Variation in meningioma recurrence risk estimates across observational cohorts: the influence of calendar time, WHO classifications, geographical settings, and healthcare systems - Christian Mirian; Lasse Rehné Jensen; Adam Gorm Hoffmann; Tareq A. Juratli; Anders Broechner; Sverre H. Torp; Helen A. Shih; Ramin A. Morshed; Jacob S. Young; Stephen T. Magill; Luca Bertero; Walter Stummer; et al. (Journal of Neuro-Oncology, 2026)
Abstract Purpose Recurrence risk estimates underpin meningioma research, including molecular classification and clinical trial benchmarking, yet are often based on retrospective or historical data. The aim of this study was to assess the variation of recurrence risk estimates across calendar periods, WHO classification editions, geographical settings, and healthcare systems.thetermine Methods We analyzed 4,111 patients with primary WHO-1/-2 meningiomas from 31 centers in 15 countries (1990–2019). Recurrence was defined according to local radiological assessment. The 5- and 10-year recurrence risks were estimated using regression standardization with inverse probability of censoring weights, adjusting for key clinical, surgical, and histopathological variables. Results Recurrence risk estimates varied across all domains examined. More recent calendar periods were associated with higher predicted recurrence risk, particularly for WHO-2 at 5 years (e.g., ≥ 2013 vs. ≤ 2007: RR 1.60, 95% CI 1.19–2.01). Differences were also observed across healthcare systems and geographical settings despite adjustment. In contrast, recurrence risk estimates were broadly comparable between WHO classification editions (2007 vs. 2016). Cohort-level visualization revealed substantial heterogeneity in follow-up duration, inclusion patterns, and recurrence detection. Conclusions Recurrence risk estimates are not stable across time or settings and are influenced by several patterns, particularly data accrual patterns. Recurrence events are inherently “locked” to the historical context in which they were detected, which may have important implications for developing molecular classifiers using retrospective specimens and clinical trials relying on benchmark established in historical settings. We propose transparent visualization of observational cohort composition and individual-level event timing to improve interpretability and comparability.