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- Baltzer, NicholasUppsala universitet,Beräkningsbiologi och bioinformatik,Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Stockholm County, Sweden (author)
Risk stratification in cervical cancer screening by complete screening history : Applying bioinformatics to a general screening population
- Article/chapterEnglish2017
Publisher, publication year, extent ...
- 2017-04-24
- Wiley,2017
- printrdacarrier
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- LIBRIS-ID:oai:DiVA.org:uu-323754
- https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-323754URI
- https://doi.org/10.1002/ijc.30725DOI
- http://kipublications.ki.se/Default.aspx?queryparsed=id:135808081URI
Supplementary language notes
- Language:English
- Summary in:English
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- 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.
Subject headings and genre
- MEDICIN OCH HÄLSOVETENSKAP Klinisk medicin Cancer och onkologi hsv//swe
- MEDICAL AND HEALTH SCIENCES Clinical Medicine Cancer and Oncology hsv//eng
- NATURVETENSKAP Data- och informationsvetenskap Bioinformatik hsv//swe
- NATURAL SCIENCES Computer and Information Sciences Bioinformatics hsv//eng
- bioinformatics
- cervical cancer
- screening
- personalized medicine
- machine learning
Added entries (persons, corporate bodies, meetings, titles ...)
- Sundström, KarinKarolinska Institutet (author)
- Nygård, Jan F.Canc Registry Norway, Dept Registry Informat, Oslo, Oslo County, Norway. (author)
- Dillner, JoakimKarolinska Institutet (author)
- Komorowski, JanUppsala universitet,Beräkningsbiologi och bioinformatik,Polish Acad Sci, Inst Comp Sci, Warsaw, Warsaw County, Poland.(Swepub:uu)jakom133 (author)
- Uppsala universitetBeräkningsbiologi och bioinformatik (creator_code:org_t)
Related titles
- In:International Journal of Cancer: Wiley141:1, s. 200-2090020-71361097-0215
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