CT-based radiomics and cluster analysis for the prediction of local progression in stage I NSCLC patients treated with microwave ablation.

To predict local progression after microwave ablation (MWA) in patients with stage I non-small cell lung cancer (NSCLC), we developed a CT-based radiomics model. Postoperative CT images were used. The intraclass correlation coefficients, two-sample t-test, least absolute shrinkage and selection operator (LASSO) regression, and Pearson correlation analysis were applied to select radiomics features and establish radiomics score. The Radiomics score was used to classify patients into new radiomics labels. The k-means cluster algorithm was employed to cluster patients into new cluster labels based on radiomics features. Logistic regression was used to build prediction models. The optimal model incorporating clinical risk factors, radiomics labels, and cluster labels achieved the best discrimination. This study proposes a radiomics model that accurately predicts local progression in patients with stage I NSCLC treated with MWA. This prediction tool may be helpful in determining MWA efficacy and individualized risk classification and treatment.