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. 2015 Mar 10;33(8):923-9.
doi: 10.1200/JCO.2014.55.4865. Epub 2015 Jan 26.

Model for individualized prediction of breast cancer risk after a benign breast biopsy

V Shane Pankratz  1 , Amy C Degnim  1 , Ryan D Frank  1 , Marlene H Frost  1 , Daniel W Visscher  1 , Robert A Vierkant  1 , Tina J Hieken  1 , Karthik Ghosh  1 , Yaman Tarabishy  1 , Celine M Vachon  1 , Derek C Radisky  1 , Lynn C Hartmann  2
Affiliations

Model for individualized prediction of breast cancer risk after a benign breast biopsy

V Shane Pankratz et al. J Clin Oncol. .

Abstract

Purpose: Optimal early detection and prevention for breast cancer depend on accurate identification of women at increased risk. We present a risk prediction model that incorporates histologic features of biopsy tissues from women with benign breast disease (BBD) and compare its performance to the Breast Cancer Risk Assessment Tool (BCRAT).

Methods: We estimated the age-specific incidence of breast cancer and death from the Mayo BBD cohort and then combined these estimates with a relative risk model derived from 377 patient cases with breast cancer and 734 matched controls sampled from the Mayo BBD cohort to develop the BBD-to-breast cancer (BBD-BC) risk assessment tool. We validated the model using an independent set of 378 patient cases with breast cancer and 728 matched controls from the Mayo BBD cohort and compared the risk predictions from our model with those from the BCRAT.

Results: The BBD-BC model predicts the probability of breast cancer in women with BBD using tissue-based and other risk factors. The concordance statistic from the BBD-BC model was 0.665 in the model development series and 0.629 in the validation series; these values were higher than those from the BCRAT (0.567 and 0.472, respectively). The BCRAT significantly underpredicted breast cancer risk after benign biopsy (P = .004), whereas the BBD-BC predictions were appropriately calibrated to observed cancers (P = .247).

Conclusion: We developed a model using both demographic and histologic features to predict breast cancer risk in women with BBD. Our model more accurately classifies a woman's breast cancer risk after a benign biopsy than the BCRAT.

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Conflict of interest statement

Authors' disclosures of potential conflicts of interest are found in the article online at www.jco.org. Author contributions are found at the end of this article.

Figures

Fig 1.
Fig 1.
Plots summarizing the calibration of 10-year risk predictions by comparing observed proportion of women with breast cancer to the proportion expected within deciles of predicted risk. If a model is well calibrated, the points should lie along the diagonal line to indicate that the observed proportion of patients in each risk group agrees closely with the proportions predicted from the model. (A) Calibration of the Breast Cancer Risk Assessment Tool. (B) Calibration of the benign breast disease–to–breast cancer model.
Fig A1.
Fig A1.
Cumulative incidence of breast cancer and of death among women with benign breast disease (BBD), estimated from the women enrolled onto the Mayo BBD cohort from 1967 to 1991.
See this image and copyright information in PMC

Comment in

  • Is the Benign Breast Disease Breast Cancer Model Well Calibrated?
    Gail MH, Pfeiffer RM. Gail MH, et al. J Clin Oncol. 2015 Sep 1;33(25):2829-30. doi: 10.1200/JCO.2015.61.6177. Epub 2015 Jul 27. J Clin Oncol. 2015. PMID: 26215936 No abstract available.
  • Reply to M.H. Gail et al.
    Pankratz VS, Degnim AC, Vierkant RA, Frank RD, Hartmann LC. Pankratz VS, et al. J Clin Oncol. 2015 Sep 1;33(25):2830-1. doi: 10.1200/JCO.2015.62.5756. Epub 2015 Jul 27. J Clin Oncol. 2015. PMID: 26215951 No abstract available.

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