# Project 4: CISNET Modeling of outcomes and cost for risk-based screening strategies

> **NIH NIH P01** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2024 · $201,393

## Abstract

Project 4 will evaluate the benefits, harms, and costs of personalized screening using one of the CISNET
models of breast cancer. We will evaluate the current approaches to personalized screening in WISDOM and
optimize the risk thresholds for the new subtype-specific risk models developed in this grant (Projects 2, 3) at
the population level in the US. We will compare personalized screening to the screening recommendations of 3
guidelines widely used in the US (ACR, ACS, USPSTF). Given that the natural history of breast cancer is long,
it is not possible to perform randomized trials comparing multiple screening strategies to assess their impact on
breast cancer morbidity and mortality. The MISCAN-Fadia is one of six CISNET models of breast cancer in the
US refined over 20 years of NCI funding. It allows us to estimate and compare the impact of different
approaches to breast cancer screening on long-term breast cancer outcomes to complement the evidence
generated from randomized trials such as WISDOM. Modeling has the advantage of combining evidence from
multiple high-quality data sources and simulating the impact of screening strategies on both harms and
benefits in subgroups of women based on their age, breast density, comorbidity level, race, and risk factor
profile. The results of simulation modeling provide critical data that have been used to guide national screening
recommendations globally. Most recently, the MISCAN-Fadia model was one of the CISNET models used to
guide the 2023 draft United States Preventative Services Task Force (USPSTF) breast cancer screening
guidelines.
The aims of the project will implement the personalized screening approaches used in WISDOM 1.0 and 2.0 in
the MISCAN model to project their impact on the benefits, harms and costs of breast cancer screening and
treatment and compare these outcomes to those from other screening strategies. We will then incorporate the
risk prediction tools for fast and slow growing cancers developed in the other projects in the grant into the
MISCAN model in order to identify the optimal risk thresholds to use for the updated risk prediction tools. For
example, we will systematically vary the risk thresholds for fast growing tumors used to recommend more
intensive screening (MRI alternating with mammography every 6 months) and the risk thresholds for slow
growing tumors used to recommend endocrine therapy and less frequent screening to estimate their impact on
breast cancer specific mortality and other outcomes. Scenarios that result in a gain of at least 5 quality
adjusted life years (approximately the gain from the move to tomosynthesis from digital mammography) with no
increase in total costs will be presented to a multidisciplinary group of stakeholders to allow them to choose the
thresholds that will be implemented in the next iteration of risk-based personalized screening in the WISDOM
study.

## Key facts

- **NIH application ID:** 10935792
- **Project number:** 1P01CA281826-01A1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** JEFFREY A TICE
- **Activity code:** P01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $201,393
- **Award type:** 1
- **Project period:** 2024-09-11 → 2029-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10935792

## Citation

> US National Institutes of Health, RePORTER application 10935792, Project 4: CISNET Modeling of outcomes and cost for risk-based screening strategies (1P01CA281826-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10935792. Licensed CC0.

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