Machine learning interpretation of extended human papillomavirus genotyping by Onclarity in an Asian cervical cancer screening population

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.