Early diagnosis of brain tumours using a novel spectroscopic liquid biopsy

Paul M. Brennan, Holly J. Butler, Loren Christie, Mark G. Hegarty, Michael D. Jenkinson, Catriona Keerie, John Norrie, Rachel O'Brien, David S. Palmer, Benjamin R. Smith, Matthew J. Baker

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Abstract

Early diagnosis of brain tumours is challenging and a major unmet need. Patients with brain tumours most often present with non-specific symptoms more commonly associated with less serious diagnoses, making it difficult to determine which patients to priortitise for brain imaging. Delays in diagnosis affect timely access to treatment, with potential impacts on quality of life and survival. A test to help identify which patients with non-specific symptoms are most likely to have a brain tumour at an earlier stage would dramatically impact on patients by prioritising demand on diagnostic imaging facilities. This clinical feasibility study of brain tumour early diagnosis was aimed at determining the accuracy of our novel spectroscopic liquid biopsy test for the triage of patients with non-specific symptoms that might be indicative of a brain tumour, for brain imaging. Patients with a suspected brain tumour based on assesement of their symptoms in primary care can be referred for open access CT scanning. Blood samples were prospectively obtained from 385 of such patients, or patients with a new brain tumour diagnosis. Samples were analysed using our spectroscopic liquid biopsy test to predict presence of disease, blinded to the brain imaging findings. The results were compared to the patient’s index brain imaging delivered as per standard care. Our test predicted the presence of glioblastoma, the most common and aggressive brain tumour, with 91% sensitivity, and all brain tumours with 81% sensitivity, and 80% specificity. Negative predictive value was 95% and positive predictive value 45%. The reported levels of diagnostic accuracy presented here have the potential to improve current symptom-based referral guidelines, and streamline assessment and diagnosis of symptomatic patients with a suspected brain tumour.
Original languageEnglish
Article numberfcab056
Number of pages9
JournalBrain Communications
Volume3
Issue number2
Early online date30 Mar 2021
DOIs
Publication statusE-pub ahead of print - 30 Mar 2021

Keywords

  • brain tumour
  • spectroscopy
  • machine learning
  • cancer
  • diagnostic
  • serum

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