Preoperative Recurrence Risk Stratification in Pleomorphic Xanthoastrocytoma Using VASARI MRI Features: A Dual-Center Cohort Study

BACKGROUND: Pleomorphic xanthoastrocytoma (PXA) carries highly variable recurrence risk, yet reliable preoperative stratification tools are lacking. We developed a recurrence prediction model using Visually Accessible Rembrandt Images (VASARI) MRI features in the largest standardized PXA imaging cohort reported to date. MATERIALS AND METHODS: This retrospective dual-center study included an imaging cohort of 155 patients with histopathologically confirmed PXA. Of these, 116 patients with available follow-up and complete data for the variables required for survival modeling comprised the survival cohort. Twenty-seven VASARI features were independently assessed by two radiologists. Univariate and multivariable Cox proportional hazards regression identified independent recurrence predictors. Model discrimination was quantified by Harrell’s concordance index (C-index) with bootstrap internal validation (1000 iterations). Calibration was explored using tertile-based calibration plots at 24 and 36 months and the integrated Brier score. RESULTS: Tumor recurrence occurred in 27 of 116 patients (23.3%) in the survival cohort. Three imaging features were independently associated with recurrence: diffusion restriction (HR=5.10; 95% CI, 2.143-12.148; P < .001), midline crossing (HR=3.82; 95% CI, 1.662-8.769; P = .002), and mixed cystic-solid non-classic morphology (HR=3.42; 95% CI, 1.353-8.625; P = .009). The model achieved good discrimination (apparent C-index=0.834; optimism-corrected C-index=0.79 [95% CI, 0.70-0.86]). Exploratory calibration showed approximate agreement between predicted and observed outcomes, although estimates were imprecise because of the limited number of events. The integrated Brier score was 0.126 compared with 0.179 for the null model. CONCLUSIONS: A three-feature VASARI-based model showed potential for preoperative recurrence risk stratification in PXA without specialized software or postprocessing. These findings are hypothesis-generating and require independent external validation before clinical application.