Integration of Risk Survival Measures Estimated From Pre- and Posttreatment Computed Tomography Scans Improves Stratification of Patients With Early-Stage Non-small Cell Lung Cancer Treated With Stereotactic Body Radiation Therapy

Int J Radiat Oncol Biol Phys. 2021 Apr 1;109(5):1647-1656. doi: 10.1016/j.ijrobp.2020.12.014. Epub 2021 Jan 19.

Abstract

Purpose: To predict overall survival of patients receiving stereotactic body radiation therapy (SBRT) for early-stage non-small cell lung cancer (ES-NSCLC), we developed a radiomic model that integrates risk of death estimates and changes based on pre- and posttreatment computed tomography (CT) scans. We hypothesize this innovation will improve our ability to stratify patients into various oncologic outcomes with greater accuracy.

Methods and materials: Two cohorts of patients with ES-NSCLC uniformly treated with SBRT (a median dose of 50 Gy in 4-5 fractions) were studied. Prediction models were built on a discovery cohort of 100 patients with treatment planning CT scans, and then were applied to a separate validation cohort of 60 patients with pre- and posttreatment CT scans for evaluating their performance.

Results: Prediction models achieved a c-index up to 0.734 in predicting survival outcomes of the validation cohort. The integration of the pretreatment risk of survival measures (risk-high vs risk-low) and changes (risk-increase vs risk-decrease) in risk of survival measures between the pretreatment and posttreatment scans further stratified the patients into 4 subgroups (risk: high, increase; risk: high, decrease; risk: low, increase; risk: low, decrease) with significant difference (χ2 = 18.549, P = .0003, log-rank test). There was also a significant difference between the risk-increase and risk-decrease groups (χ2 = 6.80, P = .0091, log-rank test). In addition, a significant difference (χ2 = 7.493, P = .0062, log-rank test) was observed between the risk-high and risk-low groups obtained based on the pretreatment risk of survival measures.

Conclusion: The integration of risk of survival measures estimated from pre- and posttreatment CT scans can help differentiate patients with good expected survival from those who will do more poorly following SBRT. The analysis of these radiomics-based longitudinal risk measures may help identify patients with early-stage NSCLC who will benefit from adjuvant treatment after lung SBRT, such as immunotherapy.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Aged
  • Carcinoma, Non-Small-Cell Lung / diagnostic imaging*
  • Carcinoma, Non-Small-Cell Lung / mortality*
  • Carcinoma, Non-Small-Cell Lung / pathology
  • Carcinoma, Non-Small-Cell Lung / radiotherapy
  • Cohort Studies
  • Dose Fractionation, Radiation
  • Female
  • Forecasting / methods
  • Humans
  • Kaplan-Meier Estimate
  • Lung Neoplasms / diagnostic imaging*
  • Lung Neoplasms / mortality*
  • Lung Neoplasms / pathology
  • Lung Neoplasms / radiotherapy
  • Male
  • Models, Theoretical
  • Postoperative Care
  • Preoperative Care
  • Prognosis
  • Radiosurgery / methods*
  • Radiosurgery / mortality
  • Tomography, X-Ray Computed*
  • Treatment Outcome