Developing infrared spectroscopic detection for stratifying brain tumour patients: glioblastoma multiforme vs. lymphoma. Issue 22 (15th October 2019)

• Record Type: Journal Article
• Authors: Cameron, James M.; Butler, Holly J.; Smith, Benjamin R.; Hegarty, Mark G.; Jenkinson, Michael D.; Syed, Khaja; Brennan, Paul M.; Ashton, Katherine; Dawson, Timothy; Palmer, David S.; Baker, Matthew J.
• Is Part Of: Analyst. Volume 144:Issue 22(2019)
• Page Start: 6736
• Page End: 6750
• Publication Date: 2019-10-15
• Subjects: Chemistry, Analytic -- Periodicals; 543
• DOI: 10.1039/c9an01731c
• Languages: English
• ISSNs: 0003-2654

Abstract

Abstract : There are currently no methods in place for the early detection of brain cancer. A reliable serum triage test could avoid the need for surgery, and speed up time to definitive treatment. Could high-throughput infrared spectroscopy fill the void? Abstract : Over a third of brain tumour patients visit their general practitioner more than five times prior to diagnosis in the UK, leading to 62% of patients being diagnosed as emergency presentations. Unfortunately, symptoms are non-specific to brain tumours, and the majority of these patients complain of headaches on multiple occasions before being referred to a neurologist. As there are currently no methods in place for the early detection of brain cancer, the affected patients' average life expectancy is reduced by 20 years. These statistics indicate that the current pathway is ineffective, and there is a vast need for a rapid diagnostic test. Attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy is sensitive to the hallmarks of cancer, as it analyses the full range of macromolecular classes. The combination of serum spectroscopy and advanced data analysis has previously been shown to rapidly and objectively distinguish brain tumour severity. Recently, a novel high-throughput ATR accessory has been developed, which could be cost-effective to the National Health Service in the UK, and valuable for clinical translation. In this study, 765 blood serum samples have been collected from healthy controls and patients diagnosed with various types of brain cancer, contributing to one of the largest spectroscopic studies to date. Three robust machine learning techniques – random forest, partial least squares-discriminant analysis and support vector machine – have all provided promising results. The novel high-throughput technology has been validated by separating brain cancer and non-cancer with balanced accuracies of 90% which is comparable to the traditional fixed diamond crystal methodology. Furthermore, the differentiation of brain tumour type could be useful for neurologists, as some are difficult to distinguish through medical imaging alone. For example, the highly aggressive glioblastoma multiforme and primary cerebral lymphoma can appear similar on magnetic resonance imaging (MRI) scans, thus are often misdiagnosed. Here, we report the ability of infrared spectroscopy to distinguish between glioblastoma and lymphoma patients, at a sensitivity and specificity of 90.1% and 86.3%, respectively. A reliable serum diagnostic test could avoid the need for surgery and speed up time to definitive chemotherapy and radiotherapy.