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Risk stratification in cervical cancer screening by complete screening history: Applying bioinformatics to a general screening population
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational Biology and Bioinformatics. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Stockholm County, Sweden.ORCID iD: 0000-0001-8505-403x
Karolinska Inst, Dept Lab Med, Stockholm, Stockholm Count, Sweden..
Canc Registry Norway, Dept Registry Informat, Oslo, Oslo County, Norway..
Karolinska Inst, Dept Lab Med, Stockholm, Stockholm Count, Sweden..
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2017 (English)In: International Journal of Cancer, ISSN 0020-7136, E-ISSN 1097-0215, Vol. 141, no 1, 200-209 p.Article in journal (Refereed) Published
Abstract [en]

Women screened for cervical cancer in Sweden are currently treated under a one-size-fits-all programme, which has been successful in reducing the incidence of cervical cancer but does not use all of the participants' available medical information. This study aimed to use women's complete cervical screening histories to identify diagnostic patterns that may indicate an increased risk of developing cervical cancer. A nationwide case-control study was performed where cervical cancer screening data from 125,476 women with a maximum follow-up of 10 years were evaluated for patterns of SNOMED diagnoses. The cancer development risk was estimated for a number of different screening history patterns and expressed as Odds Ratios (OR), with a history of 4 benign cervical tests as reference, using logistic regression. The overall performance of the model was moderate (64% accuracy, 71% area under curve) with 61-62% of the study population showing no specific patterns associated with risk. However, predictions for high-risk groups as defined by screening history patterns were highly discriminatory with ORs ranging from 8 to 36. The model for computing risk performed consistently across different screening history lengths, and several patterns predicted cancer outcomes. The results show the presence of risk-increasing and risk-decreasing factors in the screening history. Thus it is feasible to identify subgroups based on their complete screening histories. Several high-risk subgroups identified might benefit from an increased screening density. Some low-risk subgroups identified could likely have a moderately reduced screening density without additional risk.

Place, publisher, year, edition, pages
2017. Vol. 141, no 1, 200-209 p.
Keyword [en]
bioinformatics, cervical cancer, screening, personalized medicine, machine learning
National Category
Cancer and Oncology Bioinformatics (Computational Biology)
Identifiers
URN: urn:nbn:se:uu:diva-323754DOI: 10.1002/ijc.30725ISI: 000400766500021PubMedID: 28383102OAI: oai:DiVA.org:uu-323754DiVA: diva2:1108603
Available from: 2017-06-12 Created: 2017-06-12 Last updated: 2017-06-12Bibliographically approved

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