Validation of a personalized risk prediction model for contralateral breast cancer
- 247 Downloads
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.
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.
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.
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.
KeywordsCBCRisk Contralateral breast cancer Contralateral prophylactic mastectomy Absolute risk
This work was funded by the National Cancer Institute at the National Institutes of Health (Grant Number R21CA186086).
- 11.Breast Cancer Surveillance Consortium. last accessed on April 8, 2016. Data retrieved BCSC, https://breastscreening.cancer.gov/
- 12.Surveillance, Epidemiology, and End Results Program. Research data (1973-2010). released April 2013, based on the November 2012. https://seer.cancer.gov
- 13.CBCRisk. last accessed on February 15, 2018. http://www.utdallas.edu/swati.biswas/
- 17.R Development Core Team (2016) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, 2016. http://www.R-project.org
- 24.Anothaisintawee T, Teerawattananon Y, Wiratkapun C, Srinakarin J, Woodtichartpreecha P, Hirunpat S, Wongwaisayawan S, Lertsithichai P, Kasamesup V, Thakkinstian A (2014) Development and validation of a breast cancer risk prediction model for Thai women: a cross-sectional study. Asian Pac J Cancer Prev 15(16):6811–6817CrossRefPubMedGoogle Scholar