基于多参数MRI的栖息地影像组学模型用于桥小脑角区前庭神经鞘瘤与脑膜瘤鉴别诊断的开发：一项多中心研究
Development of a multi-parameter MRI-based habitat radiomics model for differential diagnosis of vestibular schwannoma and meningioma in the cerebellopontine angle: a multi-center study.
文献信息
| PMID | 42928133 |
|---|---|
| 原文 | 在 PubMed 查看原文 ↗ |
| 发表日期 | 2026 |
| 作者 | Xiyuan Tang |
| 作者单位 | Department of Neurosurgery, Guangyuan Central Hospital, Guangyuan, Sichuan, 628000, China. |
| 期刊 | BMC cancer |
| SCI 分区 | Q2 |
| IF | 4.1 |
| 研究类型 | AI/ML · 临床 |
| 所属专科 | 耳科 |
中文摘要
背景： 前庭神经鞘瘤（VS）和脑膜瘤（MM）是桥小脑角区（CPA）最常见的良性肿瘤，常表现出相似的临床症状。准确的术前鉴别对于制定合适的治疗方案至关重要。
目的： 开发一种基于MRI的栖息地影像组学模型，整合多参数MRI数据和临床特征，用于CPA区VS与MM的鉴别诊断。
方法： 本回顾性研究纳入来自三家医疗中心（广元市中心医院、南充市中心医院和川北医学院附属医院）的176例诊断为CPA区VS或MM的患者。101例患者（68例VS，33例MM）符合纳入标准。采用滑动窗口法对术前MRI扫描（T2WI和CE-T1WI）进行特征提取分析，随后进行图像预处理和高斯混合模型（GMM）聚类以分割栖息地亚区。分别构建基于T2WI、CE-T1WI以及融合T2WI与CE-T1WI的栖息地影像组学模型，并通过整合临床特征开发了栖息地-临床联合模型，并在多种不同的机器学习分类器之间对模型进行了比较。
结果： 结合T2WI和CE-T1WI特征的多参数模型优于单参数模型，在训练集中AUC值达到0.987，在测试集中达到0.974。整合栖息地影像组学和临床特征的联合模型显示出更优的诊断准确性。决策曲线分析和校准曲线证实了其临床实用性和可靠性。
结论： 基于MRI的栖息地影像组学与临床特征相结合，为鉴别CPA区VS与MM提供了一种稳健、无创的方法，为个性化治疗策略提供了潜力。
英文摘要
BACKGROUND: Vestibular schwannomas (VS) and meningiomas (MM) are the most common benign tumors in the cerebellopontine angle (CPA), often presenting with similar clinical symptoms. Accurate preoperative differentiation is essential for appropriate treatment planning.
OBJECTIVE: To develop an MRI-based habitat radiomics model for the differential diagnosis of VS and MM in the CPA, integrating multi-parameter MRI data and clinical features.
METHODS: This retrospective study included 176 patients diagnosed with CPA VS or MM from three medical centers: Guangyuan Central Hospital, Nanchong Central Hospital, and Affiliated Hospital of North Sichuan Medical College. 101 patients (68 VS, 33 MM) met the inclusion criteria. Preoperative MRI scans (T2WI and CE-T1WI) were analyzed using a sliding window method for feature extraction, followed by image preprocessing and Gaussian Mixture Model (GMM) clustering for habitat subregion segmentation.Habitat radiomics models based on T2WI, CE-T1WI, and fused T2WI & CE-T1WI were constructed respectively, and a combined habitat-clinical model was developed by integrating clinical features, and the models were compared across multiple different machine learning classifiers.
RESULTS: Multi-parameter models, combining T2WI and CE-T1WI features, outperformed single-parameter models, achieving AUC values of 0.987 in the training set and 0.974 in the test set. A combined model integrating habitat radiomics and clinical features showed superior diagnostic accuracy. Decision Curve Analysis and calibration curves confirmed its clinical utility and reliability.
CONCLUSION: MRI-based habitat radiomics, when integrated with clinical features, provides a robust, noninvasive method for differentiating CPA VS from MM, offering potential for personalized treatment strategies.