In a new study, a computer algorithm improved the accuracy and efficiency of cervical cancer screening compared with cytology (Pap test), which is the current standard for follow-up of women who test positive with primary human papillomavirus (HPV) screening. The new approach uses artificial intelligence (AI) to automate dual-stain evaluation and has clear implications for clinical care, according to a press release from the National Institutes of Health (NIH).
Findings from the study were published in the Journal of the National Cancer Institute (NCI). The algorithm was developed, and the study conducted by investigators at the NCI, part of the NIH, in collaboration with researchers from several other institutions.
In recent years, clinicians have hoped to take advantage of advances in digital imaging and machine learning to improve cervical cancer screening. Women who test negative for HPV are at low risk for cervical cancer for the following decade, and even most cervical HPV infections – which cause positive HPV tests – will not result in precancer. The challenge is to identify which women with positive HPV test results are most likely to have precancerous changes in their cervical cells and should, therefore, have a colposcopy to examine the cervix and take samples for biopsy, or who need immediate treatment.
Currently, women with positive HPV tests may have additional HPV tests or Pap cytology tests to assess the need for colposcopy, biopsy, or treatment. Pap cytology, in which specially trained laboratory professionals (cytotechnologists) analyze stained slides to look for abnormal cells, is used to find precancers before they progress to cancer. But these approaches are not ideal. For example, Pap cytology tests are time consuming, not very sensitive, and prone to false-positive findings.
In the new study, the investigators wanted to see if a fully automated dual-stain test could match or exceed the performance of the manual approach. In collaboration with Niels Grabe, PhD, and Bernd Lahrmann, PhD, of the Steinbeis Transfer Center for Medical Systems Biology, which is associated with the University of Heidelberg, they developed a whole-slide imaging platform that, after being trained with deep learning, could determine if any cervical cells were stained for both p16 and Ki-67. They compared this method with both conventional Pap cytology and manual dual-stain testing in samples from a total of 4,253 people participating in one of three epidemiological studies of HPV-positive cervical and anal precancers at Kaiser Permanente Northern California and the University of Oklahoma.
The researchers found that the AI-based dual-stain test had a lower rate of positive tests than both Pap cytology and manual dual-stain, with better sensitivity (the ability to correctly identify precancers) and substantially higher specificity (the ability to correctly identify those without precancers) than Pap cytology. AI-based dual-stain reduced referral to colposcopy by about a third compared with Pap (approximately 42 percent vs. 60 percent). The testing method was also robust, showing comparable performance in anal cytology.
