Literature DB >> 26090781

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

Georgios Theophilou1, Kássio M G Lima2, Pierre L Martin-Hirsch1, Helen F Stringfellow3, Francis L Martin4.   

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.

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Year:  2016        PMID: 26090781     DOI: 10.1039/c5an00939a

Source DB:  PubMed          Journal:  Analyst        ISSN: 0003-2654            Impact factor:   4.616


  15 in total

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Journal:  Sci Rep       Date:  2016-07-12       Impact factor: 4.379

5.  An imaging dataset of cervical cells using scanning near-field optical microscopy coupled to an infrared free electron laser.

Authors:  Diane E Halliwell; Camilo L M Morais; Kássio M G Lima; Júlio Trevisan; Michele R F Siggel-King; Tim Craig; James Ingham; David S Martin; Kelly Heys; Maria Kyrgiou; Anita Mitra; Evangelos Paraskevaidis; Georgios Theophilou; Pierre L Martin-Hirsch; Antonio Cricenti; Marco Luce; Peter Weightman; Francis L Martin
Journal:  Sci Data       Date:  2017-07-11       Impact factor: 6.444

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Authors:  Hemendra Ghimire; Mahathi Venkataramani; Zhen Bian; Yuan Liu; A G Unil Perera
Journal:  Sci Rep       Date:  2017-12-05       Impact factor: 4.379

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8.  Assessing Binary Mixture Effects from Genotoxic and Endocrine Disrupting Environmental Contaminants Using Infrared Spectroscopy.

Authors:  Camilo L M Morais; Richard F Shore; M Glória Pereira; Francis L Martin
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Authors:  Daniel L D Freitas; Ingrid M Câmara; Priscila P Silva; Nathália R S Wanderley; Maria B C Alves; Camilo L M Morais; Francis L Martin; Tirzah B P Lajus; Kassio M G Lima
Journal:  Sci Rep       Date:  2020-07-30       Impact factor: 4.379

10.  Characterization of ovarian cancer cells and tissues by Fourier transform infrared spectroscopy.

Authors:  Lei Li; Xiaoning Bi; Hengzi Sun; Simiao Liu; Mei Yu; Ying Zhang; Shifu Weng; Limin Yang; Yanan Bao; Jinguang Wu; Yizhuang Xu; Keng Shen
Journal:  J Ovarian Res       Date:  2018-08-02       Impact factor: 4.234

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