Background/Objectives:Large-scale (epi)genomic studies have substantially advanced our understanding of the molecular landscape of meningiomas, most recently embedded in the cIMPACT-NOW update 8. As a result, molecular data are increasingly integrated into risk-adapted treatment algorithms. However, it remains uncertain to what extent non-invasive MRI can capture underlying molecular variation and risk.Methods:We assembled a large, single-institution cohort of 225 newly diagnosed meningiomas (WHO grades 1–3) with available preoperative MRI, as well as comprehensive epigenome-wide methylation and copy-number profiling. Tumors were segmented into core and edema regions using a state-of-the-art automated pipeline from the BraTS challenge. Radiomic features were extracted and used to train Random Forest classifiers to predict WHO grade, molecular risk, and specific alterations such as 1p loss in a hold-out test set.Results:Our models achieved accuracy above 91% for integrated molecular risk classification, 87.5% for 1p chromosomal status, and 76.8% for WHO grade prediction, with corresponding AUCs of 0.91, 0.90, and 0.89, underscoring the robustness of radiomic features in capturing histopathological and, especially, molecular characteristics.Conclusions:Preoperative MRI effectively captures the underlying molecular biology of meningiomas and may enable rapid molecular assessment to inform decision-making and prioritization of confirmatory testing. However, it is not yet ready for clinical use, showing lower accuracy for current WHO grade classification.
背景/目的：大规模（表观）基因组研究显著增进了我们对脑膜瘤分子特征的理解，最新成果已纳入cIMPACT-NOW第8次更新。因此，分子数据正日益整合至风险适应性治疗策略中。然而，非侵入性MRI能在多大程度上捕捉潜在的分子变异及风险仍不明确。 方法：我们汇集了225例新诊断脑膜瘤（WHO 1-3级）的单中心队列，所有病例均具备术前MRI数据、全表观基因组甲基化及拷贝数谱分析。通过BraTS挑战赛的先进自动化流程，将肿瘤分割为核心区与水肿区。提取影像组学特征并训练随机森林分类器，在预留测试集中预测WHO分级、分子风险及特定变异（如1p缺失）。 结果：我们的模型在整合分子风险分类中准确率达91%以上，1p染色体状态判断准确率为87.5%，WHO分级预测准确率为76.8%，对应AUC值分别为0.91、0.90和0.89，凸显了影像组学特征在捕捉组织病理学特征（尤其是分子特征）方面的稳健性。 结论：术前MRI能有效捕捉脑膜瘤的潜在分子生物学特征，或可实现快速分子评估以辅助临床决策和验证性检测的优先级排序。但该技术尚未达到临床应用标准，在当前WHO分级体系中的准确性仍有待提升。
肿瘤(癌症)患者之家