Zeng Qiang, Tian Zhongyu, Dong Fei, Shi Feina, Xu Penglei, Zhang Jianmin, Ling Chenhan, Guo Zhige
Department of Neurosurgery, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Department of Neurosurgery, Clinical Research Center for Neurological Diseases of Zhejiang Province, Hangzhou, China.
Front Oncol. 2024 Jun 28;14:1246730. doi: 10.3389/fonc.2024.1246730. eCollection 2024.
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 (=0.023), gross total resection (GTR) (<0.001), age <68 years old (=0.023), and edema index <2.3 (=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.
本研究旨在探讨多参数磁共振成像（MRI）的影像组学特征在预测世界卫生组织（WHO）二级脑膜瘤患者无进展生存期（PFS）方面的潜在价值。
采用Kaplan-Meier生存曲线对临床特征进行生存分析。基于每个序列的肿瘤区域分割提取了总共851个影像组学特征，并应用最大相关最小冗余（mRMR）算法对影像组学特征进行筛选和选择。基于所选特征构建了袋装AdaBoost、随机梯度提升、随机森林和神经网络模型。使用受试者工作特征（ROC）和曲线下面积（AUC）评估模型的判别能力。
本研究纳入了164例WHO二级脑膜瘤患者。女性（=0.023）、全切除（GTR）（<0.001）、年龄<68岁（=0.023）和水肿指数<2.3（=0.006）是这些患者PFS的保护因素。袋装AdaBoost模型和神经网络模型在测试集上均表现最佳，AUC为0.927（95%CI，袋装AdaBoost：0.834-1.000；神经网络：0.836-1.000）。
基于影像组学特征的袋装AdaBoost模型和神经网络模型对接受术前多参数MR图像手术的WHO二级脑膜瘤患者的PFS具有良好的预测能力，从而为临床实践中的患者预后预测带来益处。我们的研究强调了利用影像组学等先进成像技术的重要性，通过提供更准确的预后信息来改善脑膜瘤的个性化治疗策略，这些信息可以指导临床医生在有效治疗患者病情的同时，更好地进行决策，同时将与不必要干预或可能无益的治疗相关的风险降至最低。
