本研究旨在探讨多参数MRI影像学特征在预测WHO II级脑膜瘤患者无进展生存期（PFS）中的潜在价值。Kaplan-Meier生存曲线用于临床特征的生存分析。基于每个序列的肿瘤区域分割，总共提取了851个放射组学特征，并应用最大相关性和最小冗余（mRMR）算法来过滤和选择放射组学特征。 Bagged AdaBoost、随机梯度提升、随机森林和神经网络模型是根据选定的特征构建的。使用受试者操作特征 (ROC) 和曲线下面积 (AUC) 评估模型的辨别能力。我们的研究纳入了 164 名 WHO II 级脑膜瘤患者。女性（p=0.023）、肉眼全切除（GTR）（p<0.001）、年龄<68岁（p=0.023）和水肿指数<2.3（p=0.006）是这些患者PFS的保护因素。 Bagged AdaBoost 模型和神经网络模型在测试集上均取得了最佳性能，AUC 为 0.927（95% CI，Bagged AdaBoost：0.834-1.000；神经网络：0.836-1.000）。Bagged AdaBoost 模型和神经网络基于放射组学特征的模型利用术前多参数 MR 图像对接受手术的 WHO II 级脑膜瘤患者的 PFS 表现出良好的预测能力，从而为临床实践中的患者预后预测带来了益处。我们的研究强调了利用放射组学等先进成像技术来改善脑膜瘤个性化治疗策略的重要性，提供更准确的预后信息，指导临床医生在有效治疗患者病情时做出更好的决策过程，同时最大限度地减少与不必要的干预相关的风险或可能无益的治疗。版权所有 © 2024 Zeng、Tian、Dong、Shi、Xu、Zhang、Ling 和Guo。

This study aims to investigate the potential value of radiomic features from multi-parameter MRI in predicting progression-free survival (PFS) of patients with WHO grade II meningiomas.Kaplan-Meier survival curves were used for survival analysis of clinical features. A total of 851 radiomic features were extracted based on tumor region segmentation from each sequence, and Max-Relevance and Min-Redundancy (mRMR) algorithm was applied to filter and select radiomic features. Bagged AdaBoost, Stochastic Gradient Boosting, Random Forest, and Neural Network models were built based on selected features. Discriminative abilities of models were evaluated using receiver operating characteristics (ROC) and area under the curve (AUC).Our study enrolled 164 patients with WHO grade II meningiomas. Female gender (p=0.023), gross total resection (GTR) (p<0.001), age <68 years old (p=0.023), and edema index <2.3 (p=0.006) are protective factors for PFS in these patients. Both the Bagged AdaBoost model and the Neural Network model achieved the best performance on test set with an AUC of 0.927 (95% CI, Bagged AdaBoost: 0.834-1.000; Neural Network: 0.836-1.000).The Bagged AdaBoost model and the Neural Network model based on radiomic features demonstrated decent predictive ability for PFS in patients with WHO grade II meningiomas who underwent operation using preoperative multi-parameter MR images, thus bringing benefit for patient prognosis prediction in clinical practice. Our study emphasizes the importance of utilizing advanced imaging techniques such as radiomics to improve personalized treatment strategies for meningiomas by providing more accurate prognostic information that can guide clinicians toward better decision-making processes when treating their patients' conditions effectively while minimizing risks associated with unnecessary interventions or treatments that may not be beneficial.Copyright © 2024 Zeng, Tian, Dong, Shi, Xu, Zhang, Ling and Guo.