans. ResNet50 architecture was used to train deep learning models based on the transfer learning mechanism. The optimal semantic features are combined with the deep learning model to construct a hybrid model. Receiver operating characteristic (ROC) curves were used to evaluate the performance. Patients were randomly partitioned into a training set (70%, n=203) and a test set (30%, n=87). The International Association for the Study of Lung Cancer (IASLC) grade, maximum tumor diameter, tumor density, spiculated sign, vacuole sign, and peritumor obstructive inflammation were incorporated into the hybrid model as independent predictors. The STAS-HYBRIDt10 proved to be the optimal STAS prediction model with an area under the curve (AUC) value of 0.874 in the training set and 0.801 in the test set. The sensitivity, specificity, and accuracy of STAS-HYBRIDt10 were 0.659/0.526, 0.904/0.837, and 0.798/0.701 in the training set and test set, respectively. The STAS-HYBRIDt10 has great potential for the preoperative prediction of STAS and may support decision-making for surgical and therapeutic planning in LUAD.