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Ki67 predicts progression in early CIN: validation of a multivariate progression-risk model.

Publication ,  Journal Article
Kruse, A-J; Baak, JPA; Janssen, EA; Kjellevold, K-H; Fiane, B; Lovslett, K; Bergh, J; Robboy, S
Published in: Cell Oncol
2004

This study of early CIN biopsies (25 CIN1 and 65 CIN2) with long follow-up was done to validate, in a new group of patients, the value of Ki67 immuno-quantitative features to predict high CIN grade in a follow-up biopsy (often denoted to as "progression"), as described in a previous study. Each biopsy in the present study was classified with the previously described Ki67-model (consisting of the stratification index and the % positive nuclei in the middle third layer of the epithelium) as "low-risk" or "high-risk", and matched with the follow-up outcome (progression-or-not). Furthermore, it was studied whether subjective evaluation of the Ki67 sections by experienced pathologists, who were aware of the prognostic quantitative Ki67 features, could also predict the outcome. Thirdly, the reproducibility of routine use of the quantitative Ki67-model was assessed. Fifteen cases progressed (17%) to CIN3, 2/25 CIN1 (8%) and 13/65 CIN2 (20%), indicating that CIN grade (as CIN1 or CIN2) is prognostic and that the percentage of CIN1 and CIN2 cases with progression in the present study is comparable to many previous studies. However, the quantitative Ki67 model had stronger prognostic value than CIN grade as none of the 40 "Ki67-model low-risk" patients progressed, in contrast to 15 (30%) of the 50 "Ki67-model high-risk" patients (p<0.001). In multivariate analysis, neither CIN grade nor any of the other quantitative Ki67 features added to the abovementioned prognostic Ki67-model. Subjective analysis of the Ki67 features was also prognostic, although quantitative assessments gave better results. Routine application of the quantitative Ki67-model in CIN1 and CIN2 was well reproducible. In conclusion, the results confirm that quantitative Ki67 features have strong prognostic value for progression in early CIN lesions.

Duke Scholars

Published In

Cell Oncol

DOI

ISSN

1570-5870

Publication Date

2004

Volume

26

Issue

1-2

Start / End Page

13 / 20

Location

Netherlands

Related Subject Headings

  • Uterine Cervical Neoplasms
  • Uterine Cervical Dysplasia
  • Risk Factors
  • Risk Assessment
  • Prognosis
  • Predictive Value of Tests
  • Pathology
  • Oncology & Carcinogenesis
  • Multivariate Analysis
  • Models, Statistical
 

Citation

APA
Chicago
ICMJE
MLA
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Kruse, A.-J., Baak, J. P. A., Janssen, E. A., Kjellevold, K.-H., Fiane, B., Lovslett, K., … Robboy, S. (2004). Ki67 predicts progression in early CIN: validation of a multivariate progression-risk model. Cell Oncol, 26(1–2), 13–20. https://doi.org/10.1155/2004/108305
Kruse, Arnold-Jan, Jan P. A. Baak, Emiel A. Janssen, Kjell-Henning Kjellevold, Bent Fiane, Kjell Lovslett, Johan Bergh, and Stanley Robboy. “Ki67 predicts progression in early CIN: validation of a multivariate progression-risk model.Cell Oncol 26, no. 1–2 (2004): 13–20. https://doi.org/10.1155/2004/108305.
Kruse A-J, Baak JPA, Janssen EA, Kjellevold K-H, Fiane B, Lovslett K, et al. Ki67 predicts progression in early CIN: validation of a multivariate progression-risk model. Cell Oncol. 2004;26(1–2):13–20.
Kruse, Arnold-Jan, et al. “Ki67 predicts progression in early CIN: validation of a multivariate progression-risk model.Cell Oncol, vol. 26, no. 1–2, 2004, pp. 13–20. Pubmed, doi:10.1155/2004/108305.
Kruse A-J, Baak JPA, Janssen EA, Kjellevold K-H, Fiane B, Lovslett K, Bergh J, Robboy S. Ki67 predicts progression in early CIN: validation of a multivariate progression-risk model. Cell Oncol. 2004;26(1–2):13–20.

Published In

Cell Oncol

DOI

ISSN

1570-5870

Publication Date

2004

Volume

26

Issue

1-2

Start / End Page

13 / 20

Location

Netherlands

Related Subject Headings

  • Uterine Cervical Neoplasms
  • Uterine Cervical Dysplasia
  • Risk Factors
  • Risk Assessment
  • Prognosis
  • Predictive Value of Tests
  • Pathology
  • Oncology & Carcinogenesis
  • Multivariate Analysis
  • Models, Statistical