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Breast Cancer Research and Treatment

, Volume 170, Issue 2, pp 415–423 | Cite as

Validation of a personalized risk prediction model for contralateral breast cancer

  • Marzana Chowdhury
  • David Euhus
  • Banu Arun
  • Chris Umbricht
  • Swati Biswas
  • Pankaj Choudhary
Epidemiology
  • 247 Downloads

Abstract

Purpose

Women diagnosed with unilateral breast cancer are increasingly choosing to remove their other unaffected breast through contralateral prophylactic mastectomy (CPM) to reduce the risk of contralateral breast cancer (CBC). Yet a large proportion of CPMs are believed to be medically unnecessary. Thus, there is a pressing need to educate patients effectively on their CBC risk. We had earlier developed a CBC risk prediction model called CBCRisk based on eight personal risk factors.

Methods

In this study, we validate CBCRisk on independent clinical data from the Johns Hopkins University (JH) and MD Anderson Cancer Center (MDA). Women whose first breast cancer diagnosis was either invasive and/or ductal carcinoma in situ and whose age at first diagnosis was between 18 and 88 years were included in the cohorts because CBCRisk was developed specifically for these women. A woman who develops CBC is called a case whereas a woman who does not is called a control. The cohort sizes are 6035 (with 117 CBC cases) for JH and 5185 (with 111 CBC cases) for MDA. We computed the relevant calibration and validation measures for 3- and 5-year risk predictions.

Results

We found that the model performs reasonably well for both cohorts. In particular, area under the receiver-operating characteristic curve for the two cohorts range from 0.61 to 0.65.

Conclusions

With this independent validation, CBCRisk can be used confidently in clinical settings for counseling BC patients by providing their individualized CBC risk. In turn, this may potentially help alleviate the rate of medically unnecessary CPMs.

Keywords

CBCRisk Contralateral breast cancer Contralateral prophylactic mastectomy Absolute risk 

Notes

Funding

This work was funded by the National Cancer Institute at the National Institutes of Health (Grant Number R21CA186086).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Mathematical SciencesUniversity of Texas at DallasRichardsonUSA
  2. 2.Division of Surgical OncologyJohns Hopkins UniversityBaltimoreUSA
  3. 3.Department of Breast Medical OncologyUniversity of Texas MD Anderson Cancer CenterHoustonUSA

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