Discrimination of fresh frozen non-tumour and tumour brain tissue using spectrochemical analyses and a classification model

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Bury, Danielle and Morais, Camilo L.M. and Martin, Francis L. and Lima, Kássio M. G. and Ashton, Katherine M. and Baker, Matthew J. and Dawson, Timothy P. (2019) Discrimination of fresh frozen non-tumour and tumour brain tissue using spectrochemical analyses and a classification model. British Journal of Neurosurgery. ISSN 1360-046X (https://doi.org/10.1080/02688697.2019.1679352)

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Abstract

Introduction: In order for brain tumours to be successfully treated, maximal resection is beneficial. A method to detect infiltrative tumour edges intraoperatively, improving on current methods would be clinically useful. Vibrational spectroscopy offers the potential to provide a handheld, reagent-free method for tumour detection. Purpose: This study was designed to determine the ability of both Raman and Fourier-transform infrared (FTIR) spectroscopy towards differentiating between normal brain tissue, glioma or meningioma. Method: Unfixed brain tissue, which had previously only been frozen, comprising normal, glioma or meningioma tissue was placed onto calcium fluoride slides for analysis using Raman and attenuated total reflection (ATR)-FTIR spectroscopy. Matched haematoxylin and eosin slides were used to confirm tumour areas. Analyses were then conducted to generate a classification model. Results: This study demonstrates the ability of both Raman and ATR-FTIR spectroscopy to discriminate tumour from non-tumour fresh frozen brain tissue with 94% and 97.2% of cases correctly classified, with sensitivities of 98.8% and 100%, respectively. This decreases when spectroscopy is used to determine tumour type. Conclusion: The study demonstrates the ability of both Raman and ATR-FTIR spectroscopy to detect tumour tissue from non-tumour brain tissue with a high degree of accuracy. This demonstrates the ability of spectroscopy when targeted for a cancer diagnosis. However, further improvement would be required for a classification model to determine tumour type using this technology, in order to make this tool clinically viable.

ORCID iDs

Bury, Danielle, Morais, Camilo L.M., Martin, Francis L., Lima, Kássio M. G., Ashton, Katherine M., Baker, Matthew J. ORCID logoORCID: https://orcid.org/0000-0003-2362-8581 and Dawson, Timothy P.;
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