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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Lasers in Surgery an...arrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
Lasers in Surgery and Medicine
Article . 2012 . Peer-reviewed
License: Wiley Online Library User Agreement
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Parallel factor analysis of ovarian autofluorescence as a cancer diagnostic

Authors: Ronie, George; Michalis, Michaelides; Molly A, Brewer; Urs, Utzinger;

Parallel factor analysis of ovarian autofluorescence as a cancer diagnostic

Abstract

AbstractBackground and ObjectivesEndogenous fluorescence from certain amino acids, structural proteins, and enzymatic co‐factors in tissue is altered by carcinogenesis. We evaluate the potential of these changes in fluorescence to predict a diagnosis of malignancy and to estimate the risk of developing ovarian cancer.Study Design/Materials and MethodsOvarian biopsies were interrogated over 270–550 nm excitation and fluorescence was collected from 290 to 700 nm. Two hundred forty‐nine measurements were performed on 49 IRB‐consented patients undergoing oophorectomy. Data are analyzed using parallel factor analysis to determine excitation and emission spectra of the underlying fluorophores that contribute to the total detected fluorescence intensity.ResultsUsing multivariate normal distribution fits and cross‐validation techniques, sensitivity and specificity of 88% and 93%, respectively, are achieved when classifying malignant samples versus others, while 88% and 80%, respectively, are achieved when classifying normal post‐menopausal patients as being either at high‐ or low‐risk of developing ovarian cancer based on their personal and family history of cancer. Performance of classifying cancer increases when the normal group does not include benign neoplasm and endometriosis samples. Performance of high‐ versus low‐risk classification decreases when normal samples include both pre‐ and post‐menopausal women. Excitation over 270–400 and 380–560 nm, respectively, have the best diagnostic performance for cancer detection and risk‐status assessment.ConclusionsAssessing the endogenous fluorescence could be useful in screening women at increased risk of developing ovarian cancer. Lasers Surg. Med. 44:282–295, 2012. © 2012 Wiley Periodicals, Inc.

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Keywords

Adult, Aged, 80 and over, Ovarian Neoplasms, Models, Statistical, Adolescent, Biopsy, Ovary, Middle Aged, Risk Assessment, Sensitivity and Specificity, Fluorescence, Young Adult, Spectrometry, Fluorescence, Multivariate Analysis, Humans, Female, Factor Analysis, Statistical, Algorithms, Aged

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
21
Average
Top 10%
Top 10%
Related to Research communities
Cancer Research
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