Prediction of Distant Metastases After Stereotactic Body Radiation Therapy for Early Stage NSCLC Development and External Validation of a Multi-Institutional Model: development and external validation of a multi-institutional model

OBJECTIVES: Distant metastases (DM) are the primary driver of mortality for patients with early stage non-small cell lung cancer (NSCLC) receiving stereotactic body radiation therapy (SBRT), yet patient-level risk is difficult to predict. We developed and validated a model to predict individualized risk of DM in this population.

METHODS: We utilized a multi-institutional database of 1,280 patients with cT1-3N0M0 NSCLC treated with SBRT from 2006-2015 for model development and internal validation. A Fine and Gray (FG) regression model was built to predict 1-year DM risk and compared to a random survival forests (RSF) model. The higher performing model was evaluated on an external dataset of 130 patients from a separate institution. Discriminatory performance was evaluated using the time-dependent area under the curve (AUC). Calibration was assessed graphically and with Brier scores.

RESULTS: The FG model yielded an AUC of 0.71 (95% CI: 0.57-0.86) compared to the RSF's AUC of 0.69 [95% CI: 0.63-0.85] in the internal test set and was selected for further testing. On external validation, the FG model yielded an AUC 0.70 (95% CI: 0.57-0.83) with good calibration (Brier score 0.08). The model identified a high-risk patient subgroup with greater 1-year DM rates in the internal test (20.0% (3/15) v 2.9% (7/241), P=0.001) and external validation 21.4% (3/15) v 7.8% (9/116), P= 0.095). A model nomogram and online application was made available.

CONCLUSIONS: We developed and externally validated a practical model that predicts DM risk in NSCLC patients receiving SBRT that may help select patients for systemic therapy.