Deep Learning Model to Predict the Recurrence of Stage IA Invasive Lung Adenocarcinoma After Sub-lobar Resection

This study aims to develop a deep learning model based on noncontrast CT images to predict the recurrence risk of stage IA invasive lung adenocarcinoma after sub-lobar resection，which can serve as potential tool to assist thoracic surgeons in making optimal treatment decisions.The study will use existing CT data to train and validate the model, without requiring any additional intervention for the participants.

This study is designed to develop a deep learning model to predict the recurrence risk of stage IA invasive lung adenocarcinoma after sub-lobar resection using noncontrast CT images. The best indications for sub-lobar resection in patients with early-stage LADC are still debated, making surgical method selection somewhat difficult. The deep learning model can noninvasively and objectively predict the recurrence risk of patients with stage IA ILADC following sub-lobectomy and are helpful in predicting prognosis of patients with stage IA ILADC after sub-lobectomy and can facilitate the choosing of the optimal surgery mode of these patients. The study will utilize retrospective data from patients with stage IA invasive lung adenocarcinoma after sub-lobar resection . Noncontrast CT images will be collected at admission and used as inputs for the deep learning model. The model will be trained using convolutional neural networks (CNN) to identify patterns associated with recurrence. In addition to model development, the study will also evaluate the model's performance on a separate validation cohort to assess generalizability. Statistical analyses will include performance metrics such as area under the receiver operating characteristic (ROC) curve (AUC) and precision-recall curve. This study aims to provide a valuable tool for clinicians to make timely decisions in choosing the optimal therapeutic approach.