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Role of MRI radiomics for the prediction of MYCN amplification in neuroblastomas

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

Abstract

Objectives

We evaluate MR radiomics and develop machine learning–based classifiers to predict MYCN amplification in neuroblastomas.

Methods

A total of 120 patients with neuroblastomas and baseline MR imaging examination available were identified of whom 74 (mean age ± standard deviation [SD] of 6 years and 2 months ± 4 years and 9 months; 43 females and 31 males, 14 MYCN amplified) underwent imaging at our institution. This was therefore used to develop radiomics models. The model was tested in a cohort of children with the same diagnosis but imaged elsewhere (n = 46, mean age ± SD: 5 years 11 months ± 3 years 9 months, 26 females and 14 MYCN amplified). Whole tumour volumes of interest were adopted to extract first-order histogram and second-order radiomics features. Interclass correlation coefficient and maximum relevance and minimum redundancy algorithm were applied for feature selection. Logistic regression, support vector machine, and random forest were employed as the classifiers. Receiver operating characteristic (ROC) analysis was performed to evaluate the diagnostic accuracy of the classifiers on the external test set.

Results

The logistic regression model and the random forest both showed an AUC of 0.75. The support vector machine classifier obtained an AUC of 0.78 on the test set with a sensitivity of 64% and a specificity of 72%.

Conclusion

The study provides preliminary retrospective evidence demonstrating the feasibility of MRI radiomics in predicting MYCN amplification in neuroblastomas. Future studies are needed to explore the correlation between other imaging features and genetic markers and to develop multiclass predictive models.

Key Points

• MYCN amplification in neuroblastomas is an important determinant of disease prognosis.

• Radiomics analysis of pre-treatment MR examinations can be used to predict MYCN amplification in neuroblastomas.

• Radiomics machine learning models showed good generalisability to external test set, demonstrating reproducibility of the computational models.

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Abbreviations

AUC-ROC:

Area under the receiver operator curve

CT:

Computed tomography

FST1:

(Non-contrast) fat-saturated T1-weighted images

ICC:

Interclass correlation

LR:

Logistic regression

MRI:

Magnetic resonance imaging

NFST2:

Non-fat-saturated T2-weighted images

PACS:

Picture Archiving and Communications System

ROI:

Region of interest

SVM:

Support vector machine

VOI:

Volume of interest

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Funding

The authors state that this work has not received any funding.

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Authors and Affiliations

  1. Department of Radiology, Cincinnati Children’s Hospital and Medical Centre, Cincinnati, OH, USA

    Adarsh Ghosh

  2. Department of Radiology, Children’s Hospital of Philadelphia, Philadelphia, PA, USA

    Adarsh Ghosh, Ensar Yekeler, Sara Reis Teixeira, Deepa Dalal & Lisa States

  3. Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA

    Lisa States

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  2. Ensar Yekeler
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Correspondence to Adarsh Ghosh.

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Guarantor

The scientific guarantor of this publication is Dr Lisa States.

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.

Informed consent

Written informed consent was waived by the Institutional Review Board.

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Study subjects or cohorts overlap

A part of the data (n = 77) has been reported in the manuscript: Whole-tumour apparent diffusion coefficient (ADC) histogram analysis to identify MYCN amplification in neuroblastomas: preliminary results https://doi.org/10.1007/s00330-022-08750-2

Methodology

• retrospective

• diagnostic

• performed at one institution, but data obtained retrospectively from multiple sites

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Ghosh, A., Yekeler, E., Teixeira, S.R. et al. Role of MRI radiomics for the prediction of MYCN amplification in neuroblastomas. Eur Radiol 33, 6726–6735 (2023). https://doi.org/10.1007/s00330-023-09628-7

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