Diffuse reflectance patterns in cervical spectroscopy.
OBJECTIVE
Our laboratory seeks to develop minimally invasive cost-effective methods to improve screening and detection of curable precursors to cervical cancer. Previously, we have presented pilot studies that assess the diagnostic power of auto-fluorescence and diffuse reflectance spectroscopy. In the present study, we evaluate diffuse reflectance spectra from a comprehensive 850 patient clinical trial to determine its ability to discriminate normal tissue from several grades of abnormal cervical tissue.


METHODS
Diffuse reflectance spectra at four source detector separations measured from 549 cervical sites were available for analysis. Three classifiers were implemented: one used spectral data directly as input, a second used simple spectral features such as peak position and intensity, and one used principal component analysis for feature selection. Algorithms were developed and evaluated using leave-one-out cross-validation to classify normal and precancerous cervical tissue. The percentage of samples correctly classified was used to evaluate and compare the performance of the algorithms, as compared to histology.


RESULTS
Diffuse reflectance spectra of cervical precancer showed consistent differences from that of normal tissue at all source detector separations; reflectance intensity of precancer was lower than that of normal tissue on average. Normal cervical tissue spectra show more intensity variation between patients than other tissue grades. Reflectance spectra acquired from the closest source detector separations consistently demonstrated the most relevant information for tissue classification. Two persistent spectral patterns demonstrated that the contribution of hemoglobin absorption and the wavelength-dependent spectral slope contained relevant information for classification.


CONCLUSIONS
Spectral patterns in diffuse reflectance spectra can be used for the discrimination of normal cervical tissue from low grade and high grade squamous intraepithelial lesions.
Marín
Nena M
NM
Department of Biomedical Engineering, University of Texas, 2501 Speedway Street, University of Texas, Austin, TX 78712, USA.
Milbourne
Andrea
A
Rhodes
Helen
H
Ehlen
Thomas
T
Miller
Dianne
D
Benedet
Lou
L
Richards-Kortum
Rebecca
R
Follen
Michele
M
D000465:Algorithms; D016002:Discriminant Analysis; D005240:Feasibility Studies; D005260:Female; D006801:Humans; D011230:Precancerous Conditions / Q000175:diagnosis* / Q000473:pathology; D025341:Principal Component Analysis; D013057:Spectrum Analysis / Q000379:methods*; D002583:Uterine Cervical Neoplasms / Q000175:diagnosis* / Q000473:pathology; D002578:Uterine Cervical Dysplasia / Q000175:diagnosis* / Q000473:pathology