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Time-dependent prognostic value of automated Ki67 assessment and its integration with molecular risk profiling in WHO grade 2 meningioma
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DOI：10.1186/s40478-026-02387-8.png)
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Abstract
En 中文
WHO grade 2 meningiomas exhibit highly heterogeneous clinical courses. While the Ki67 proliferation index is a standard biomarker, its prognostic utility remains limited by methodological inconsistency and potential time-dependent dynamics. We evaluated an automated, artifact-adjusted Ki67 assessment and its integration with molecular risk profiling. 98 WHO grade 2 meningiomas (WHO 2021) were analyzed using an automated QuPath-based pipeline with HistoART for artifact exclusion. Molecular risk was defined by methylation and copy number profiling to calculate the integrated molecular-morphologic risk score by Maas et al. We employed extended Cox models to account for proportional hazards violations. Automated Ki67 values were significantly lower than routine pathological estimates (median 2.91% vs. 10%; p < 0.001) and correlated modestly with integrated risk scores (ρ = 0.26, p = 0.009). We identified a biphasic risk pattern: within the first 38 postoperative months, an automated Ki67 > 3.62% was a strong independent predictor for local recurrence (HR 5.06, p < 0.001) and progression-free survival (HR 4.15, p = 0.002), remaining significant alongside subtotal resection and the integrated risk group. Beyond 38 months, prognostic impact attenuated. Ki67 and the integrated molecular risk score contributed independently in multivariable models, suggesting complementary biological dimensions. Automated, artifact-adjusted Ki67 quantification provides time-dependent, independent prognostic information in WHO grade 2 meningioma, complementary to molecular risk stratification. It may serve as a cost-effective surveillance marker—both as an adjunct to molecular profiling and as a standalone tool where molecular testing is unavailable.
Keywords:
Meningioma
Ki67
Digital pathology
QuPath
DNA methylation
Integrated risk score
WHO grade 2
Foundation model approach
Deep learning
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