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ATR-FTIR spectroscopy coupled with chemometric analysis discriminates normal, borderline and malignant ovarian tissue: classifying subtypes of human cancer

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<mark>Journal publication date</mark>2016
<mark>Journal</mark>The Analyst
Issue number2
Volume2016
Number of pages10
Pages (from-to)585-594
Publication StatusPublished
Early online date12/06/15
<mark>Original language</mark>English

Abstract

Surgical management of ovarian tumours largely depends on their histo-pathological diagnosis. Currently, screening for ovarian malignancy with tumour markers in conjunction with radiological investigations has a low specificity for discriminating benign from malignant tumours. Also, pre-operative biopsy of ovarian masses increases the risk of intra-peritoneal dissemination of malignancy. Intra-operative frozen section, although sufficiently accurate in differentiating tumours according to their histological type, increases operation times. This results in increased surgery-related risks to the patient and additional burden to resource allocation. We set out to determine whether attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy, combined with chemometric analysis can be applied to discriminate between normal, borderline and malignant ovarian tumours and classify ovarian carcinoma subtypes according to the unique spectral signatures of their molecular composition. Formalin-fixed, paraffin-embedded ovarian tissue blocks were de-waxed, mounted on Low-E slides and desiccated before being analysed using ATR-FTIR spectroscopy. Chemometric analysis in the form of principal component analysis (PCA), successive projection algorithm (SPA) and genetic algorithm (GA), followed by linear discriminant analysis (LDA) of the obtained spectra revealed clear segregation between benign versus borderline versus malignant tumours as well as segregation between different histological tumour subtypes, when these approaches are used in combination. ATR-FTIR spectroscopy coupled with chemometric analysis has the potential to provide a novel diagnostic approach in the accurate diagnosis of ovarian tumours assisting surgical decision making to avoid under-treatment or over-treatment, with minimal impact to the patient.