A nomogram for individualized estimation of survival among patients with brain metastasis

Jill S. Barnholtz-Sloan, Changhong Yu, Andrew E. Sloan, Jaime Vengoechea, Meihua Wang, James J. Dignam, Michael A. Vogelbaum, Paul W. Sperduto, Minesh P. Mehta, Mitchell MacHtay, Michael W. Kattan

Research output: Contribution to journalArticlepeer-review

93 Scopus citations

Abstract

Purpose: An estimated 2445 of patients with cancer develop brain metastases. Individualized estimation of survival for patients with brain metastasis could be useful for counseling patients on clinical outcomes and prognosis. Methods: De-identified data for 2367 patients with brain metastasis from 7 Radiation Therapy Oncology Group randomized trials were used to develop and internally validate a prognostic nomogram for estimation of survival among patients with brain metastasis. The prognostic accuracy for survival from 3 statistical approaches (Cox proportional hazards regression, recursive partitioning analysis [RPA], and random survival forests) was calculated using the concordance index. A nomogram for 12-month, 6-month, and median survival was generated using the most parsimonious model. Results: The majority of patients had lung cancer, controlled primary disease, no surgery, Karnofsky performance score (KPS) < 70, and multiple brain metastases and were in RPA class II or had a Diagnosis-Specific Graded Prognostic Assessment (DS-GPA) score of 1.252.5. The overall median survival was 136 days (95 confidence interval, 126144 days). We built the nomogram using the model that included primary site and histology, status of primary disease, metastatic spread, age, KPS, and number of brain lesions. The potential use of individualized survival estimation is demonstrated by showing the heterogeneous distribution of the individual 12-month survival in each RPA class or DS-GPA score group. Conclusion: Our nomogram provides individualized estimates of survival, compared with current RPA and DS-GPA group estimates. This tool could be useful for counseling patients with respect to clinical outcomes and prognosis.

Original languageEnglish (US)
Pages (from-to)910-918
Number of pages9
JournalNeuro-oncology
Volume14
Issue number7
DOIs
StatePublished - Jul 2012

All Science Journal Classification (ASJC) codes

  • Oncology
  • Clinical Neurology
  • Cancer Research

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