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Cervical fluorescence of normal women
Author(s) -
Brookner Carrie K.,
Utzinger Urs,
Staerkel Gregg,
RichardsKortum Rebecca,
Mitchell Michele Follen
Publication year - 1999
Publication title -
lasers in surgery and medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.888
H-Index - 112
eISSN - 1096-9101
pISSN - 0196-8092
DOI - 10.1002/(sici)1096-9101(1999)24:1<29::aid-lsm6>3.0.co;2-h
Subject(s) - dysplasia , papanicolaou stain , medicine , cervical intraepithelial neoplasia , principal component analysis , cervix , papanicolaou test , radiology , pathology , cervical cancer , gynecology , cancer , artificial intelligence , computer science
Background and Objective Cervical tissue fluorescence spectra have previously been measured in vivo in women with a recent abnormal Papanicolaou smear. Diagnostic algorithms have been developed to diagnose squamous intraepithelial lesions (SILs) based on these fluorescence emission spectra. However, algorithms have not been tested in women with no history of cervical neoplasia. Study Design/Materials and Methods Cervical fluorescence was measured from 54 women with no history of cervical dysplasia, and the spectra were compared to those from colposcopically normal sites in women with suspected dysplasia. Representative spectra from each group were compared and a two‐sided, unpaired Student's t ‐test was performed to compare mean principal component scores used in previously published diagnostic algorithms. The ability of previously reported diagnostic algorithms to classify these samples as normal tissue was also assessed. Results At the 0.05 level of significance, the mean scores of 4 of the 7 important principal components were statistically different for the two populations. However, when the data collected from volunteers in this study were preprocessed in the appropriate manner and the algorithms were applied, more normal samples were correctly classified than in the previous clinical study in which these algorithms were developed. Conclusion Previously reported algorithms can accurately classify tissue type based on spectra from women with and without a history of cervical neoplasia. Lasers Surg. Med. 24:29–37, 1999. © 1999 Wiley‐Liss, Inc.