This Career Development Application provides targeted coursework and mentored research to enable pro- gression to independent research in the highly cross-disciplinary areas of mathematical modeling and person- alized breast cancer care. Every year, close to 60,000 women in the US undergo radical surgery after diagno- sis with screen-detected breast carcinoma in situ (BCIS), yet as many as 45,000 of these are treated for be- nign lesions that would not progress to invasive breast cancer in their lifetime. The resulting overtreatment of non-progressive BCIS lesions can cause substantial harms and significantly reduce the patient's quality of life without reducing breast cancer mortality. Although the widespread overtreatment of women with BCIS is well documented at the population level, its prevention at the patient level is hindered by the current treatment par- adigm, which dictates that virtually all patients undergo immediate treatment. This in turn perpetuates the lack of data needed for the evaluation of management strategies other than immediate treatment, such as active surveillance. To resolve this conundrum, randomized controlled trials on active surveillance have been initiated, but only recently and only in Europe. It is anticipated that these trials, even if successful, will not yield clinically actionable data for at least 10 years. At the same time, however, there is a wealth of existing clinical and bio- logical data on BCIS that is dispersed across a large number of data and knowledge sources. In the absence of quantitative models that enable the integration of these dispersed sources, the bulk of the existing data re- mains inaccessible to patients. Thus, to enable informed decision making among patients with BCIS, there is a critical need (i) to develop predictive models that integrate available patient- and tumor-specific data to make personalized risk and uncertainty projections for different management strategies, and (ii) to effectively com- municate these personalized projections to patients. In the absence of tools for the quantification and commu- nication of personalized risk projections, it remains difficult for patients and physicians to weigh the trade-offs associated with different management strategies and to make an informed, evidence-based decision that re- duces the risk of potentially harmful overtreatment of BCIS. The long-term goal is to develop personalized de- cision aids that maximize informed decision-making and minimize overtreatment in patients with BCIS. The overall objective of this proposal comprises the first three steps towards this goal: (i) to develop personalized risk projection models for different management strategies of BCIS, (ii) to use these projections to develop a personalized decision aid, and (iii) to evaluate its impact in in a test cohort of women without a history of breast cancer. Our central hypothesis is that communication of model-based personalized risk projections leads to an improved understanding of the trade-offs associated with different management strategies for BCIS. The ra- tionale for the proposed research is that with personalized outcome estimates, patients gain access to the in- formation needed for an evidence-based decision that is aligned with their personal risk tolerance.
The specific aims for the mentored (K) and independent (R) research phases of this K99/R00 are as follows.
Aim K1: Discover data and knowledge sources that are relevant for personalized risk projections in BCIS pa- tients, and curate them into a harmonized data store and knowledge base, respectively.
Aim K2: Develop mathematical models that use the data store and knowledge base to compute personalized risk projections for different BCIS management strategies, including active surveillance.
Aim K3: Design a two-stage study to develop, refine and evaluate a model-based personalized decision aid for BCIS patients through cognitive interviews (Stage 1) and a RCT (Stage 2).
Aim R1: Perform model validation and uncertainty quantification to maximize model confidence.
Aim R2: Stage 1: Conduct cognitive interviews to develop and refine an interactive decision aid for the effec- tive communication of personalized risk projections in BCIS patients.
Aim R3: Stage 2: Implement a RCT to test the main hypothesis that the use of personalized decision aids leads to (i) an increase in the proportion of women who would consider active surveillance as a viable management strategy for BCIS, and (ii) an increase in knowledge of the associated risk trade-offs. The deliverables will include a data-driven mathematical modeling framework, expected to yield the best pos- sible patient-specific risk projections for different management strategies of BCIS. The interactive decision aid is expected to provide an intuitive understanding of the risks and uncertainties that are associated with different BCIS management strategies. Moreover, this approach will have widespread application in other screen- detected lesions of unknown progression risk, such as those increasingly diagnosed in the prostate, thyroid and lung. The applicant has completed graduate studies in physics (MSc) and mathematics (PhD) and has ini- tiated projects with the primary mentor who has extensive experience in early stage breast cancer, including BCIS. Based on his history of successful collaborative research with clinicians, the applicant is in the unique position to bridge the divide between mathematical modeling and personalized cancer care.
The proposed project is highly relevant to public health because it addresses the emerging and growing burden of overdiagnosis resulting from broad-based cancer screening. The evidence-based, personalized decision aids developed in this research are designed to improve risk communication, facilitate informed decision-making and hence increase risk-concordant treatment selection and reduce overtreatment among patients with breast carcinoma in situ. Thus, the proposed research is relevant to the NIH's mission to protect and improve health.
| Grimm, Lars J; Ryser, Marc D; Partridge, Ann H et al. (2017) Surgical Upstaging Rates for Vacuum Assisted Biopsy Proven DCIS: Implications for Active Surveillance Trials. Ann Surg Oncol 24:3534-3540 |
