Establishment of early diagnosis models for cervical precancerous lesions using large-scale cervical cancer screening datasets.

ABSTRACT:

Background

Human papilloma virus (HPV) DNA test was applied in cervical cancer screening as an effective cancer prevention strategy. The viral load of HPV generated by different assays attracted increasing attention on its potential value in disease diagnosis and progression discovery.

Methods

In this study, three HPV testing datasets were assessed and compared, including Hybrid Capture 2 (n = 31,954), Aptima HPV E6E7 (n = 3269) and HPV Cobas 4800 (n = 13,342). Logistic regression models for diagnosing early cervical lesions of the three datasets were established and compared. The best variable factor combination (VL + BV) and dataset (HC2) were used for the establishment of six machine learning models. Models were evaluated and compared, and the best-performed model was val

SUBMITTER: Meng B 

PROVIDER: S-EPMC9636682 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature