A clinical nomogram and recursive partitioning analysis to determine the risk of regional failure after radiosurgery alone for brain metastases

George Rodrigues*, Andrew Warner, Jaap Zindler, Ben Slotman, Frank Lagerwaard

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

24 Citations (Web of Science)

Abstract

This investigation defined patient populations at high-, intermediate-, and low-risk of regional failure (RF) after stereotactic radiosurgery (SRS) lesion treatment using clinical nomograms and recursive partitioning analysis (RPA).We created a retrospective database compiling 361 oligometastatic brain metastases patients treated with single-modality Linac-based SRS. Logistic analysis was performed to identify factors to be included in a RPA to predict for cumulative RF at 1-year. A 1-year cumulative RF clinical nomogram was constructed and validated (c-index statistic).Age, number of brain metastases, World Health Organization (WHO) performance status (PS), and maximum gross tumor volume (GTV) size were found to be statistically significant predictors of the primary outcome. RPA classifications were defined as follows: low-risk (55Y; intermediate-risk (25-40% 1-year RF): age ?55Y AND solitary lesion OR WHO?1 AND 2-3 lesions; and high-risk (>40% 1-year RF): WHO PS=0 AND 2-3 lesions. These classifications were highly statistically significant (p
Original languageEnglish
Pages (from-to)52-58
JournalRadiotherapy and Oncology
Volume111
Issue number1
DOIs
Publication statusPublished - Apr 2014

Keywords

  • Brain metastases
  • Stereotactic radiosurgery
  • Recursive partitioning analysis
  • Predictive modeling
  • Regional failure/control

Cite this