A Model for Individualized Risk Prediction of Contralateral Breast Cancer

描述（由申请人提供）：越来越多被诊断为浸润性乳腺癌或原位导管癌的患者选择行对侧预防性乳房切除术（CPM）以降低患对侧乳腺癌（CBC）的风险。这是一个特别令人不安的趋势，因为许多cpm被认为在医学上是不必要的。这是因为在过去二十年中，由于有效的辅助治疗的可用性，大多数患者的CBC风险显着下降。尽管如此，被诊断为原发性乳腺癌的患者往往大大高估了他们的CBC风险。同时，他们低估了与CPM相关的并发症、风险和负面影响。这些错误的认知部分解释了美国CPM率上升的原因。此外，几乎没有证据表明CPM有助于延长生存期。因此，需要适当地权衡CPM的优点和缺点。鉴于CPM的侵袭性和不可逆性，我们有必要为正在经历情感挑战时期的乳腺癌患者提供健全有效的教育。医生确实试图教育他们的病人；然而，他们缺乏能够帮助他们完成这项工作的工具。特别是，他们需要一个CBC风险预测模型，可以为散发性（非遗传性）乳腺癌患者提供个性化的风险估计。本项目旨在通过开发这样一个模型，验证它，并满足这一需求

DESCRIPTION (provided by applicant): Patients diagnosed with invasive breast cancer or ductal carcinoma in situ are increasingly choosing to undergo contralateral prophylactic mastectomy (CPM) to reduce their risk of contralateral breast cancer (CBC). This is a particularly disturbing trend as a large number of these CPMs are believed to be medically unnecessary. This is because the risk of CBC has dropped markedly for most patients in the last two decades due to availability of effective adjuvant therapies. Despite this fact, patients diagnosed with first primary breast cancer tend to substantially overestimate their CBC risk. At the same time, they underestimate the complications, risks, and negative effects associated with CPM. These incorrect perceptions partly explain the rising CPM rates in the U.S. Moreover, there is little evidence that CPM helps in prolonging survival. Thus, the benefits of CPM need to be weighed properly with its drawbacks. Given the invasive and irreversible nature of CPM, it behooves us to provide sound and effective education to breast cancer patients, who are going through an emotionally challenging period. Physicians do try to educate their patients; however, they lack tools that can help them in this endeavor. In particular, they need a CBC risk prediction model that can provide individualized risk estimates for sporadic (non-genetic) breast cancer patients. This project aims to fill this need by developing such a model, validating it, and implementing it in a freely available software package for immediate clinical use. To build the model, we will use data from Surveillance, Epidemiology, and End Results (SEER) Program and meta-analysis of risk estimates from literature. The proposed model will be in the style of the Gail model - a popular tool for counseling women on the risk of developing breast cancer - but one that is exclusively designed for counseling women with unilateral breast cancer on the risk of developing CBC. After building the model, we will validate it on prospectively collected data on breast cancer patients from four institutions - University of Texas at Southwestern Medical Center, Parkland Memorial Hospital in Dallas, M D Anderson Cancer Center, and Dartmouth Medical School. Once the model is validated, we will create a user-friendly package in statistical software R for implementing the model. Then we will integrate the package into CancerGene, a widely used and freely available clinical software for counseling patients on the risk of breast cancer. CancerGene is licensed to more than 4000 sites worldwide and therefore will be a perfect gateway to make the new model available to a large number of practitioners. We believe our proposed model will greatly facilitate patients' education and will help stem the increasing trend of medically unnecessary CPMs.