title: Extent of resection of glioblastoma revisited: personalized survival modeling facilitates more accurate survival prediction and supports a maximum-safe-resection approach to surgery.
abstract: OBJECTIVE
  Approximately 12,000 glioblastomas are diagnosed annually in the United States. The median survival rate for this disease is 12 months, but individual survival rates can vary with patient-specific factors, including extent of surgical resection (EOR). The goal of our investigation is to develop a reliable strategy for personalized survival prediction and for quantifying the relationship between survival, EOR, and adjuvant chemoradiotherapy.
  
  
  METHODS
  We used accelerated failure time (AFT) modeling using data from 721 newly diagnosed patients with glioblastoma (from 1993 to 2010) to model the factors affecting individualized survival after surgical resection, and we used the model to construct probabilistic, patient-specific tools for survival prediction. We validated this model with independent data from 109 patients from a second institution.
  
  
  RESULTS
  AFT modeling using age, Karnofsky performance score, EOR, and adjuvant chemoradiotherapy produced a continuous, nonlinear, multivariable survival model for glioblastoma. The median personalized predictive error was 4.37 months, representing a more than 20% improvement over current methods. Subsequent model-based calculations yield patient-specific predictions of the incremental effects of EOR and adjuvant therapy on survival.
  
  
  CONCLUSIONS
  Nonlinear, multivariable AFT modeling outperforms current methods for estimating individual survival after glioblastoma resection. The model produces personalized survival curves and quantifies the relationship between variables modulating patient-specific survival. This approach provides comprehensive, personalized, probabilistic, and clinically relevant information regarding the anticipated course of disease, the overall prognosis, and the patient-specific influence of EOR and adjuvant chemoradiotherapy. The continuous, nonlinear relationship identified between expected median survival and EOR argues against a surgical management strategy based on rigid EOR thresholds and instead provides the first explicit evidence supporting a maximum safe resection approach to glioblastoma surgery.
authors: Nicholas F. Marko, Frederick F. Lang, Dima Suki, and Raymond E. Sawaya, The University of Texas MD Anderson Cancer Center, Houston, TX; Robert J. Weil and Jason L. Schroeder, Cleveland Clinic, Cleveland, OH.
authors: Nicholas F
authors: NF
authors: Marko
authors: Robert J
authors: RJ
authors: Weil
authors: Jason L
authors: JL
authors: Schroeder
authors: Frederick F
authors: FF
authors: Lang
authors: Dima
authors: D
authors: Suki
authors: Raymond E
authors: RE
authors: Sawaya
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001932:Brain Neoplasms / Q000401:mortality / Q000473:pathology / Q000601:surgery*; D059186:Chemoradiotherapy, Adjuvant; D003661:Decision Support Techniques; D005260:Female; D005909:Glioblastoma / Q000401:mortality / Q000473:pathology / Q000601:surgery*; D006801:Humans; D017567:Karnofsky Performance Status; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D018365:Neoplasm, Residual; D017711:Nonlinear Dynamics; D009820:Ohio; D018579:Patient Selection; D057285:Precision Medicine; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D012307:Risk Factors; D016019:Survival Analysis; D013781:Texas; D013997:Time Factors; D016896:Treatment Outcome; D055815:Young Adult