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: One-hundred and forty-one stage III NSCLC patients, who received radiation therapy in a prospective clinical trial, were included in this analysis. The impact of dose-volume histogram (DVH) metrics (mean and max dose, V5Gy[%]-V70Gy[%]) for the whole heart, left and right atrium, and left and right ventricle, on pericardial effusion toxicity (≥grade 2, CTCAE v4.0 grading) were examined. Elastic net logistic regression, using repeat cross-validation (n = 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: Grade 2 PCE incidence was 49.6% (n = 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.