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Genetics of Breast and Ovarian Cancer (PDQ®)

  • Updated: 07/11/2014

Table 1. Characteristics of the Gail and Claus Modelsa

 Gail Model (Breast Cancer Risk Assessment Tool)b Claus Model 
Data derived from Breast Cancer Detection Demonstration Project StudyCancer and Steroid Hormone Study
Study population 2,852 cases, aged ≥35 y4,730 cases, aged 20–54 y
In situ and invasive cancerInvasive cancer
3,146 controls4,688 controls
CaucasianCaucasian
Annual breast screeningNot routinely screened
Family history characteristics FDRs with breast cancerFDRs or SDRs with breast cancer
Age of onset in relatives
Other characteristics Current ageCurrent age
Age at menarche
Age at first live birth
Number of breast biopsies
Atypical hyperplasia in breast biopsy
Race (included in the most current version of the Gail model)
Strengths Incorporates:Incorporates:
Risk factors other than family historyPaternal and maternal history
Age at onset of breast cancer
Family history of ovarian cancer
Limitations Underestimates risk in hereditary familiesMay underestimate risk in hereditary families
Number of breast biopsies without atypical hyperplasia may cause inflated risk estimatesMay not be applicable to all combinations of affected relatives
Does not include risk factors other than family history
Does not incorporate:
Paternal family history of breast cancer or any family history of ovarian cancer
Age at onset of breast cancer in relatives
All known risk factors for breast cancer [89]
Best application For individuals with no family history of breast cancer or one FDR with breast cancer, aged ≥50 yFor individuals with no more than two FDRs or SDRs with breast cancer
For determining eligibility for chemoprevention studies

FDR = first-degree relative; SDR = second-degree relative.
aAdapted from Domchek et al.,[87] Rubenstein et al.,[88] and Rhodes.[89]
bModified based on periodic updates.[90,91]

References

  1. Domchek SM, Eisen A, Calzone K, et al.: Application of breast cancer risk prediction models in clinical practice. J Clin Oncol 21 (4): 593-601, 2003.  [PUBMED Abstract]

  2. Rubinstein WS, O'Neill SM, Peters JA, et al.: Mathematical modeling for breast cancer risk assessment. State of the art and role in medicine. Oncology (Huntingt) 16 (8): 1082-94; discussion 1094, 1097-9, 2002.  [PUBMED Abstract]

  3. Rhodes DJ: Identifying and counseling women at increased risk for breast cancer. Mayo Clin Proc 77 (4): 355-60; quiz 360-1, 2002.  [PUBMED Abstract]

  4. Gail MH, Costantino JP, Pee D, et al.: Projecting individualized absolute invasive breast cancer risk in African American women. J Natl Cancer Inst 99 (23): 1782-92, 2007.  [PUBMED Abstract]

  5. Schonfeld SJ, Pee D, Greenlee RT, et al.: Effect of changing breast cancer incidence rates on the calibration of the Gail model. J Clin Oncol 28 (14): 2411-7, 2010.  [PUBMED Abstract]