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Predicting meningioma grades and pathologic marker expression via deep learning

  • Magnetic Resonance
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European Radiology Aims and scope Submit manuscript

Abstract

Objectives

To establish a deep learning (DL) model for predicting tumor grades and expression of pathologic markers of meningioma.

Methods

A total of 1192 meningioma patients from two centers who underwent surgical resection between September 2018 and December 2021 were retrospectively included. The pathological data and post-contrast T1-weight images for each patient were collected. The patients from institute I were subdivided into training, validation, and testing sets, while the patients from institute II served as the external testing cohort. The fine-tuned ResNet50 model based on transfer learning was adopted to classify WHO grade in the whole cohort and predict Ki-67 index, H3K27me3, and progesterone receptor (PR) status of grade 1 meningiomas. The predictive performance was evaluated by the accuracy and loss curve, confusion matrix, receiver operating characteristic curve (ROC), and area under curve (AUC).

Results

The DL prediction model for each label achieved high predictive performance in two cohorts. For WHO grade prediction, the area under the curve (AUC) was 0.966 (95%CI 0.957–0.975) in the internal testing set and 0.669 (95%CI 0.643–0.695) in the external validation cohort. The AUC in predicting Ki-67 index, H3K27me3, and PR status were 0.905 (95%CI 0.895–0.915), 0.773 (95%CI 0.760–0.786), and 0.771 (95%CI 0.750–0.792) in the internal testing set and 0.591 (95%CI 0.562–0.620), 0.658 (95%CI 0.648–0.668), and 0.703 (95%CI 0.674–0.732) in the external validation cohort, respectively.

Conclusion

DL models can preoperatively predict meningioma grades and pathologic marker expression with favorable predictive performance.

Clinical relevance statement

Our DL model could predict meningioma grades and expression of pathologic markers and identify high-risk patients with WHO grade 1 meningioma, which would suggest a more aggressive operative intervention preoperatively and a more frequent follow-up schedule postoperatively.

Key Points

  • WHO grades and some pathologic markers of meningioma were associated with therapeutic strategies and clinical outcomes.

  • A deep learning–based approach was employed to develop a model for predicting meningioma grades and the expression of pathologic markers.

  • Preoperative prediction of meningioma grades and the expression of pathologic markers was beneficial for clinical decision-making.

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Abbreviations

ACC:

Accuracy

AUC:

Area under curve

CI:

Confidence interval

CNNs:

Convolutional neural networks

CNS:

Central nervous system

DL:

Deep learning

FC:

Fully connected

GFAP:

Glial fibrillary acidic protein

H3K27me3:

Trimethylation of lysine 27 (K27) of histone H3

IDH:

Isocitrate dehydrogenase

IHC:

Immunohistochemical

MITK:

Medical Imaging Interaction Toolkit

ML:

Machine learning

MRI:

Magnetic resonance imaging

PR:

Progesterone receptor

ROC:

Receiver operating characteristic curve

T1C:

Contrast-enhanced T1-weighted

WHO:

World Health Organization

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Funding

The National Natural Science Foundation of China (No. 82072788) (YG), Science and Technology Commission of Shanghai Municipality (No. 22140900200) (YG), and Shanghai Sailing Program (No. 20YF1403900) (LYH) supported this work.

Author information

Authors and Affiliations

  1. Department of Neurosurgery of Huashan Hospital, State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science and Institutes of Brain Science, Fudan University, Shanghai, China

    Jiawei Chen, Yanping Xue, Leihao Ren, Lingyang Hua, Qing Xie, Ruiqi Wu & Ye Gong

  2. Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China

    Kun Lv & Peng Du

  3. Department of Pathology, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China

    Haixia Cheng

  4. Department of Neurosurgery, Shanghai International Hospital, Shanghai, China

    Shuchen Sun

  5. Department of Neurosurgery, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Shuchen Sun

  6. Department of Critical Care Medicine, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China

    Ye Gong

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  1. Jiawei Chen
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Corresponding authors

Correspondence to Qing Xie, Ruiqi Wu or Ye Gong.

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The scientific guarantor of this publication is Ye Gong.

Conflict of interest

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

No complex statistical methods were necessary for this paper.

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Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained (Huashan Hospital, Shanghai Medical College, Fudan University).

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No study subjects or cohorts overlap.

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• retrospective

• experimental

• multicenter study

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Jiawei Chen, Yanping Xue, and Leihao Ren are co-first authors.

Qing Xie, Ruiqi Wu, and Ye Gong are co-corresponding authors.

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Chen, J., Xue, Y., Ren, L. et al. Predicting meningioma grades and pathologic marker expression via deep learning. Eur Radiol (2023). https://doi.org/10.1007/s00330-023-10258-2

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