Development and application of an elastic net logistic regression model to investigate the impact of cardiac substructure dose on radiation-induced pericardial effusion in patients with NSCLC

Abstract

BACKGROUND: Typically, cardiac substructures are neither delineated nor analyzed during radiation treatment planning. Therefore, we developed a novel machine learning model to evaluate the impact of cardiac substructure dose for predicting radiation-induced pericardial effusion (PCE). MATERIALS AND METHODS: = 100 iterations, 75%/25% training/test set data split), was conducted with cardiac-based DVH metrics as covariates. The following model types were constructed and analyzed: (i) standard model type, which only included whole-heart DVH metrics; and (ii) a model type trained with both whole-heart and substructure DVH metrics. Model performance was analyzed on the test set using area under the curve (AUC), accuracy, calibration slope and calibration intercept. A final fitted model, based on the optimal model type, was developed from the entire study population for future comparisons. RESULTS: = 70). Models using whole heart and substructure dose had the highest performance (median values: AUC = 0.820; calibration slope/intercept = 1.356/-0.235; accuracy = 0.743) and outperformed the standard whole-heart only model type (median values: AUC = 0.799; calibration slope/intercept = 2.456/-0.729; accuracy = 0.713). The final fitted elastic net model showed high performance in predicting PCE (median values: AUC = 0.879; calibration slope/intercept = 1.352/-0.174; accuracy = 0.801). CONCLUSIONS: We developed and evaluated elastic net regression toxicity models of radiation-induced PCE. We found the model type that included cardiac substructure dose had superior predictive performance. A final toxicity model that included cardiac substructure dose metrics was developed and reported for comparison with external datasets.

Topics & Concepts