PURPOSE Meningiomas are the most frequent primary intracranial tumors. Patient outcome varies widely from benign to highly aggressive, ultimately fatal courses. Reliable identification of risk of progression for individual patients is of pivotal importance. However, only biomarkers for highly aggressive tumors are established ( CDKN2A/B and TERT), whereas no molecularly based stratification exists for the broad spectrum of patients with low- and intermediate-risk meningioma. METHODS DNA methylation data and copy-number information were generated for 3,031 meningiomas (2,868 patients), and mutation data for 858 samples. DNA methylation subgroups, copy-number variations (CNVs), mutations, and WHO grading were analyzed. Prediction power for outcome was assessed in a retrospective cohort of 514 patients, validated on a retrospective cohort of 184, and on a prospective cohort of 287 multicenter cases. RESULTS Both CNV- and methylation family–based subgrouping independently resulted in increased prediction accuracy of risk of recurrence compared with the WHO classification (c-indexes WHO 2016, CNV, and methylation family 0.699, 0.706, and 0.721, respectively). Merging all risk stratification approaches into an integrated molecular-morphologic score resulted in further substantial increase in accuracy (c-index 0.744). This integrated score consistently provided superior accuracy in all three cohorts, significantly outperforming WHO grading (c-index difference P = .005). Besides the overall stratification advantage, the integrated score separates more precisely for risk of progression at the diagnostically challenging interface of WHO grade 1 and grade 2 tumors (hazard ratio 4.34 [2.48-7.57] and 3.34 [1.28-8.72] retrospective and prospective validation cohorts, respectively). CONCLUSION Merging these layers of histologic and molecular data into an integrated, three-tiered score significantly improves the precision in meningioma stratification. Implementation into diagnostic routine informs clinical decision making for patients with meningioma on the basis of robust outcome prediction.

By integrating networks of activated molecular glioma pathways, the model based on genotype better predicts prognosis than histology and, therefore, provides a more reliable tool for standardizing future treatment strategies.

Acquisition of IDH1 or IDH2 mutation (IDHmut) is among the earliest genetic events that take place in the development of most low-grade glioma (LGG). IDHmut has been associated with longer overall patient survival. However, its impact on malignant transformation (MT) remains to be defined. A collection of 210 archived adult LGG previously stratified by IDHmut, MGMT methylation (MGMTmet), 1p/19q combined loss of heterozygosity (1p19qloh) and TP53 immunopositivity (TP53pos) status was analyzed. We used multistate models to assess MT-free survival, considering one initial, one transient (MT), and one absorbing state (death). Missing explanatory variables were multiply imputed. Overall, although associated with a lower risk of death (HR(DEATH) = 0.35, P = 0.0023), IDHmut had a non-significantly higher risk of MT (HR(MT) = 1.84; P = 0.1683) compared to IDH wild type (IDHwt). The double combination of IDHmut and MGMTmet and the triple combination of IDHmut, MGMTmet and 1p/19qloh, despite significantly lower hazards for death (HR(DEATH) versus IDHwt: 0.35, P = 0.0194 and 0.15, P = 0.0008, respectively), had non-significantly different hazards for MT. Conversely, the triple combination of IDHmut/MGMTmet/TP53pos, with a non-significantly different hazard for death, had a significantly higher hazard for MT than IDHwt (HR(MT) versus IDHwt: 2.83; P = 0.0452). Although IDHmut status is associated with longer overall patient survival, all IDHmut/MGMTmet subsets consistently showed higher risks of MT than of death, compared to IDHwt LGG. This supports the findings that molecular events relevant to IDH mutations impact early glioma development prior to malignant transformation.

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Fractional volumes may provide an optimal trade-off for texture analysis in the clinical setting. All texture parameters proved significantly different with minimal expansion of the ROI, underlining the susceptibility of texture analysis to generating misrepresentative tumor information.