Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China (M.H., X.W., X.P., Z.W., L.Z., F.W., M.Z., X.G., X.X.); Department of Radiology, Shaoxing No. 2 Hospital Medical Community General Hospital, Shaoxing, China (M.H.).
Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China (M.H., X.W., X.P., Z.W., L.Z., F.W., M.Z., X.G., X.X.).
Acad Radiol. 2024 May;31(5):2061-2073. doi: 10.1016/j.acra.2023.10.059. Epub 2023 Nov 22.
Tumor progression and recurrence（P/R）after surgical resection are common in meningioma patients and can indicate poor prognosis. This study aimed to investigate the values of clinicopathological information and preoperative magnetic resonance imaging (MRI) radiomics in predicting P/R and progression-free survival (PFS) in meningioma patients.
A total of 169 patients with pathologically confirmed meningioma were included in this study, 54 of whom experienced P/R. Clinicopathological information, including age, gender, Simpson grading, World Health Organization (WHO) grading, Ki-67 index, and radiotherapy history, as well as preoperative traditional radiographic findings and radiomics features for each MRI modality (T1-weighted, T2-weighted, and enhanced T1-weighted images) were initially extracted. After feature selection, the optimal performance was estimated among the models established using different feature sets. Finally, Cox survival analysis was further used to predict PFS.
Ki-67 index, Simpson grading, WHO grading, and radiotherapy history were found to be independent predictors for P/R in the multivariate regression analysis. This clinicopathological model had an area under the curve (AUC) of 0.865 and 0.817 in the training and testing sets, respectively. The performance of the combined radiomics model reached 0.85 and 0.84, respectively. A clinicopathological-radiomics model was then established, which significantly improved the prediction of meningioma P/R (AUC = 0.93 and 0.88, respectively). Finally, the risk ratio was estimated for each selected feature, and the C-index of 0.749 was obtained.
Radiomics signatures of preoperative MRI have the ability to predict meningioma at the risk of P/R. By integrating clinicopathological information, the best performance was achieved.
在脑膜瘤患者中，手术切除后的肿瘤进展和复发（P/R）较为常见，且预示着不良预后。本研究旨在探讨临床病理信息和术前磁共振成像（MRI）影像组学在预测脑膜瘤患者 P/R 和无进展生存期（PFS）中的价值。
本研究共纳入 169 例经病理证实的脑膜瘤患者，其中 54 例发生 P/R。收集患者的临床病理信息，包括年龄、性别、Simpson 分级、世界卫生组织（WHO）分级、Ki-67 指数和放疗史，以及术前 MRI 的传统影像学表现和各种 MRI 模态（T1 加权、T2 加权和增强 T1 加权图像）的影像组学特征。在特征选择后，使用不同特征集建立的模型中评估最佳性能。最后，采用 Cox 生存分析预测 PFS。
多变量回归分析显示，Ki-67 指数、Simpson 分级、WHO 分级和放疗史是 P/R 的独立预测因素。该临床病理模型在训练集和测试集的 AUC 分别为 0.865 和 0.817。联合影像组学模型的性能分别达到 0.85 和 0.84。然后建立了临床病理-影像组学模型，显著提高了脑膜瘤 P/R 的预测能力（AUC 分别为 0.93 和 0.88）。最后，对每个选定特征进行风险比估计，并获得 0.749 的 C 指数。
术前 MRI 的影像组学特征具有预测脑膜瘤 P/R 风险的能力。通过整合临床病理信息，可以获得最佳性能。
