Role of Machine Learning in Liquid Biopsy of Brain Tumours

Abstract

Liquid biopsy has multiple benefits and is used extensively
in other fields of oncology, but its role in neuro-oncology
has been limited so far. Multiple tumour-derived materials
like circulating tumour cells (CTCs), tumour-educated
platelets (TEPs), cell-free DNA (cfDNA), circulating tumour
DNA (ctDNA), and miRNA are studied in CSF, blood (plasma,
serum) or urine. Large and complex amounts of data from
liquid biopsy can be simplified by machine learning using
various algorithms. By using this technique, we can
diagnose brain tumours and differentiate low versus highgrade
glioma and true progression from
pseudo-progression. The potential of liquid biopsy in brain
tumours has not been extensively studied, but it has a
bright future in the coming years. Here, we present a
literature review on the role of machine learning in liquid
biopsy of brain tumours.