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Reducing Misclassification Bias in Cervical Dysplasia Risk Factor Analysis With p16-Based Diagnoses

Meserve, Emily MD, MPH1; Berlin, Michelle MD, MPH2; Mori, Tomi PhD3; Krum, Robert MD4; Morgan, Terry K. MD, PhD1,2

Journal of Lower Genital Tract Disease:
doi: 10.1097/LGT.0000000000000001
Basic Science
Abstract

Objective: Conventional hematoxylin and eosin (HE)–based diagnoses have been the reference standard for cervical cancer risk factor analyses. However, this HE-based method is known to have modest interobserver reproducibility and only moderate predictive value. In contrast, more recent immunohistochemical-based diagnoses using the neoplastic marker p16 are known to improve diagnostic accuracy. Our objective was to test whether p16-based diagnoses would significantly affect high-grade dysplasia (cervical intraepithelial neoplasia 2+) risk factor analysis compared with the current reference standard (HE).

Materials and Methods: Retrospective cohort of 500 index cases were randomly selected from a series of more than 5,000 cervical biopsies performed at Kaiser Permanente Northwest from 1997 to 2003 after a patient’s first abnormal cervical Pap smear (positive for atypical squamous cells of undetermined significance). Subjects were subsequently excluded if they did not have at least 5 years of clinical follow-up, including cervical biopsies, or 3 reproducibly negative Pap smears. This yielded 358 cases for risk factor analysis. The index biopsies and all follow-up biopsies were immunostained for p16 and the proliferation marker Ki-67, which were then independently reviewed by 2 pathologists blinded to clinical outcomes. Data were analyzed by χ2 test and logistic regression modeling.

Results: We observed clinically significant diagnostic errors in 22% of index biopsies. Improved accuracy using p16 strengthened the risk estimate of low family income for cervical intraepithelial neoplasia 2+ (odds ratio = 1.71, 95% confidence interval = 1.09–2.63) compared with HE-based diagnoses (odds ratio = 1.12, 95% confidence interval = 0.72–1.72). The addition of Ki-67 staining did not significantly influence these results.

Conclusions: p16-based diagnoses may affect the power of risk factor analysis, especially when using small cohorts.

In Brief

p16-based diagnoses affect cervical cancer risk factor analysis.

Author Information

Departments of 1Pathology, 2Obstetrics & Gynecology, and 3Medical Informatics & Clinical Epidemiology at Oregon Health & Science University; and 4Pathology at Kaiser Permanente Northwest, Portland, OR

Reprint requests to: Terry K. Morgan, MD, PhD, Departments of Pathology and Obstetrics & Gynecology, Oregon Health & Science University, 3181 SW Sam Jackson, Mail Code L471, Portland, OR 97239. E-mail: morgante@ohsu.edu

The authors have declared they have no conflicts of interest.

This publication was made possible with support from the Oregon Clinical and Translational Research Institute (OCTRI), grant number TL1 RR024159 from the National Center for Advancing Translational Sciences, a component of the National Institutes of Health (NIH), and NIH Roadmap for Medical Research.

Copyright © 2014 by the American Society for Colposcopy and Cervical Pathology