Prediction of Distant Metastases After Stereotactic Body Radiation Therapy for Early Stage NSCLC: Development and External Validation of a Multi-Institutional Model

Sarah J. Gao, Lan Jin, Hugh W. Meadows, Timothy D. Shafman, Cary P. Gross, James B. Yu, Hugo J.W.L. Aerts, Joseph A. Miccio, John M. Stahl, Raymond H. Mak, Roy H. Decker, Benjamin H. Kann

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Introduction: Distant metastases (DMs) are the primary driver of mortality for patients with early stage 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 used a multi-institutional database of 1280 patients with cT1-3N0M0 NSCLC treated with SBRT from 2006 to 2015 for model development and internal validation. A Fine and Gray (FG) regression model was built to predict 1-year DM risk and compared with a random survival forests model. The higher performing model was evaluated on an external data set 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% confidence interval [CI]: 0.57–0.86) compared with the AUC of random survival forest at 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 of 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 of 15] versus 2.9% [7 of 241], p = 0.001) and external validation (21.4% [3 of 15] versus 7.8% [9 of 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 patients with NSCLC receiving SBRT which may help select patients for systemic therapy.

Original languageEnglish (US)
Pages (from-to)339-349
Number of pages11
JournalJournal of Thoracic Oncology
Volume18
Issue number3
DOIs
StatePublished - Mar 2023

All Science Journal Classification (ASJC) codes

  • Oncology
  • Pulmonary and Respiratory Medicine

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