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Originally published as JCO Early Release 10.1200/JCO.2008.21.2175 on February 9 2009 © 2009 American Society of Clinical Oncology.
Underestimation of Risk by Gail Model Extends Beyond Women With Atypical HyperplasiaDivision of Medical Oncology and Hematology, Princess Margaret Hospital, Toronto, Ontario, Canada To the Editor: We read with interest the article by Pankratz et al,1 in which the authors showed that the Gail model2,3 performs poorly in estimating the risk of breast cancer in women with atypical hyperplasia. Although this observation is significant, it is not surprising. Furthermore, we feel the article lacked a robust explanation for this poor performance as well as suggestions for alternative models for use by clinicians who manage women with atypical hyperplasia or other high-risk features. The Gail model2,3 is a risk assessment model that focuses on nongenetic risk factors, including prior history of benign proliferative breast disease. The model algorithm includes risk adjustment for family history involving first-degree relatives alone. Consequently, its major limitation is that this leads to underestimation of risk in the 50% of families with cancer in the paternal lineage; it also takes no account of the age of onset of breast cancer in family members. The only other risk assessment model that specifically includes risk adjustment for history of atypical hyperplasia is the Tyrer-Cuzick model.4 This model addresses many of the pitfalls of the Gail model; it combines adjustment for extensive family history, more comprehensive assessment of endogenous estrogen exposure, and inclusion of lobular carcinoma in situ, among other proliferative breast disorders. To date, only one study5 has prospectively compared and validated the Gail and Tyrer-Cuzick models. In this study, the fitness and discriminatory accuracy of these models were assessed using data from a cohort of 1,933 women at high risk for breast cancer attending the Family History Evaluation and Screening Programme in Manchester, United Kingdom. Of this cohort, 52 women developed breast cancer in a mean follow-up of 5.27 years. The study used the Gail and Tyrer-Cuzick models as well as the BRCAPRO6 and Claus7 models to estimate the risk of breast cancer. The ratios of expected to observed numbers of breast cancers were 0.48 (95% CI, 0.37 to 0.64) for the Gail model, 0.56 (95% CI, 0.43 to 0.75) for the Claus model, 0.49 (95% CI, 0.37 to 0.65) for the BRCAPRO model, and 0.81 (95% CI, 0.62 to 1.08) for the Tyrer-Cuzick model. The accuracy of the models for individual cases was evaluated using receiver operating characteristic curves. These showed that the concordance statistic was 0.735 for the Gail model, 0.716 for the Claus model, 0.737 for the BRCAPRO model, and 0.762 for the Tyrer-Cuzick model. This study included patients with atypical hyperplasia, but a subgroup analysis was not carried out, because numbers in this group were too small. In conclusion, previous work has already established that the Gail model has poor accuracy in estimating the degree of risk of breast cancer in women at high risk for the disease. The Tyrer-Cuzick model may offer better accuracy in such patients. Clearly, additional studies are necessary to gauge the accuracy of this and newer models, such as BOADICEA,8 in patients with high-risk features, including atypical hyperplasia. AUTHORS' DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST The author(s) indicated no potential conflicts of interest. REFERENCES
1. Pankratz VS, Hartmann LC, Degnim AC, et al: Assessment of the accuracy of the Gail model in women with atypical hyperplasia. J Clin Oncol 26:5374–5379, 2008. 2. Gail MH, Brinton LA, Byar DP, et al: Projecting individualized probabilities of developing breast cancer for white females who are being examined annually. J Natl Cancer Inst 81:1879–1886, 1989. 3. Costantino JP, Gail MH, Pee D, et al: Validation studies for models projecting the risk of invasive and total breast cancer incidence. J Natl Cancer Inst 91:1541–1548, 1999. 4. Tyrer J, Duffy SW, Cuzick J: A breast cancer prediction model incorporating familial and personal risk factors. Stat Med 23:1111–1130, 2004.[CrossRef][Medline] 5. Amir E, Evans DG, Shenton A, et al: Evaluation of breast cancer risk assessment packages in the family history evaluation and screening programme. J Med Genet 40:807–814, 2003. 6. Claus EB, Risch N, Thompson WD: Autosomal dominant inheritance of early onset breast cancer: Implications for risk prediction. Cancer 73:643–651, 1994.[CrossRef][Medline] 7. Ford D, Easton DF, Bishop DT, et al: Risk of cancer in BRCA-1 mutation carriers. Lancet 343:692–695, 1994.[CrossRef][Medline] 8. Antoniou AC, Pharoah PD, McMullan G, et al: A comprehensive model for familial breast cancer incorporating BRCA1, BRCA2 and other genes. Br J Cancer 86:76–83, 2002.[CrossRef][Medline]
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Copyright © 2009 by the American Society of Clinical Oncology, Online ISSN: 1527-7755. Print ISSN: 0732-183X
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