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Applications of artificial intelligence in magnetic resonance imaging of primary pediatric cancers: a scoping review and CLAIM score assessment

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Abstract

Purposes

To review the uses of AI for magnetic resonance (MR) imaging assessment of primary pediatric cancer and identify common literature topics and knowledge gaps. To assess the adherence of the existing literature to the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) guidelines.

Materials and methods

A scoping literature search using MEDLINE, EMBASE and Cochrane databases was performed, including studies of > 10 subjects with a mean age of < 21 years. Relevant data were summarized into three categories based on AI application: detection, characterization, treatment and monitoring. Readers independently scored each study using CLAIM guidelines, and inter-rater reproducibility was assessed using intraclass correlation coefficients.

Results

Twenty-one studies were included. The most common AI application for pediatric cancer MR imaging was pediatric tumor diagnosis and detection (13/21 [62%] studies). The most commonly studied tumor was posterior fossa tumors (14 [67%] studies). Knowledge gaps included a lack of research in AI-driven tumor staging (0/21 [0%] studies), imaging genomics (1/21 [5%] studies), and tumor segmentation (2/21 [10%] studies). Adherence to CLAIM guidelines was moderate in primary studies, with an average (range) of 55% (34%–73%) CLAIM items reported. Adherence has improved over time based on publication year.

Conclusion

The literature surrounding AI applications of MR imaging in pediatric cancers is limited. The existing literature shows moderate adherence to CLAIM guidelines, suggesting that better adherence is required for future studies.

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Funding

This work was partially supported by the Samuel Lunenfeld Summer Studentship Award 2021 (project ID: 6120100197), and by The Terry Fox New Frontiers Program Project Grant in Early Detection and Prevention of Li-Fraumeni Syndrome (2018–2023).

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Authors and Affiliations

  1. Department of Medical Imaging, University of Toronto, Toronto, ON, Canada

    Brian Tsang, Aaryan Gupta, Tolulope Ola & Andrea S. Doria

  2. Department of Diagnostic Imaging, Research Institute, The Hospital for Sick Children, Toronto, ON, Canada

    Brian Tsang, Aaryan Gupta, Tolulope Ola & Andrea S. Doria

  3. Instituto de Radiologia do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (InRad/HC-FMUSP), São Paulo, SP, Brazil

    Marcelo Straus Takahashi

  4. Instituto da Criança do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (ICr/HC-FMUSP), São Paulo, SP, Brazil

    Marcelo Straus Takahashi

  5. DasaInova, Diagnósticos da América SA (Dasa), São Paulo, SP, Brazil

    Marcelo Straus Takahashi

  6. Western University, London, ON, Canada

    Henrique Baffi

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  1. Brian Tsang
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Contributions

Study concepts, BT, AG, MST, and ASD; Literature research: BT, AG, MST, and ASD; Data analysis and interpretation: BT, AG, MST, and ASD; Manuscript drafting BT, and ASD; Manuscript editing and revisions, all authors; Final approval of the manuscript: all authors.

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Correspondence to Andrea S. Doria.

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Tsang, B., Gupta, A., Takahashi, M.S. et al. Applications of artificial intelligence in magnetic resonance imaging of primary pediatric cancers: a scoping review and CLAIM score assessment. Jpn J Radiol 41, 1127–1147 (2023). https://doi.org/10.1007/s11604-023-01437-8

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  • DOI: https://doi.org/10.1007/s11604-023-01437-8

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