# The Mathematics of Breast Cancer Overtreatment: Improving Treatment Choice through Effective Communication of Personalized Cancer Risk

> **NIH NIH R00** · DUKE UNIVERSITY · 2020 · $248,999

## Abstract

Project Summary/Abstract
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 ...

## Key facts

- **NIH application ID:** 9989064
- **Project number:** 5R00CA207872-05
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** Marc Ryser
- **Activity code:** R00 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $248,999
- **Award type:** 5
- **Project period:** 2018-09-01 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9989064

## Citation

> US National Institutes of Health, RePORTER application 9989064, The Mathematics of Breast Cancer Overtreatment: Improving Treatment Choice through Effective Communication of Personalized Cancer Risk (5R00CA207872-05). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9989064. Licensed CC0.

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