Development of a multivariate statistical algorithm to analyze human cervical tissue fluorescence spectra acquired in vivo.
OBJECTIVE
A general multivariate statistical algorithm has been developed to analyze the diagnostic content of cervical tissue fluorescence spectra acquired in vivo.


METHODS
The primary steps of the algorithm are to: (1) preprocess the data to reduce inter-patient and intra-patient variation of tissue spectra within a diagnostic category, without a priori information, (2) dimensionally reduce the preprocessed fluorescence emission spectrum with minimal information loss and use it to select the minimum number of the original emission variables of the fluorescence spectrum required to achieve classification with negligible decrease in predictive ability, and (3) assign a posterior probability to the diagnosis of each sample, so that samples with relative uncertain diagnosis can be reevaluated by a clinician. The algorithm was tested retrospectively and prospectively on cervical tissue spectra acquired from 476 sites from 92 patients at 337 nm excitation.


RESULTS
The algorithm based on the entire fluorescence spectrum differentiates squamous intraepithelial lesions (SILs) from normal squamous epithelia and inflammation with an average sensitivity and specificity of 88% +/- 1.4 and 70% +/- 1, respectively. The average sensitivity and specificity of the identical algorithm based on intensity selected at only two emission wavelengths is 88% +/- 1.4 and 71% +/- 1.4, respectively.


CONCLUSIONS
The multivariate statistical algorithm based on both types of spectral inputs at 337 nm excitation has a similar sensitivity and significantly improved specificity relative to colposcopy in expert hands.
Ramanujam
N
N
Biomedical Engineering Program, University of Texas, Austin 78705, USA.
Mitchell
M F
MF
Mahadevan
A
A
Thomsen
S
S
Malpica
A
A
Wright
T
T
Atkinson
N
N
Richards-Kortum
R
R
D000465:Algorithms; D001185:Artificial Intelligence; D002278:Carcinoma in Situ / Q000175:diagnosis; D002584:Cervix Uteri / Q000473:pathology*; D003127:Colposcopy; D003936:Diagnosis, Computer-Assisted; D016002:Discriminant Analysis; D004848:Epithelium / Q000473:pathology; D005260:Female; D005544:Forecasting; D006801:Humans; D007091:Image Processing, Computer-Assisted; D007834:Lasers; D016015:Logistic Models; D015999:Multivariate Analysis; D011336:Probability; D011446:Prospective Studies; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D013050:Spectrometry, Fluorescence / Q000706:statistics & numerical data; D002577:Uterine Cervical Diseases / Q000175:diagnosis*; D002583:Uterine Cervical Neoplasms / Q000175:diagnosis; D002575:Uterine Cervicitis / Q000175:diagnosis; D002578:Uterine Cervical Dysplasia / Q000175:diagnosis