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Predicting peritumoral edema development after gamma knife radiosurgery of meningiomas using machine learning methods: a multicenter study

  • Imaging Informatics and Artificial Intelligence
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

Edema is a complication of gamma knife radiosurgery (GKS) in meningioma patients that leads to a variety of consequences. The aim of this study is to construct radiomics-based machine learning models to predict post-GKS edema development.

Methods

In total, 445 meningioma patients who underwent GKS in our institution were enrolled and partitioned into training and internal validation datasets (8:2). A total of 150 cases from multicenter data were included as the external validation dataset. In each case, 1132 radiomics features were extracted from each pre-treatment MRI sequence (contrast-enhanced T1WI, T2WI, and ADC maps). Nine clinical features and eight semantic features were also generated. Nineteen random survival forest (RSF) and nineteen neural network (DeepSurv) models with different combinations of radiomics, clinical, and semantic features were developed with the training dataset, and evaluated with internal and external validation. A nomogram was derived from the model achieving the highest C-index in external validation.

Results

All the models were successfully validated on both validation datasets. The RSF model incorporating clinical, semantic, and ADC radiomics features achieved the best performance with a C-index of 0.861 (95% CI: 0.748–0.975) in internal validation, and 0.780 (95% CI: 0.673–0.887) in external validation. It stratifies high-risk and low-risk cases effectively. The nomogram based on the predicted risks provided personalized prediction with a C-index of 0.962 (95%CI: 0.951–0.973) and satisfactory calibration.

Conclusion

This RSF model with a nomogram could represent a non-invasive and cost-effective tool to predict post-GKS edema risk, thus facilitating personalized decision-making in meningioma treatment.

Clinical relevance statement

The RSF model with a nomogram built in this study represents a handy, non-invasive, and cost-effective tool for meningioma patients to assist in better counselling on the risks, appropriate individual treatment decisions, and customized follow-up plans.

Key Points

• Machine learning models were built to predict post-GKS edema in meningioma. The random survival forest model with clinical, semantic, and ADC radiomics features achieved excellent performance.

• The nomogram based on the predicted risks provides personalized prediction with a C-index of 0.962 (95%CI: 0.951–0.973) and satisfactory calibration and shows the potential to assist in better counselling, appropriate treatment decisions, and customized follow-up plans.

• Given the excellent performance and convenient acquisition of the conventional sequence, we envision that this non-invasive and cost-effective tool will facilitate personalized medicine in meningioma treatment.

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Abbreviations

ADC:

Apparent diffusion coefficient

CE-T1WI:

Contrast-enhanced T1-weighted image

CI:

Confidence interval

CPH:

Cox proportional hazards

DWI:

Diffusion-weighted Imaging

EANO:

European Association of Neuro-Oncology

GKS:

Gamma knife radiosurgery

GND:

Greenwood-Nam-D’Agostino

ICC:

Intraclass/interclass correlation coefficient

IRB:

Institutional review board

ML:

Machine learning

OOB:

Out-of-bag

RANO:

Response Assessment in Neuro-Oncology

RSF:

Random survival forest

SRS:

Stereotactic radiosurgery

T2WI:

T2-weighted image

VEGF:

Vascular endothelial growth factor

VOI:

Volume of interest

VPF:

Vascular permeability factor

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Funding

This project was supported by the Clinical Research Plan of SHDC (Grant No. SHDC2020CR4069), the Medical Engineering Fund of Fudan University(Grant No. yg2021-029), the Shanghai Sailing Program (Grant No. 21YF1404800), the Youth Program of Special Project for Clinical Research of Shanghai Municipal Health Commission Health industry (Grant No. 20204Y0421), the Youth Medical Talents –Medical Imaging Practitioner Program (No.3030256001), the Shanghai Municipal Science and Technology Major Project (No. 2018SHZDZX01), ZJ Lab, and Shanghai Center for Brain-Inspired Technology.

Author information

Author notes

  1. Xuanxuan Li, Yiping Lu, Li Liu, and Dongdong Wang contributed equally to the paper. They should be regarded as the co-first authors.

Authors and Affiliations

  1. Department of Radiology, Huashan Hospital, Fudan University, 12, Middle Wulumuqi Rd., Jing’an District, Shanghai, 200040, China

    Xuanxuan Li, Yiping Lu, Dongdong Wang, Yajing Zhao, Nan Mei, Daoying Geng & Bo Yin

  2. Department of Radiology, Shanghai Cancer Center, Fudan University, Shanghai, China

    Li Liu

  3. Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu Province, China

    Xin Ma

  4. Department of Environmental Health, School of Public Health, Fudan University, Shanghai, China

    Weiwei Zheng

  5. GE Healthcare, Shanghai, China

    Shaofeng Duan & Pu-Yeh Wu

  6. Department of Computer Science, University of Warwick, Coventry, UK

    Hongkai Wen

  7. Neurosurgery Gamma Knife Centre, Zibo Wanjie Cancer Hospital, Zibo City, Shandong, China

    Yongli Tan & Xiaogang Sun

  8. Beijing Neurosurgical Institute, Beijing Tiantan Hospital, Capital Medical University, Beijing, China

    Shibin Sun

  9. Neurosurgery Department, Wenzhou Central Hospital, Wenzhou, Zhejiang Province, China

    Zhiwei Li

  10. Department of Radiology, Shanghai Gamma Hospital, Huashan Hospital, Fudan University, 518, Middle Wuzhong Rd., Xuhui District, Shanghai, 200235, China

    Tonggang Yu

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  1. Xuanxuan Li
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Corresponding authors

Correspondence to Tonggang Yu or Bo Yin.

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The scientific guarantor of this publication is Dr. Bo Yin.

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The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

One of the authors (Weiwei Zheng, School of Public Health, Fudan University) has significant statistical expertise.

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• retrospective

• diagnostic or prognostic study

• multicenter study

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Li, X., Lu, Y., Liu, L. et al. Predicting peritumoral edema development after gamma knife radiosurgery of meningiomas using machine learning methods: a multicenter study. Eur Radiol 33, 8912–8924 (2023). https://doi.org/10.1007/s00330-023-09955-9

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