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A fully automatic multiparametric radiomics model for differentiation of adult pilocytic astrocytomas from high-grade gliomas

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A Correction to this article was published on 18 March 2022

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Abstract

Objectives

To develop a fully automatic radiomics model to differentiate adult pilocytic astrocytomas (PA) from high-grade gliomas (HGGs).

Methods

This retrospective study included 302 adult patients with PA (n = 62) or HGG (n = 240). The patients were randomly divided into training (n = 211) and test (n = 91) sets. Clinical data were obtained, and radiomic features (n = 372) were extracted from multiparametric MRI with automatic tumour segmentation. After feature selection with F-score, a Light Gradient Boosting Machine classifier with subsampling was trained to develop three models: (1) clinical model, (2) radiomics model, and (3) combined clinical and radiomics model. Human performance was also assessed. The performance of the classifier was validated in the test set. SHapley Additive exPlanations (SHAP) was applied to explore the interpretability of the model.

Results

A total of 15 radiomic features were selected. In the test set, the combined clinical and radiomics model (area under the curve [AUC], 0.93) showed a significantly higher performance than the clinical model (AUC, 0.79, p = 0.037) and had a similar performance to the radiomics model (AUC, 0.92, p = 0.828). The combined clinical and radiomics model also showed a significantly higher performance than humans (AUC, 0.76–0.81, p < 0.05). The model explanation by SHAP suggested that lower intratumoural heterogeneity from T2-weighted images was highly associated with PA diagnosis.

Conclusions

The fully automatic combined clinical and radiomics model may be helpful for differentiating adult PAs from HGGs.

Key Points

• Differentiating adult PAs from HGGs is challenging because PAs may manifest a large spectrum of imaging presentations, often including aggressive imaging features.

• The fully automatic combined clinical and radiomics model showed a significantly higher performance than the clinical model or humans.

• The model explanation by SHAP suggested that second-order features from T2-weighted imaging were important in diagnosis and might reflect the underlying pathophysiology that PAs have lesser tissue heterogeneity than HGGs.

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Abbreviations

AUC:

Area under the curve

CI:

Confidence interval

FLAIR:

Fluid-attenuation inversion recovery

GLCM:

Grey-level co-occurrence matrix

GLSZM:

Grey-level size zone matrix

HGG:

High-grade glioma

IDH:

Isocitrate dehydrogenase

NGTDM:

Neighbouring grey-tone difference matrix

PA:

Pilocytic astrocytoma

SHAP:

SHapley Additive exPlanations

T1:

T1-weighted

T1C:

Post-contrast T1-weighted

T2:

T2-weighted

WHO:

World Health Organization

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Funding

This research received funding from the 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). This research was also supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2020R1I1A1A01071648). This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (HI21C1161).

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Authors and Affiliations

  1. 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, 120-752, Korea

    Yae Won Park, Sung Soo Ahn & Seung-Koo Lee

  2. Department of Computer Science, Yonsei University, Seoul, Korea

    Jihwan Eom

  3. Department of Psychology, Yonsei University, Seoul, Korea

    Dain Kim

  4. Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Korea

    Eui Hyun Kim, Seok-Gu Kang & Jong Hee Chang

  5. Department of Pathology, Yonsei University College of Medicine, Seoul, Korea

    Se Hoon Kim

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  1. Yae Won Park
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Correspondence to Sung Soo Ahn.

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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

We consulted a PhD who has significant statistical expertise (K.H, a biostatistician with 11 years of experience in biostatistics).

Informed consent

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

Ethical approval

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

Methodology

• retrospective

• diagnostic or prognostic study

• performed at one institution

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The original online version of this article was revised: The title was incorrectly given as “A fully automatic multiparametric radiomics model for differentiation of adult pilocytic astrocytomas from glioblastomas” but should have been instead “A fully automatic multiparametric radiomics model for differentiation of adult pilocytic astrocytomas from high-grade gliomas”.

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Park, Y.W., Eom, J., Kim, D. et al. A fully automatic multiparametric radiomics model for differentiation of adult pilocytic astrocytomas from high-grade gliomas. Eur Radiol 32, 4500–4509 (2022). https://doi.org/10.1007/s00330-022-08575-z

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