Machine Learning Based on Clinical Information and Integrated CT Radiomics to Predict Local Recurrence of Stage Ia Lung Adenocarcinoma after Microwave Ablation.

PURPOSE

To develop and compare 3 different machine learning-based models of clinical information and integrated radiomics features predicting the local recurrence of Stage Ia lung adenocarcinoma after microwave ablation (MWA) for assisting clinical decision making.

MATERIALS AND METHODS

The data of 360 patients with Stage Ia lung adenocarcinoma who underwent MWA were included in the training (n = 208), internal test (n = 90), and external test (n = 62) sets based on the inclusion and exclusion criteria. The predictors associated with local recurrence were identified using univariate and multivariate analyses of clinical information. The integrated radiomics features were extracted from pre-MWA and post-MWA (scanned immediately after the ablation) computed tomography (CT) images, and 10 radiomics features were selected by the t-test and least absolute shrinkage and selection operator. The L2-logistic regression of machine learning was applied for the clinical model, CT radiomics model, and combined model including clinical predictors and radiomics features. Model performance was evaluated by the receiver operating characteristic and decision curve analysis.

RESULTS

The ablative margin was an independent clinical predictor (P = 0.001; odds ratio [OR], 0.46; 95% CI, 0.29-0.73). The combined model showed the highest area under the curve value among the 3 models (training, 0.86; 95% CI, 0.81-0.91; internal test, 0.93; 95% CI, 0.87-0.98; external test, 0.89; 95% CI, 0.79-0.96).

CONCLUSIONS

The combined model could accurately predict the local recurrence of Stage Ia lung adenocarcinoma after MWA to better support a clinical decision.