Enhancement of early cervical cancer diagnosis with epithelial layer analysis of fluorescence lifetime images

This work reports the use of layer analysis to aid the fluorescence lifetime diagnosis of cervical intraepithelial neoplasia (CIN) from H&E stained cervical tissue sections. The mean and standard deviation of lifetimes in single region of interest (ROI) of cervical epithelium were previously sho...

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Bibliographic Details
Published in:PloS one 2015-05, Vol.10 (5), p.e0125706-e0125706
Main Authors: Gu, Jun, Fu, Chit Yaw, Ng, Beng Koon, Liu, Lin Bo, Lim-Tan, Soo Kim, Lee, Caroline Guat Lay
Format: Article
Language:English
Subjects:
Cancer
Cancer diagnosis
Cervical cancer
Cervical Intraepithelial Neoplasia - pathology
Cervix
Diagnosis
Early Detection of Cancer - methods
Engineering
Epithelial cells
Epithelium
Female
Fluorescence
Handbooks
Health sciences
Human papillomavirus
Humans
Image Interpretation, Computer-Assisted - methods
Learning algorithms
Lifetime
Medical diagnosis
Medical screening
Microscopy, Fluorescence - methods
Morphology
Neural networks
Pathology
Photonics
Sensitivity analysis
Sensitivity and Specificity
Uterine Cervical Neoplasms - pathology
Womens health
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Summary:This work reports the use of layer analysis to aid the fluorescence lifetime diagnosis of cervical intraepithelial neoplasia (CIN) from H&E stained cervical tissue sections. The mean and standard deviation of lifetimes in single region of interest (ROI) of cervical epithelium were previously shown to correlate to the gold standard histopathological classification of early cervical cancer. These previously defined single ROIs were evenly divided into layers for analysis. A 10-layer model revealed a steady increase in fluorescence lifetime from the inner to the outer epithelial layers of healthy tissue sections, suggesting a close association with cellular maturity. The shorter lifetime and minimal lifetime increase towards the epithelial surface of CIN-affected regions are in good agreement with the absence of cellular maturation in CIN. Mean layer lifetimes in the top-half cervical epithelium were used as feature vectors for extreme learning machine (ELM) classifier discriminations. It was found that the proposed layer analysis technique greatly improves the sensitivity and specificity to 94.6% and 84.3%, respectively, which can better supplement the traditional gold standard cervical histopathological examinations.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0125706