Stables Ryan, Clemens Graeme, Butler Holly J, Ashton Katherine M, Brodbelt Andrew, Dawson Timothy P, Fullwood Leanne M, Jenkinson Michael D, Baker Matthew J
Digital Media Technology Laboratory, Millennium Point, City Centre Campus Birmingham City University, West Midlands, B47XG, UK.
WestCHEM, Department of Pure and Applied Chemistry, University of Strathclyde, Technology and Innovation Centre, 99 George Street, Glasgow, G11RD, UK.
Analyst. 2016 Dec 19;142(1):98-109. doi: 10.1039/c6an01583b.
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.
光谱诊断已被证明是分析和鉴别人体组织疾病状态的有效工具。此外，拉曼光谱探针特别受关注，因为它们可用于体内光谱诊断，比如在手术中识别肿瘤边缘等任务。在本研究中，我们探究了一种基于特征驱动的方法，用于对组织样本中的转移性脑癌、胶质母细胞瘤（GB）和非癌组织进行分类，并提供了一种使用声音进行内镜诊断的实时反馈方法。为此，我们首先评估了三种分类器（支持向量机、K近邻和线性判别分析）在使用子带光谱特征和直接从拉曼光谱获取的主成分进行训练时的灵敏度和特异性。我们证明，特征提取方法使支持向量机的分类准确率提高了26.25%，使K近邻的分类准确率提高了25%。然后我们讨论了数据集中最显著子带的分子归属。将最显著的子带特征映射到调频（FM）合成器的参数上，以便从每个组织样本生成音频片段。基于子带特征的特性，合成器能够在疾病类别内保持相似的音色，并在不同疾病类别之间提供不同的音色。这通过听力测试得到了加强，在测试中参与者能够区分不同类别，平均分类准确率为71.1%。通过声音提供直观反馈可使外科医生将视觉注意力集中在患者身上，从而在手术过程中更好地控制诊断和手术工具，进而推动光谱诊断的临床应用。
