Machine Learning Interpretation of Extended Human Papillomavirus Genotyping by Onclarity in an Asian Cervical Cancer Screening Population.
Journal:
Journal of clinical microbiology
PMID:
31511337
Abstract
This study aimed (i) to compare the performance of the BD Onclarity human papillomavirus (HPV) assay with the Cobas HPV test in identifying cervical intraepithelial neoplasia 2/3 or above (CIN2/3+) in an Asian screening population and (ii) to explore improving the cervical cancer detection specificity of Onclarity by machine learning. We tested 605 stratified random archived samples of cervical liquid-based cytology samples with both assays. All samples had biopsy diagnosis or repeated negative cytology follow-up. Association rule mining (ARM) was employed to discover coinfection likely to give rise to CIN2/3+. Outcome classifiers interpreting the extended genotyping results of Onclarity were built with different underlying models. The sensitivities (Onclarity, 96.32%; Cobas, 95.71%) and specificities (Onclarity, 46.38%; Cobas, 45.25%) of the high-risk HPV (hrHPV) components of the two tests were not significantly different. When HPV16 and HPV18 were used to further interpret hrHPV-positive cases, Onclarity displayed significantly higher specificity (Onclarity, 87.10%; Cobas, 80.77%). Both hrHPV tests achieved the same sensitivities (Onclarity, 90.91%; Cobas, 90.91%) and similar specificities (Onclarity, 48.46%; Cobas, 51.98%) when used for triaging atypical squamous cells of undetermined significance. Positivity in both HPV16 and HPV33/58 of the Onclarity channels entails the highest probability of developing CIN2/3+. Incorporating other hrHPVs into the outcome classifiers improved the specificity of identifying CIN2/3 to up to 94.32%. The extended genotyping of Onclarity therefore can help to highlight patients having the highest risk of developing CIN2/3+, with the potential to reduce unnecessary colposcopy and negative psychosocial impact on women receiving the reports.