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A narrative review of radiomics and deep learning advances in neuroblastoma: updates and challenges

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Abstract

Neuroblastoma is an extremely heterogeneous tumor that commonly occurs in children. The diagnosis and treatment of this tumor pose considerable challenges due to its varied clinical presentations and intricate genetic aberrations. Presently, various imaging modalities, including computed tomography, magnetic resonance imaging, and positron emission tomography, are utilized to assess neuroblastoma. Nevertheless, these conventional imaging modalities have limitations in providing quantitative information for accurate diagnosis and prognosis. Radiomics, an emerging technique, can extract intricate medical imaging information that is imperceptible to the human eye and transform it into quantitative data. In conjunction with deep learning algorithms, radiomics holds great promise in complementing existing imaging modalities. The aim of this review is to showcase the potential of radiomics and deep learning advancements to enhance the diagnostic capabilities of current imaging modalities for neuroblastoma.

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Funding

This study was supported by the National Natural Science Foundation of Chongqing ((CSTB)2023NSCQ-BHX0127).

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Author notes

  1. Haoru Wang and Xin Chen contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, Children’s Hospital of Chongqing Medical University, 136 Zhongshan Road 2, Yuzhong District, Chongqing, 400014, China

    Haoru Wang, Xin Chen & Ling He

  2. National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China

    Haoru Wang, Xin Chen & Ling He

  3. Chongqing Key Laboratory of Pediatrics, Chongqing, China

    Haoru Wang, Xin Chen & Ling He

Authors
  1. Haoru Wang
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  2. Xin Chen
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  3. Ling He
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Contributions

X.C. and L.H. conceived, supervised, and supported the study. H.W. performed the literature search and drafted the initial manuscript. X.C. and L.H. reviewed and edited the draft of the manuscript. All authors reviewed and approved the final manuscript.

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Correspondence to Ling He.

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Wang, H., Chen, X. & He, L. A narrative review of radiomics and deep learning advances in neuroblastoma: updates and challenges. Pediatr Radiol 53, 2742–2755 (2023). https://doi.org/10.1007/s00247-023-05792-6

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  • DOI: https://doi.org/10.1007/s00247-023-05792-6

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