Automated analysis of fluorescent in situ hybridization (FISH) labeled genetic biomarkers in assisting cervical cancer diagnosis.
The numerical and/or structural deviation of some chromosomes (i.e., monosomy and _polysomy of chromosomes 3 and X) are routinely used as positive genetic biomarkers to diagnose cervical cancer and predict the disease progression. Among the available diagnostic methods to analyze the aneusomy of chromosomes 3 and X, fluorescence in situ hybridization (FISH) technology has demonstrated significant advantages in assisting clinicians to more accurately detect and diagnose cervical carcinoma at an early stage, in particular for the women at a high risk for progression of low-grade and high-grade squamous intra-epithelium lesions (LSIL and HSIL). In order to increase the diagnostic accuracy, consistency, and efficiency from that of manual FISH analysis, this study aims to develop and test an automated FISH analysis method that includes a two-stage scheme. In the first stage, an interactive multiple-threshold algorithm is utilized to segment potential interphase nuclei candidates distributed in different intensity levels and a rule-based classifier is implemented to identify analyzable interphase cells. In the second stage, FISH labeled biomarker spots of chromosomes 3 and X are segmented by a top-hat transform. The independent FISH spots are then detected by a knowledge-based classifier, which enables recognition of the splitting and stringy FISH signals. Finally, the ratio of abnormal interphase cells with numerical changes of chromosomes 3 and X is calculated to detect positive cases. The experimental results of four test cases showed high agreement of FISH analysis results between the automated scheme and the cytogeneticist's analysis including 92.7% to 98.7% agreement in cell segmentation and 4.4% to 11.0% difference in cell classification. This preliminary study demonstrates the feasibility of potentially applying the automatic FISH analysis method to expedite the screening and detecting cervical cancer at an early stage.
Wang
Xingwei
X
Department of Radiology, University of Pittsburgh Medical Center, Pittsburgh, PA, 15213, USA.
Zheng
Bin
B
Zhang
Roy R
RR
Li
Shibo
S
Chen
Xiaodong
X
Mulvihill
John J
JJ
Lu
Xianglan
X
Pang
Hui
H
Liu
Hong
H
D001331:Automation; D014408:Biomarkers, Tumor / Q000032:analysis* / Q000235:genetics; D005260:Female; D006801:Humans; D017404:In Situ Hybridization, Fluorescence / Q000379:methods*; D002583:Uterine Cervical Neoplasms / Q000175:diagnosis* / Q000235:genetics*; D014626:Vaginal Smears; D002578:Uterine Cervical Dysplasia / Q000175:diagnosis / Q000235:genetics
D014408:Biomarkers, Tumor