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Differences and similarities in breast cancer risk assessment models in clinical practice: which model to choose?

  • Epidemiology
  • Published:
Breast Cancer Research and Treatment Aims and scope Submit manuscript

Abstract

To show differences and similarities between risk estimation models for breast cancer in healthy women from BRCA1/2-negative or untested families. After a systematic literature search seven models were selected: Gail-2, Claus Model, Claus Tables, BOADICEA, Jonker Model, Claus-Extended Formula, and Tyrer–Cuzick. Life-time risks (LTRs) for developing breast cancer were estimated for two healthy counsellees, aged 40, with a variety in family histories and personal risk factors. Comparisons were made with guideline thresholds for individual screening. Without a clinically significant family history LTRs varied from 6.7% (Gail-2 Model) to 12.8% (Tyrer–Cuzick Model). Adding more information on personal risk factors increased the LTRs and yearly mammography will be advised in most situations. Older models (i.e. Gail-2 and Claus) are likely to underestimate the LTR for developing breast cancer as their baseline risk for women is too low. When models include personal risk factors, surveillance thresholds have to be reformulated. For current clinical practice, the Tyrer–Cuzick Model and the BOADICEA Model seem good choices.

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Acknowledgements

We would like to acknowledge AC Antoniou for providing us with software for his risk assessment model BOADICEA. We also would like to thank our librarian JW Schoones for his help with the literature search.

Author information

Authors and Affiliations

  1. Department of Medical Decision Making, Leiden University Medical Center, Leiden, The Netherlands

    Catharina E. Jacobi & Bob Siegerink

  2. Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands

    Geertruida H. de Bock

  3. Department of Clinical Genetics, Center for Human and Clinical Genetics, Leiden University Medical Center, P.O. Box 9600, 2300 RC, Leiden, The Netherlands

    Bob Siegerink & Christi J. van Asperen

Authors
  1. Catharina E. Jacobi
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  2. Geertruida H. de Bock
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  4. Christi J. van Asperen
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Corresponding author

Correspondence to Christi J. van Asperen.

Appendix 1: Literature search

Appendix 1: Literature search

In all search engines, the two concepts “Familial breast cancer” and “Risk models” are represented by different variations or permutations of relevant terms.

PubMed (1950–2006)

In PubMed, words or phrases without field descriptions are mapped automatically to the appropriate field descriptions such as title, abstract, MeSH (Medical Subject Headings), MaJR (Major Medical Subject Headings). The concepts are combined, using the following search strategy:

(“familial breast cancer risk” OR (“breast cancer families” AND risk) OR (“breast cancer family” AND risk) OR (“risk assessment” AND “familial breast cancer”)) AND ((risk[ti] AND (model[ti] OR assessment[ti]) OR ((“Models, Statistical”[Majr] OR “Models, Genetic”[Majr]) AND “Probability”[Mesh]))) OR (“Breast Neoplasms/genetics”[Majr] OR (breast cancer AND (“Mass Screening”[MeSH] OR “Genetic Services”[MeSH] OR familial OR family OR families OR gene OR genes OR “Genetic Predisposition to Disease”[MeSH]))) AND ((risk[ti] AND (model[ti] OR assessment[ti]) OR ((“Models, Statistical”[Majr] OR “Models, Genetic”[Majr]) AND “Probability”[Mesh])))

EMBASE (1980–2006)

In EMBASE, subject headings and free text words are used in combination. Subject headings are marked with ‘/’ at the end of the specific term and are “exploded”, i.e. the narrower subject headings are also selected automatically. The following field descriptions were used for free text terms: mp = title, abstract, subject headings, heading word, drug trade name, original title, device manufacturer, drug manufacturer name; ti = title. The concepts are combined, using the following search strategy:

(familial breast cancer risk.mp OR (breast cancer families AND risk).mp OR (breast cancer family AND risk).mp OR (risk assessment AND familial breast cancer).mp) AND ((risk.ti AND (model.ti OR assessment.ti) OR ((exp mathematical model/) AND exp risk/))) OR ((exp *Breast Cancer/AND genetic$.mp) OR (exp Breast Cancer/AND (exp genetic service/OR exp cancer screening/OR familial.mp OR family.mp OR families.mp OR gene.mp OR genes.mp OR exp multifactorial inheritance/))) AND ((risk.ti AND (model.ti OR assessment.ti) OR ((exp mathematical model/) AND exp risk/)))

Web of Science (1945–2006)

In the Web of Science, free text words are used in combination. Words preceded by TI are searched in the field title. Words preceded by TS are searched in the fields abstract, keywords, or title. The concepts are combined, using the following search strategy:

(((TS=“risk assessment” AND TS=“familial breast cancer”) OR TS=“familial breast cancer risk” OR (TS=“breast cancer families” AND TS=risk) OR (TS=“breast cancer family” AND TS=risk)) AND ((TI=risk AND (TI=model OR TI=assessment)) OR (TS=model* AND TS=risk*))) OR (((TI=“Breast Cancer” OR TI=“breast tumor*” OR TI=“breast tumour*” OR TI=“breast carcin*” OR TI=“breast neoplas*”) AND (TS=“genetic screen*” OR TS=“cancer screen*” OR TS=famil* OR TS=gene OR TS=genes OR TS=predispos* OR TS=susceptib*)) AND ((TI=risk* AND (TI=model* OR TI=assessment)) OR (TS=model* AND TI=risk*))) OR ((((TI=“Breast Cancer” OR TI=“breast tumor*” OR TI=“breast tumour*” OR TI=“breast carcin*” OR TI=“breast neoplas*”) AND TI=genetic*)) AND ((TI=risk* AND (TI=model* OR TI=assessment)) OR (TS=model* AND TI=risk*)))

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Jacobi, C.E., de Bock, G.H., Siegerink, B. et al. Differences and similarities in breast cancer risk assessment models in clinical practice: which model to choose?. Breast Cancer Res Treat 115, 381–390 (2009). https://doi.org/10.1007/s10549-008-0070-x

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