Automated Detection of Dual p16/Ki67 Nuclear Immunoreactivity in Liquid-Based Pap Tests for Improved Cervical Cancer Risk Stratification
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The Papanicolau (Pap) test is a routine cytological procedure for early detection of dysplastic lesions in cervical epithelium. A reliable screening method is crucial for triage of women at risk; however manual screening and interpretation are associated with relatively low sensitivity and substantial interobserver diagnostic variability. P16 and Ki67 biomarkers have been recently proposed as adjunctive tools in the diagnosis of high-risk human papillomavirus (hrHPV) associated dysplasias to supplement the morphological characteristics of cells by additional colorimetric features. In this study, an automated technique for the evaluation of dual p16/Ki67 immunoreactivity in cervical cell nuclei is introduced. Smears stained with p16 and Ki67 antibodies were digitized, and analyzed by algorithms we developed. Gradient-based radial symmetry operator and adaptive processing of symmetry image were employed to obtain the nuclear mask. This step was followed by the extraction of features including pixel data and immunoreactivity signature from each nucleus. The features were analyzed by two support vector machine classifiers to assign a nucleus into one of four types of immunoreactivity: p16 positive (p16+/Ki67−), Ki67 positive (p16−/Ki67+), dual p16/Ki67 positive (p16+/Ki67+) and negative (p16−/Ki67−), respectively. Results obtained by our method correlated well with readings by two cytopathologists (n = 18,068 cells); p16+/Ki67+ nuclei were classified with respective precisions of 77.1% and 82.6%. Specificity in identification of p16−/Ki67− nuclei was better than 99.5%, and the sensitivity in detection of all immunopositive nuclei was 86.3 and 89.4%, respectively. We found that the quantitative characterization of immunoreactivity provided by the additional highlighting of classified nuclei can positively impact the efficacy and screening outcome of the Pap test.
KeywordsPap test Cervical cancer screening Immunocytochemistry Computer analysis Nuclei segmentation Quantification
This work was supported in part by a grant from the Department of Surgery at Cedars-Sinai Medical Center, and in part by a NIH grant 5R21CA143618-02 (to AG). We also thank Dr Hunter Hardy M.D. for technical help in specimen imaging.
Conflict of interest
The authors declare that they have no conflict of interest.
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