Brain Metastases

Soffietti, Riccardo; Cornu, Philippe; Delattre, Jean‐Yves; Grant, Robin; Graus, Francesc; Grisold, Wolfgang; Heimans, Jan J.; Hildebrand, Janet S.; Hoskin, P.; Kalljo, M.; Krauseneck, P.; Marosi, Christine; Siegal, Tali; Vecht, Charles J.

doi:10.1002/9780470753279.ch33

Cited by 1 publication

(1 citation statement)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Around 13,000 people in the UK are diagnosed with brain cancer every year, of which, 60% are metastatic tumours, which have originated from primary cancers outside the CNS 4 . The major primary tumours that metastasise to the brain are lung (50%), breast (15 -25%) melanoma (5 -20%) and all others (5 -30%) 5 . The identification of the organ of origin increases the efficiency of treatment and patient survival, however in approx.…”

Section: Introductionmentioning

confidence: 99%

Feature driven classification of Raman spectra for real-time spectral brain tumour diagnosis using sound

Stables

Clemens

Butler

et al. 2017

Analyst

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%