Development of a machine learning-based model to predict prognosis of resected invasive pulmonary adenocarcinoma

Invasive pulmonary adenocarcinoma (IPA) poses a significant threat to global health and patients still experience tumor recurrence and metastasis. This study aimed to construct an optimized prognosis model using machine learning to predict the disease-free survival (DFS) of IPA patients.

A total of 670 resected IPA patients from 2015 to 2020 were enrolled. Clinicopathological information was collected and the outcomes of patients were followed up. Patients were divided into a training set and a test set at a ratio of 4:1. Four machine learning models were compared to build the DFS models and 5-fold cross validation was performed. The area under the receiver operating characteristic curve (AUC), C-index, calibration curves, and decision curve analysis (DCA) were used to evaluate the model.

Among the four models, the least absolute shrinkage and selection operator (Lasso) model showed the best performance in predicting DFS at 2-year (training set: AUC =0.906, test set: AUC =0.862), at 3-year (training set: AUC =0.894, test set: AUC =0.879), at 4-year (training set: AUC =0.901, test set: AUC =0.902), and at 5-year (training set: AUC =0.927, test set: AUC =0.887). The calibration curves and DCA exhibited a good predictive performance.

Our study successfully constructed a machine-learning based prognostic model to predict DFS, which may provide oncologists with an effective tool for early medical intervention and survival improvement.