Preoperative prognostic classification system for hemispheric low-grade gliomas in adults

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Abstract
Object: Hemispheric low-grade gliomas (LGGs) have an unpredictable progression and overall survival (OS) profile. As a result, the objective in the present study was to design a preoperative scoring system to prognosticate long-term outcomes in patients with LGGs. Methods: The authors conducted a retrospective review with long-term follow-up of 281 adults harboring hemispheric LGGs (World Health Organization Grade II lesions). Clinical and radiographic data were collected and analyzed to identify preoperative predictors of OS, progression-free survival (PFS), and extent of resection (EOR). These variables were used to devise a prognostic scoring system. Results: The 5-year estimated survival probability was 0.86. Multivariate Cox proportional hazards modeling demonstrated that 4 factors were associated with lower OS: presumed eloquent location (hazard ratio [HR] 4.12, 95% confidence interval [CI] 1.71–10.42), Karnofsky Performance Scale score ≤ 80 (HR 3.53, 95% CI 1.56–8.00), patient age > 50 years (HR 1.96, 95% CI 1.47–3.77), and tumor diameter > 4 cm (HR 3.43, 95% CI 1.43–8.06). A scoring system calculated from the sum of these factors (range 0–4) demonstrated risk stratification across study groups, with the following 5-year cumulative survival estimates: Scores 0–1, OS = 0.97, PFS = 0.76; Score 2, OS = 0.81, PFS = 0.49; and Scores 3–4, OS = 0.56, PFS = 0.18 (p < 0.001 for both OS and PFS, log-rank test). This proposed scoring system demonstrated a high degree of interscorer reliability (kappa = 0.86). Four illustrative cases are described. Conclusions: The authors propose a simple and reliable scoring system that can be used to preoperatively prognosticate the degree of lesion resectability, PFS, and OS in patients with LGGs. The application of a standardized scoring system for LGGs should improve clinical decision-making and allow physicians to reliably predict patient outcome at the time of the original imaging-based diagnosis.