Integrating radiomics and deep learning for enhanced prediction of high-grade patterns in stage IA lung...

The presence of high-grade patterns (HGPs) often has a detrimental effect on prognosis. It is helpful to make individualized clinical treatment plans when preoperative recognition of the presence of HGPs becomes possible. So, this study aimed to develop a model based on preoperative computed tomography (CT) images to predict the presence of HPGs in invasive pulmonary non-mucinous adenocarcinoma.

A retrospective analysis was conducted on 403 surgically treated patients with clinical stage IA and pathologically confirmed invasive non-mucinous adenocarcinoma. There were 256 deep learning features and 1,836 handcrafted features extracted from the regions of interest (ROIs) in preoperative CT images. The optimal subset of features was screened using t-test, Pearson correlation analysis, and least absolute shrinkage and selection operator (LASSO) regression to construct the fusion model. Receiver operating characteristic (ROC) curve was applied to assess the model's performance. Decision curve analysis (DCA) and calibration curve were used to assess the clinical usefulness.

The fusion model combining radiomics features and deep learning features using the XGBoost classifier exhibited strong predictive efficacy with the area under the curve (AUC) of 0.983, 0.862, and 0.832 in the training, validation, and test set. It means that the model can distinguish well between tumors with and without HGPs. The fusion model had better diagnostic performance when compared to the radiomics model and deep learning model. Calibration curve indicated good coherence between model prediction and the actual observation. DCA revealed the fusion model exerted the highest clinical benefit.

The fusion model can identify the presence of HPGs in invasive lung adenocarcinoma from preoperative CT images. It assists clinicians in determining individualized treatments and monitoring strategies for patients.