Feature driven classification of Raman spectra for real-time spectral brain tumour diagnosis using sound. Issue 1 (19th October 2016)

• Record Type: Journal Article
• Authors: Stables, Ryan; Clemens, Graeme; Butler, Holly J.; Ashton, Katherine M.; Brodbelt, Andrew; Dawson, Timothy P.; Fullwood, Leanne M.; Jenkinson, Michael D.; Baker, Matthew J.
• Is Part Of: Analyst. Volume 142:Issue 1(2017)
• Page Start: 98
• Page End: 109
• Publication Date: 2016-10-19
• Subjects: Chemistry, Analytic -- Periodicals; 543
• DOI: 10.1039/c6an01583b
• Languages: English
• ISSNs: 0003-2654

Abstract

Abstract : Spectroscopic diagnostics have been shown to be an effective tool for the analysis and discrimination of disease states from human tissue. Abstract : Spectroscopic diagnostics have been shown to be an effective tool for the analysis and discrimination of disease states from human tissue. Furthermore, Raman spectroscopic probes are of particular interest as they allow for in vivo spectroscopic diagnostics, for tasks such as the identification of tumour margins during surgery. In this study, we investigate a feature-driven approach to the classification of metastatic brain cancer, glioblastoma (GB) and non-cancer from tissue samples, and we provide a real-time feedback method for endoscopic diagnostics using sound. To do this, we first evaluate the sensitivity and specificity of three classifiers (SVM, KNN and LDA), when trained with both sub-band spectral features and principal components taken directly from Raman spectra. We demonstrate that the feature extraction approach provides an increase in classification accuracy of 26.25% for SVM and 25% for KNN. We then discuss the molecular assignment of the most salient sub-bands in the dataset. The most salient sub-band features are mapped to parameters of a frequency modulation (FM) synthesizer in order to generate audio clips from each tissue sample. Based on the properties of the sub-band features, the synthesizer was able to maintain similar sound timbres within the disease classes and provide different timbres between disease classes. This was reinforced via listening tests, in which participants were able to discriminate between classes with mean classification accuracy of 71.1%. Providing intuitive feedback via sound frees the surgeons' visual attention to remain on the patient, allowing for greater control over diagnostic and surgical tools during surgery, and thus promoting clinical translation of spectroscopic diagnostics.