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Incorporating Radiomics into Machine Learning Models to Predict Outcomes of Neuroblastoma

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Abstract

Neuroblastoma is one of the most common pediatric cancers. This study used machine learning (ML) to predict the mortality and a few other investigated intermediate outcomes of neuroblastoma patients non-invasively from CT images. Performances of multiple ML algorithms over retrospective CT images of 65 neuroblastoma patients are analyzed. An artificial neural network (ANN) is used on tumor radiomic features extracted from 3D CT images. A pre-trained 2D convolutional neural network (CNN) is used on slices of the same images. ML models are trained for various pathologically investigated outcomes of these patients. A subspecialty-trained pediatric radiologist independently reviewed the manually segmented primary tumors. Pyradiomics library is used to extract 105 radiomic features. Six ML algorithms are compared to predict the following outcomes: mortality, presence or absence of metastases, neuroblastoma differentiation, mitosis-karyorrhexis index (MKI), presence or absence of MYCN gene amplification, and presence of image-defined risk factors (IDRF). The prediction ranges over multiple experiments are measured using the area under the receiver operating characteristic (ROC-AUC) for comparison. Our results show that the radiomics-based ANN method slightly outperforms the other algorithms in predicting all outcomes except classification of the grade of neuroblastic differentiation, for which the elastic regression model performed the best. Contributions of the article are twofold: (1) noninvasive models for the prognosis from CT images of neuroblastoma, and (2) comparison of relevant ML models on this medical imaging problem.

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Acknowledgements

This work was supported in part by the National Cancer Institute Grant R01CA154561 and the National Institute of Biomedical Imaging & Bioengineering Grant R15EB030807. Anonymous reviewers’ comments have significantly improved the article.

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Authors and Affiliations

  1. Department of Computer Engineering and Sciences, Florida Institute of Technology, Melbourne, FL, USA

    Gengbo Liu & Debasis Mitra

  2. Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA

    Mini Poon, Matthew A. Zapala, Debasis Mitra & Youngho Seo

  3. Department of Pediatrics, University of California, San Francisco, CA, USA

    William C. Temple, Kieuhoa T. Vo & Kathrine K. Matthay

Authors
  1. Gengbo Liu
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  2. Mini Poon
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  3. Matthew A. Zapala
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  4. William C. Temple
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  5. Kieuhoa T. Vo
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  6. Kathrine K. Matthay
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  7. Debasis Mitra
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  8. Youngho Seo
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Corresponding authors

Correspondence to Debasis Mitra or Youngho Seo.

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This study was a retrospective study of medical records and medical images and qualified as exempt by the appropriate Institutional Review Board (IRB) at the Florida Institute of Technology.

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The authors declare no competing interests.

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Liu, G., Poon, M., Zapala, M.A. et al. Incorporating Radiomics into Machine Learning Models to Predict Outcomes of Neuroblastoma. J Digit Imaging 35, 605–612 (2022). https://doi.org/10.1007/s10278-022-00607-w

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  • DOI: https://doi.org/10.1007/s10278-022-00607-w

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