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Deep learning–based automatic segmentation of meningioma from multiparametric MRI for preoperative meningioma differentiation using radiomic features: a multicentre study

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

Abstract

Objectives

Develop and evaluate a deep learning–based automatic meningioma segmentation method for preoperative meningioma differentiation using radiomic features.

Methods

A retrospective multicentre inclusion of MR examinations (T1/T2-weighted and contrast-enhanced T1-weighted imaging) was conducted. Data from centre 1 were allocated to training (n = 307, age = 50.94 ± 11.51) and internal testing (n = 238, age = 50.70 ± 12.72) cohorts, and data from centre 2 external testing cohort (n = 64, age = 48.45 ± 13.59). A modified attention U-Net was trained for meningioma segmentation. Segmentation accuracy was evaluated by five quantitative metrics. The agreement between radiomic features from manual and automatic segmentations was assessed using intra class correlation coefficient (ICC). After univariate and minimum-redundancy-maximum-relevance feature selection, L1-regularized logistic regression models for differentiating between low-grade (I) and high-grade (II and III) meningiomas were separately constructed using manual and automatic segmentations; their performances were evaluated using ROC analysis.

Results

Dice of meningioma segmentation for the internal testing cohort were 0.94 ± 0.04 and 0.91 ± 0.05 for tumour volumes in contrast-enhanced T1-weighted and T2-weighted images, respectively; those for the external testing cohort were 0.90 ± 0.07 and 0.88 ± 0.07. Features extracted using manual and automatic segmentations agreed well, for both the internal (ICC = 0.94, interquartile range: 0.88–0.97) and external (ICC = 0.90, interquartile range: 0.78–70.96) testing cohorts. AUC of radiomic model with automatic segmentation was comparable with that of the model with manual segmentation for both the internal (0.95 vs. 0.93, p = 0.176) and external (0.88 vs. 0.91, p = 0.419) testing cohorts.

Conclusions

The developed deep learning–based segmentation method enables automatic and accurate extraction of meningioma from multiparametric MR images and can help deploy radiomics for preoperative meningioma differentiation in clinical practice.

Key Points

• A deep learning–based method was developed for automatic segmentation of meningioma from multiparametric MR images.

• The automatic segmentation method enabled accurate extraction of meningiomas and yielded radiomic features that were highly consistent with those that were obtained using manual segmentation.

• High-grade meningiomas were preoperatively differentiated from low-grade meningiomas using a radiomic model constructed on features from automatic segmentation.

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Abbreviations

ASSD:

Average symmetric surface distance

AUC:

Area under the curve

CE-T1WI:

Contrast-enhanced T1-weighted imaging

CETV:

Tumour volume segmented in CE-T1WI

CNN:

Convolutional neural network

HD95:

95% Hausdorff distance

ICC:

Intraclass correlation coefficient

PPV:

Positive predictive value

ROC:

Receiver operating characteristic

ROI:

Region of interest

T1WI:

T1-weighted imaging

T2TV:

Tumour volume segmented in T2WI

T2WI:

T2-weighted imaging

VOE:

Volume overlap error

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Acknowledgements

The author(s) would like to thank Ms. Qian Li for the computing resource she provided.

Funding

This study has received funding from the National Natural Science Foundation of China (U21A6005, 81871349), Key-Area Research and Development Program of Guangdong Province (2018B030340001, 2018B030333001), the National Key Research and Development Program of China (2019YFC0118702), the Technology Research and Development Program of Guangdong (2017B090912006), and the Science and Technology Planning Project of Guangzhou City, China (201907010043).

Author information

Author notes

  1. Haolin Chen and Shuqi Li contributed equally to this work.

Authors and Affiliations

  1. School of Biomedical Engineering, Southern Medical University, 1023 Shatainan Road, Guangzhou, 510515, China

    Haolin Chen, Yongju Yi & Yanqiu Feng

  2. Guangdong Provincial Key Laboratory of Medical Image Processing & Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China

    Haolin Chen & Yanqiu Feng

  3. Guangdong-Hong Kong-Macao Greater Bay Area Centre for Brain Science and Brain-Inspired Intelligence & Key Laboratory of Mental Health of the Ministry of Education, Guangzhou, China

    Haolin Chen & Yanqiu Feng

  4. Department of Radiology, Sun Yat-Sen University Cancer Centre, Guangzhou, China

    Shuqi Li, Lizhi Liu, Xiaofei Lv & Guangying Ruan

  5. State Key Laboratory of Oncology in South China, Sun Yat-Sen University Cancer Centre, Guangzhou, China

    Shuqi Li, Lizhi Liu, Xiaofei Lv, Guangying Ruan & Chao Ke

  6. Collaborative Innovation Centre for Cancer Medicine, Sun Yat-Sen University Cancer Centre, Guangzhou, China

    Shuqi Li, Lizhi Liu, Xiaofei Lv, Guangying Ruan & Chao Ke

  7. Department of Radiology, Xiangya Hospital, Central South University, Changsha, China

    Youming Zhang

  8. Network Information Centre, The Sixth Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China

    Yongju Yi

  9. Department of Neurosurgery and Neuro-oncology, Sun Yat-Sen University Cancer Centre, 651 Dongfeng East Road, Guangzhou, 510060, China

    Chao Ke

  10. Department of Rehabilitation, Zhujiang Hospital, Southern Medical University, Guangzhou, China

    Yanqiu Feng

Authors
  1. Haolin Chen
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Corresponding authors

Correspondence to Chao Ke or Yanqiu Feng.

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Guarantor

The scientific guarantor of this publication is Yanqiu Feng.

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.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Study subjects or cohorts overlap

Some study subjects or cohorts have been previously reported in Ke C, Chen H, Lv X et al (2020) Differentiation between benign and nonbenign meningiomas by using texture analysis from multiparametric MRI. J Magn Reson Imaging 51:1810-1820

Methodology

• retrospective

• experimental

• multicentre study

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Chen, H., Li, S., Zhang, Y. et al. Deep learning–based automatic segmentation of meningioma from multiparametric MRI for preoperative meningioma differentiation using radiomic features: a multicentre study. Eur Radiol 32, 7248–7259 (2022). https://doi.org/10.1007/s00330-022-08749-9

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  • DOI: https://doi.org/10.1007/s00330-022-08749-9

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