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Yonsei Med J. 2014 Mar;55(2):331-8. doi: 10.3349/ymj.2014.55.2.331.

Diagnostic algorithm to reflect regressive changes of human papilloma virus in tissue biopsies.

Author information

1
Department of Pathology, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 120-752, Korea. cho1988@yuhs.ac.

Abstract

PURPOSE:

Landmark indicators have not yet to be developed to detect the regression of cervical intraepithelial neoplasia (CIN). We propose that quantitative viral load and indicative histological criteria can be used to differentiate between atypical squamous cells of undetermined significance (ASCUS) and a CIN of grade 1.

MATERIALS AND METHODS:

We collected 115 tissue biopsies from women who tested positive for the human papilloma virus (HPV). Nine morphological parameters including nuclear size, perinuclear halo, hyperchromasia, typical koilocyte (TK), abortive koilocyte (AK), bi-/multi-nucleation, keratohyaline granules, inflammation, and dyskeratosis were examined for each case. Correlation analyses, cumulative logistic regression, and binary logistic regression were used to determine optimal cut-off values of HPV copy numbers. The parameters TK, perinuclear halo, multi-nucleation, and nuclear size were significantly correlated quantitatively to HPV copy number.

RESULTS:

An HPV loading number of 58.9 and AK number of 20 were optimal to discriminate between negative and subtle findings in biopsies. An HPV loading number of 271.49 and AK of 20 were optimal for discriminating between equivocal changes and obvious koilocytosis.

CONCLUSION:

We propose that a squamous epithelial lesion with AK of >20 and quantitative HPV copy number between 58.9-271.49 represents a new spectrum of subtle pathological findings, characterized by AK in ASCUS. This can be described as a distinct entity and called "regressing koilocytosis".

KEYWORDS:

ASCUS; CIN; HPV; loading concentration; regressing koilocytosis

PMID:
24532500
PMCID:
PMC3936611
DOI:
10.3349/ymj.2014.55.2.331
[Indexed for MEDLINE]
Free PMC Article

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