Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, , China.
CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, , China; School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
EBioMedicine. 2020 Aug;58:102933. doi: 10.1016/j.ebiom.2020.102933. Epub 2020 Jul 30.
Prediction of brain invasion pre-operatively rather than postoperatively would contribute to the selection of surgical techniques, predicting meningioma grading and prognosis. Here, we aimed to predict the risk of brain invasion in meningioma pre-operatively using a nomogram by incorporating radiomic and clinical features.
In this case-control study, 1728 patients from Beijing Tiantan Hospital (training cohort: n = 1070) and Lanzhou University Second Hospital (external validation cohort: n = 658) were diagnosed with meningiomas by histopathology. Radiomic features were extracted from the T1-weighted post-contrast and T2-weighted magnetic resonance imaging. The least absolute shrinkage and selection operator was used to select the most informative features of different modalities. The support vector machine algorithm was used to predict the risk of brain invasion. Furthermore, a nomogram was constructed by incorporating radiomics signature and clinical risk factors, and decision curve analysis was used to validate the clinical usefulness of the nomogram.
Sixteen features were significantly correlated with brain invasion. The clinicoradiomic model derived from the fusing MRI sequences and sex resulted in the best discrimination ability for risk prediction of brain invasion, with areas under the curves (AUCs) of 0•857 (95% CI, 0•831-0•887) and 0•819 (95% CI, 0•775-0•863) and sensitivities of 72•8% and 90•1% in the training and validation cohorts, respectively.
Our clinicoradiomic model showed good performance and high sensitivity for risk prediction of brain invasion in meningioma, and can be applied in patients with meningiomas.
This work was supported by the National Natural Science Foundation of China (81772006, 81922040); the Youth Innovation Promotion Association CAS (grant numbers 2019136); special fund project for doctoral training program of Lanzhou University Second Hospital (grant numbers YJS-BD-33).
术前而非术后预测脑侵犯有助于选择手术技术，预测脑膜瘤分级和预后。在此，我们旨在通过纳入放射组学和临床特征，使用列线图预测脑膜瘤术前脑侵犯的风险。
在这项病例对照研究中，来自北京天坛医院（训练队列：n=1070）和兰州大学第二医院（外部验证队列：n=658）的 1728 名患者经组织病理学诊断为脑膜瘤。从 T1 加权对比后和 T2 加权磁共振成像中提取放射组学特征。使用最小绝对值收缩和选择算子选择不同模态的最有信息特征。使用支持向量机算法预测脑侵犯的风险。此外，通过纳入放射组学特征和临床危险因素构建列线图，并使用决策曲线分析验证列线图的临床实用性。
16 个特征与脑侵犯显著相关。融合 MRI 序列和性别的临床放射组学模型在预测脑侵犯风险方面具有最佳的区分能力，其在训练队列和验证队列中的曲线下面积（AUC）分别为 0.857（95%CI，0.831-0.887）和 0.819（95%CI，0.775-0.863），敏感度分别为 72.8%和 90.1%。
我们的临床放射组学模型在脑膜瘤脑侵犯风险预测中表现出良好的性能和高灵敏度，可以应用于脑膜瘤患者。
本工作得到了国家自然科学基金（81772006、81922040）；中国科学院青年创新促进会（2019136）；兰州大学第二医院博士研究生培养项目（YJS-BD-33）的资助。
