BACKGROUND: To determine the benefit of surgical management in recurrent glioblastoma, we analyzed a series of patients with recurrent glioblastoma who had undergone surgery, and we devised a new scale to predict their survival. METHODS: Clinical data from 55 consecutive patients with recurrent glioblastoma were evaluated after surgical management. Kaplan-Meier survival analysis and Cox proportional hazards regression modeling were used to identify prognostic variables for the development of a predictive scale. After the multivariate analysis, performance status (P = .078) and ependymal involvement (P = .025) were selected for inclusion in the new prognostic scale. The devised scale was validated with a separate set of 96 patients from 3 different institutes. RESULTS: A 3-tier scale (scoring range, 0-2 points) composed of additive scores for the Karnofsky performance status (KPS) (0 for KPS ≥ 70 and 1 for KPS < 70) and ependymal involvement (0 for no enhancement and 1 for enhancement of the ventricle wall in the magnetic resonance imaging) significantly distinguished groups with good (0 points; median survival, 18.0 months), intermediate (1 point; median survival, 10.0 months), and poor prognoses (2 points; median survival, 4.0 months). The new scale was successfully applied to the validation cohort of patients showing distinct prognosis among the groups (median survivals of 11.0, 9.0, and 4.0 months for the 0-, 1-, and 2-point groups, respectively). CONCLUSIONS: We developed a practical scale to facilitate deciding whether to proceed with surgical management in patients with recurrent glioblastoma. This scale was useful for the diagnosis of prognostic groups and can be used to develop guidelines for patient treatment.
BACKGROUND: To determine the benefit of surgical management in recurrent glioblastoma, we analyzed a series of patients with recurrent glioblastoma who had undergone surgery, and we devised a new scale to predict their survival. METHODS: Clinical data from 55 consecutive patients with recurrent glioblastoma were evaluated after surgical management. Kaplan-Meier survival analysis and Cox proportional hazards regression modeling were used to identify prognostic variables for the development of a predictive scale. After the multivariate analysis, performance status (P = .078) and ependymal involvement (P = .025) were selected for inclusion in the new prognostic scale. The devised scale was validated with a separate set of 96 patients from 3 different institutes. RESULTS: A 3-tier scale (scoring range, 0-2 points) composed of additive scores for the Karnofsky performance status (KPS) (0 for KPS ≥ 70 and 1 for KPS < 70) and ependymal involvement (0 for no enhancement and 1 for enhancement of the ventricle wall in the magnetic resonance imaging) significantly distinguished groups with good (0 points; median survival, 18.0 months), intermediate (1 point; median survival, 10.0 months), and poor prognoses (2 points; median survival, 4.0 months). The new scale was successfully applied to the validation cohort of patients showing distinct prognosis among the groups (median survivals of 11.0, 9.0, and 4.0 months for the 0-, 1-, and 2-point groups, respectively). CONCLUSIONS: We developed a practical scale to facilitate deciding whether to proceed with surgical management in patients with recurrent glioblastoma. This scale was useful for the diagnosis of prognostic groups and can be used to develop guidelines for patient treatment.
Entities:
Keywords:
ependymal involvement; performance status; recurrent glioblastoma; scoring system; surgery
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