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In Journal of clinical microbiology

This study aimed (1) to compare the performance of the BD Onclarity HPV assay with Cobas HPV test in identifying cervical intraepithelial neoplasia 2/3 or above (CIN2/3+) in an Asian screening population; and (2) to explore improving 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 co-infection 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 ASC-US. Positivity in both HPV16 and HPV33/58 of the Onclarity channels entails 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 can therefore help to highlight patients having highest risk of developing CIN2/3+, with potential to reduce unnecessary colposcopy and negative psychosocial impact on women receiving the reports.

Wong Oscar G W, Ng Idy F Y, Tsun Obe K L, Pang Herbert H, Ip Philip P C, Cheung Annie N Y

2019-Sep-11