A radiomics model for preoperative prediction of brain invasion in meningioma non-invasively based on MRI: A multicentre study

Jing Zhang; Kuan Yao; Panpan Liu; Zhenyu Liu; Tao Han; Zhiyong Zhao; Yuntai Cao; Guojin Zhang; Junting Zhang; Jie Tian; Junlin Zhou

doi:10.1016/j.ebiom.2020.102933

A radiomics model for preoperative prediction of brain invasion in meningioma non-invasively based on MRI: A multicentre study

EBioMedicine. 2020 Aug:58:102933. doi: 10.1016/j.ebiom.2020.102933. Epub 2020 Jul 30.

Authors

Jing Zhang¹, Kuan Yao², Panpan Liu³, Zhenyu Liu⁴, Tao Han⁵, Zhiyong Zhao⁶, Yuntai Cao⁵, Guojin Zhang⁵, Junting Zhang⁷, Jie Tian⁸, Junlin Zhou⁹

Affiliations

¹ 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.
² 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.
³ Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Nansihuan Xilu 119, Fengtai District, Beijing, China; Department of Neurosurgery, The Municipal Hospital of Weihai, China.
⁴ 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 Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100080, China.
⁵ 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.
⁶ Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou, China.
⁷ Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Nansihuan Xilu 119, Fengtai District, Beijing, China. Electronic address: zhangjunting2003@aliyun.com.
⁸ 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 Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100080, China; Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing, 100191, China; Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710126, China; Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology, Beijing, 100191, China. Electronic address: jie.tian@ia.ac.cn.
⁹ 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. Electronic address: ery_zhoujl@lzu.edu.cn.

Abstract

Background: 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.

Methods: 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.

Findings: 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.

Interpretation: 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.

Funding: 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).

Keywords: Brain invasion; Magnetic resonance images; Meningioma; Radiomics.

Publication types

Multicenter Study

MeSH terms

Adult
Case-Control Studies
Female
Humans
Magnetic Resonance Imaging
Male
Meningeal Neoplasms / diagnostic imaging*
Meningeal Neoplasms / pathology
Meningeal Neoplasms / surgery
Meningioma / diagnostic imaging*
Meningioma / pathology
Meningioma / surgery
Middle Aged
Neoplasm Grading
Neoplasm Invasiveness
Nomograms*
Prognosis
Support Vector Machine