Application of an interpretable artificial neural network for 3-year local recurrence prediction in lung cancer patients treated with stereotactic body radiation therapy

Background Accurate prediction of local recurrence (LR) after stereotactic body radiation therapy (SBRT) remains challenging in patients with lung cancer. The aim of the present study was to develop an artificial neural network (ANN)-based prediction model for predicting 3-year LR following SBRT. Materials and methods A total of 127 lung tumors treated with SBRT were retrospectively analyzed. Predictors selected by multivariable logistic regression using the Akaike information criterion were used to construct Model 1. Model 2 was developed by additionally incorporating the maximum biologically effective dose to the planning target volume (BEDPTV max ) and histology into Model 1. Both models were developed using an ANN with internal bootstrap validation. Model discrimination, calibration, and clinical utility were assessed using receiver operating characteristic analysis, calibration plots, and decision curve analysis. The predictive performance of the ANN models was compared with that of a conventional logistic regression and three radiobiological models using the same dataset. Results Model 2 demonstrated significantly improved discrimination than Model 1, with AUCs of 0.892 (95% CI, 0.838-0.944) and 0.791 (95% CI, 0.712-0.856), respectively ( P < 0.05). Both ANN models showed a general agreement between predicted and observed probabilities and provided net benefit across clinically relevant threshold probabilities. Model 2 also demonstrated higher AUC than the conventional logistic regression and three radiobiological models. Conclusion ANN-based models have potential to predict 3-year LR in patients with lung cancer treated with SBRT and outperformed the conventional logistic regression and radiobiological models. External validation in independent cohorts is warranted.