Diagnostic technologies for neuroblastoma.

Journal: Lab on a chip
Published Date:

Abstract

Neuroblastoma is an aggressive childhood cancer characterised by high relapse rates and heterogenicity. Current medical diagnostic methods involve an array of techniques, from blood tests to tumour biopsies. This process is associated with long-term physical and psychological trauma. Moreover, current technologies do not identify neuroblastoma at an early stage while tumours are easily resectable. In recent decades, many advancements have been made for neuroblastoma diagnosis, including liquid biopsy platforms, radiomics, artificial intelligence (AI) integration and biosensor technologies. These innovations support the trend towards rapid, non-invasive and cost-effective diagnostic methods which can be utilised for accurate risk stratification. Point-of-care (POC) diagnostic devices enable rapid and accurate detection of disease biomarkers and can be performed at the location of the patient. Whilst POC diagnostics has been well-researched within the oncological landscape, few devices have been reported for neuroblastoma, and these remain in the early research phase and as such are limited by lack of clinical validation. Recent research has revealed several potential biomarkers which have great translational potential for POC diagnosis, including proteomic, metabolic and epigenetic markers such as amplification and microRNAs (miRNAs). Using POC devices to detect high-risk biomarkers in biofluids such as blood and urine, offers a non-invasive approach to diagnosis of neuroblastoma, enabling early screening at a population level as well as real-time health monitoring at home. This is critical to mitigating long-term morbidity associated with late diagnosis and unnecessary treatment, in turn improving outcomes for neuroblastoma patients.

Authors

  • Leena Khelifa
    Department of Chemical Engineering, Imperial College London, South Kensington, London, SW7 2BU, UK. yubing.hu@imperial.ac.uk.
  • Yubing Hu
    Department of Chemical Engineering, Imperial College London, South Kensington, London, SW7 2BU, UK. yubing.hu@imperial.ac.uk.
  • Jennifer Tall
    Division of Clinical Studies, The Institute of Cancer Research, London, SM2 5NG, UK.
  • Rasha Khelifa
    Faculty of Medicine, Imperial College London, South Kensington, SW7 5NH, UK.
  • Amina Ali
    Faculty of Medicine, Imperial College London, South Kensington, SW7 5NH, UK.
  • Evon Poon
    Division of Clinical Studies, The Institute of Cancer Research, London, SM2 5NG, UK.
  • Mohamed Zaki Khelifa
    Faculty of Medicine, Imperial College London, South Kensington, SW7 5NH, UK.
  • Guowei Yang
    Faculty of Medicine, Imperial College London, South Kensington, SW7 5NH, UK.
  • Catarina Jones
    School of Electronics and Computer Science, University of Southampton, Southampton, SO17 1BJ, UK.
  • Rosalia Moreddu
    Institute for Life Sciences, University of Southampton, Southampton, SO17 1BJ, UK.
  • Nan Jiang
  • Savas Tasoglu
    Department of Mechanical Engineering, Koç University, Sariyer, Istanbul, 34450 Turkey. stasoglu@ku.edu.tr and Koç University Research Center for Translational Medicine, Koç University, Sariyer, Istanbul, 34450 Turkey and Koç University Arçelik Research Center for Creative Industries (KUAR), Koç University, Sariyer, Istanbul, 34450 Turkey and Boğaziçi Institute of Biomedical Engineering, Boğaziçi University, Çengelköy, Istanbul, 34684 Turkey.
  • Louis Chesler
    Division of Clinical Studies, The Institute of Cancer Research, London, SM2 5NG, UK.
  • Ali K Yetisen
    Department of Chemical Engineering, Imperial College London, London SW7 2AZ, UK. a.yetisen@imperial.ac.uk.

Keywords

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