Predicting spread through air space of lung adenocarcinoma based on deep learning and machine learning models.

Objective: The aim of this study was to develop a machine learning model that can predict spread through air space (STAS) of lung adenocarcinoma preoperatively. STAS is associated with poor prognosis in invasive lung adenocarcinoma. Therefore non-invasive and accurate pre-surgical prediction of STAS in patients with lung adenocarcinoma is essential for individualised patient management.

Methods: We included 138 patients with invasive lung adenocarcinoma who underwent lobectomy, collected their preoperative imaging data and clinical features, built a model for predicting STAS using machine learning and deep learning methods, and validated the efficacy of the model. Finally a nomogram was created based on logistic regression (LR).

Results: Imaging histology features showed good model efficacy in both the training set (LR AUC = 0.764) and the test set (LR AUC = 0.776), and we combined the imaging histology and clinical features to jointly build a nomogram graph (AUC = 0.878), extracted the deep learning features, and built a machine learning model based on the ResNET50 algorithm, where the LR AUC = 0.918.

Conclusions: This presented radiomics model can be served as a non-invasive for predicting STAS in Infiltrating lung adenocarcinoma.