由于脑膜瘤的WHO分级不能完全反映其临床行为，因此需要澄清其他可能表明肿瘤侵袭性增大和复发风险增加的因素。在本研究中，作者们使用Ki-67作为临床预后标志物，独立于WHO分级，通过多参数MRI放射组学特征鉴定了脑膜瘤的标志。回顾性分析了2012年至2018年间所有切除的脑膜瘤。术前磁共振图像用于高通量放射组学特征提取，并随后用于开发机器学习算法，基于Ki-67指数< 5%和≥5%，独立于WHO分级对脑膜瘤进行分层。根据机器学习对Ki-67分层的预测与基于组织病理学Ki-67的结果进行比较，评估了无进展生存期（PFS），在机器学习预测的Ki-67层的基础上，与基于组织病理学Ki-67的结果相比较。共纳入了343例脑膜瘤：其中291例属于WHO分级I级，43例属于II级，9例属于III级病例。复发率总体为19.8%（I级为15.1%，II级为44.2%，III级为77.8%），随访中位数为28.5个月。II级和III级肿瘤的Ki-67指数较I级肿瘤更高，尽管脑膜瘤的肿瘤和周围水肿容量独立于脑膜瘤WHO分级具有相当大的变异。识别了46个高效的放射组学特征（形态学特征1个，基于强度的特征7个，纹理特征38个），并用它们构建了支持向量机模型，以基于5%的Ki-67阈值对肿瘤进行分层，结果表明，在发现（n = 257）和验证（n = 86）数据集中，曲线下面积分别为0.83（95% CI 0.78-0.89）和0.84（95% CI 0.75-0.94）。组织病理学Ki-67与机器学习预测的Ki-67相比较，表现出很好的性能（整体准确度> 80%），I级脑膜瘤的分类准确度最高。机器学习分类器预测的Ki-67明显预示着Ki-67指数≥5%的脑膜瘤PFS较短，与使用组织病理学Ki-67观察到的不同患者预后结果相一致。Ki-67增殖指数可以作为WHO分级无关的脑膜瘤侵袭性增加的替代标志物。利用放射组学特征分析的机器学习方法可用于术前预测脑膜瘤的Ki-67，这提供了增强的分析洞察力，有助于改善诊断分类并指导个体化治疗策略。

The clinical behavior of meningiomas is not entirely captured by its designated WHO grade, therefore other factors must be elucidated that portend increased tumor aggressiveness and associated risk of recurrence. In this study, the authors identify multiparametric MRI radiomic signatures of meningiomas using Ki-67 as a prognostic marker of clinical outcomes independent of WHO grade.A retrospective analysis was conducted of all resected meningiomas between 2012 and 2018. Preoperative MR images were used for high-throughput radiomic feature extraction and subsequently used to develop a machine learning algorithm to stratify meningiomas based on Ki-67 indices < 5% and ≥ 5%, independent of WHO grade. Progression-free survival (PFS) was assessed based on machine learning prediction of Ki-67 strata and compared with outcomes based on histopathological Ki-67.Three hundred forty-three meningiomas were included: 291 with WHO grade I, 43 with grade II, and 9 with grade III. The overall rate of recurrence was 19.8% (15.1% in grade I, 44.2% in grade II, and 77.8% in grade III) over a median follow-up of 28.5 months. Grade II and III tumors had higher Ki-67 indices than grade I tumors, albeit tumor and peritumoral edema volumes had considerable variation independent of meningioma WHO grade. Forty-six high-performing radiomic features (1 morphological, 7 intensity-based, and 38 textural) were identified and used to build a support vector machine model to stratify tumors based on a Ki-67 cutoff of 5%, with resultant areas under the curve of 0.83 (95% CI 0.78-0.89) and 0.84 (95% CI 0.75-0.94) achieved for the discovery (n = 257) and validation (n = 86) data sets, respectively. Comparison of histopathological Ki-67 versus machine learning-predicted Ki-67 showed excellent performance (overall accuracy > 80%), with classification of grade I meningiomas exhibiting the greatest accuracy. Prediction of Ki-67 by machine learning classifier revealed shorter PFS for meningiomas with Ki-67 indices ≥ 5% compared with tumors with Ki-67 < 5% (p < 0.0001, log-rank test), which corroborates divergent patient outcomes observed using histopathological Ki-67.The Ki-67 proliferation index may serve as a surrogate marker of increased meningioma aggressiveness independent of WHO grade. Machine learning using radiomic feature analysis may be used for the preoperative prediction of meningioma Ki-67, which provides enhanced analytical insights to help improve diagnostic classification and guide patient-specific treatment strategies.