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Intelligent noninvasive meningioma grading with a fully automatic segmentation using interpretable multiparametric deep learning

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Abstract

Objectives

To establish a robust interpretable multiparametric deep learning (DL) model for automatic noninvasive grading of meningiomas along with segmentation.

Methods

In total, 257 patients with pathologically confirmed meningiomas (162 low-grade, 95 high-grade) who underwent a preoperative brain MRI, including T2-weighted (T2) and contrast-enhanced T1-weighted images (T1C), were included in the institutional training set. A two-stage DL grading model was constructed for segmentation and classification based on multiparametric three-dimensional U-net and ResNet. The models were validated in the external validation set consisting of 61 patients with meningiomas (46 low-grade, 15 high-grade). Relevance-weighted Class Activation Mapping (RCAM) method was used to interpret the DL features contributing to the prediction of the DL grading model.

Results

On external validation, the combined T1C and T2 model showed a Dice coefficient of 0.910 in segmentation and the highest performance for meningioma grading compared to the T2 or T1C only models, with an area under the curve (AUC) of 0.770 (95% confidence interval: 0.644–0.895) and accuracy, sensitivity, and specificity of 72.1%, 73.3%, and 71.7%, respectively. The AUC and accuracy of the combined DL grading model were higher than those of the human readers (AUCs of 0.675–0.690 and accuracies of 65.6–68.9%, respectively). The RCAM of the DL grading model showed activated maps at the surface regions of meningiomas indicating that the model recognized the features at the tumor margin for grading.

Conclusions

An interpretable multiparametric DL model combining T1C and T2 can enable fully automatic grading of meningiomas along with segmentation.

Key Points

• The multiparametric DL model showed robustness in grading and segmentation on external validation.

• The diagnostic performance of the combined DL grading model was higher than that of the human readers.

• The RCAM interpreted that DL grading model recognized the meaningful features at the tumor margin for grading.

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Data availability

The data supporting this finding of this study are available from the corresponding author upon reasonable request.

Abbreviations

3D:

Three-dimensional

AUC:

Area under the curve

CI:

Confidence interval

DL:

Deep learning

IQR:

Interquartile range

LRP:

Layer-wise relevance propagation

RCAM:

Relevance-weighted Class Activation Mapping

T1C:

Contrast-enhanced T1-weighted

T2:

T2-weighted

WHO:

World Health Organization

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Funding

Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, Information and Communication Technologies & Future Planning (2020R1A2C1003886); Korea Health Technology R&D Project through the Korea Health Industry Development Institute, funded by the Ministry of Health & Welfare (HI21C1161); Basic Science Research Program through the NRF funded by the Ministry of Education (2020R1I1A1A01071648); Basic Science Research Program through the NRF funded by the Ministry of Science and ICT (2021R1A4A1031437, 2022R1A2C2008983); Brain Research Program through the NRF funded by the Ministry of Science, ICT & Future Planning (2018M3C7A1024734); Partially supported by the Yonsei Signature Research Cluster Program of 2022 (2022-22-0002); Artificial Intelligence Graduate School Program, Yonsei University [No. 2020-0-01361]; the KIST Institutional Program (Project No.2E31051-21-204).

Author information

Author notes

  1. Yohan Jun, Yae Won Park, and Hyungseob Shin contributed equally to this work.

Authors and Affiliations

  1. Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA

    Yohan Jun

  2. Department of Radiology, Harvard Medical School, Boston, MA, USA

    Yohan Jun

  3. Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea

    Yae Won Park, Kyunghwa Han, Sung Soo Ahn, Dosik Hwang & Seung-Koo Lee

  4. School of Electrical and Electronic Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea

    Hyungseob Shin, Yejee Shin, Jeong Ryong Lee & Dosik Hwang

  5. Department of Radiology, Ewha Womans University College of Medicine, Seoul, Korea

    Soo Mee Lim

  6. Center for Healthcare Robotics, Korea Institute of Science and Technology, Seoul, Korea

    Dosik Hwang

  7. Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, Korea

    Dosik Hwang

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  1. Yohan Jun
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Corresponding authors

Correspondence to Sung Soo Ahn or Dosik Hwang.

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Guarantor

The scientific guarantor of this publication is Professor Seung-Koo Lee, MD, PhD, from Yonsei University College of Medicine (slee@yuhs.ac).

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

One of the authors has significant statistical expertise (K.H, a biostatistics professor with 11 years of experience in biostatistics).

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The institutional review board waived the requirement to obtain informed patient consent for this retrospective study.

Ethical approval

The Institutional Review Board waived the requirement of informed patient consent for this retrospective study.

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• retrospective

• diagnostic or prognostic study

• multicenter study

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Jun, Y., Park, Y.W., Shin, H. et al. Intelligent noninvasive meningioma grading with a fully automatic segmentation using interpretable multiparametric deep learning. Eur Radiol 33, 6124–6133 (2023). https://doi.org/10.1007/s00330-023-09590-4

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