Biofluid diagnostics by FTIR spectroscopy: A platform technology for cancer detection
Fourier Transform Infrared Spectroscopy (FTIR) has been largely employed by scientific researchers to improve diagnosis and treatment of cancer, using various biofluids and tissues. The technology has proved to be easy to use, rapid and cost-effective for analysis on human blood serum to discriminat...
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Published in | Cancer letters Vol. 477; pp. 122 - 130 |
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Main Authors | , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
Ireland
Elsevier B.V
01.05.2020
Elsevier Limited |
Subjects | |
Online Access | Get full text |
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Summary: | Fourier Transform Infrared Spectroscopy (FTIR) has been largely employed by scientific researchers to improve diagnosis and treatment of cancer, using various biofluids and tissues. The technology has proved to be easy to use, rapid and cost-effective for analysis on human blood serum to discriminate between cancer versus healthy control samples. The high sensitivity and specificity achievable during samples classification aided by machine learning algorithms, offers an opportunity to transform cancer referral pathways, as it has been demonstrated in a unique and recent prospective clinical validation study on brain tumours. We herein highlight the importance of early detection in cancer research using FTIR, discussing the technique, the suitability of serum for analysis and previous studies, with special focus on pre-clinical factors and clinical translation requirements and development.
•A simple, rapid, cost-effective and accurate test is needed in the clinical environment.•ATR-FTIR analysis of biofluids has obtained remarkable statistical results with the aid of machine learning algorithms.•Clinical spectroscopy is in continuous development to achieve translation into the clinic.•A proof-of-concept study on detection of brain tumours has recently progressed to a prospective clinical validation study. |
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Bibliography: | ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-3 content type line 23 ObjectType-Review-1 |
ISSN: | 0304-3835 1872-7980 |
DOI: | 10.1016/j.canlet.2020.02.020 |