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J Clin Oncol. 2018 Jun 20;36(18):1840-1846. doi: 10.1200/JCO.2017.75.9480. Epub 2018 Apr 20.

Model for Predicting Breast Cancer Risk in Women With Atypical Hyperplasia.

Author information

1
Amy C. Degnim, Stacey J. Winham, Ryan D. Frank, Robert A. Vierkant, Marlene H. Frost, Tanya L. Hoskin, Celine M. Vachon, Karthik Ghosh, Tina J. Hieken, Jodi M. Carter, Lori A. Denison, Brendan Broderick, Lynn C. Hartmann, and Daniel W. Visscher, Mayo Clinic, Rochester, MN; V. Shane Pankratz, University of New Mexico Health Sciences Center, Albuquerque, NM; William D. Dupont, Vanderbilt University, Nashville, TN; and Derek C. Radisky, Mayo Clinic, Jacksonville, FL.

Abstract

Purpose Women with atypical hyperplasia (AH) on breast biopsy have an aggregate increased risk of breast cancer (BC), but existing risk prediction models do not provide accurate individualized estimates of risk in this subset of high-risk women. Here, we used the Mayo benign breast disease cohort to develop and validate a model of BC risk prediction that is specifically for women with AH, which we have designated as AH-BC. Patients and Methods Retrospective cohorts of women age 18 to 85 years with pathologically confirmed benign AH from Rochester, MN, and Nashville, TN, were used for model development and external validation, respectively. Clinical risk factors and histologic features of the tissue biopsy were selected using L1-penalized Cox proportional hazards regression. Identified features were included in a Fine and Gray regression model to estimate BC risk, with death as a competing risk. Model discrimination and calibration were assessed in the model-building set and an external validation set. Results The model-building set consisted of 699 women with AH, 142 of whom developed BC (median follow-up, 8.1 years), and the external validation set consisted of 461 women with 114 later BC events (median follow-up, 11.4 years). The final AH-BC model included three covariates: age at biopsy, age at biopsy squared, and number of foci of AH. At 10 years, the AH-BC model demonstrated good discrimination (0.63 [95% CI, 0.57 to 0.70]) and calibration (0.87 [95% CI, 0.66 to 1.24]). In the external validation set, the model showed acceptable discrimination (0.59 [95% CI, 0.51 to 0.67]) and calibration (0.91 [95% CI, 0.65 to 1.42]). Conclusion We have created a new model with which to refine BC risk prediction for women with AH. The AH-BC model demonstrates good discrimination and calibration, and it validates in an external data set.

PMID:
29676945
PMCID:
PMC6008107
[Available on 2019-06-20]
DOI:
10.1200/JCO.2017.75.9480

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