Grant Details
Grant Number: |
3P01CA154292-09S1 Interpret this number |
Primary Investigator: |
Miglioretti, Diana |
Organization: |
University Of California At Davis |
Project Title: |
Risk-Based Breast Cancer Screening and Surveillance in Community Practice - Admin Supplement for P3 |
Fiscal Year: |
2020 |
Abstract
PROJECT SUMMARY
This application is being submitted in response to the Notice of Special Interest (NOSI) identified as NOT-CA-
20-038. The goals of this supplement are to advance progress toward implementing risk-based imaging
surveillance for breast cancer in clinical practice. We propose to improve methodological approaches for
developing risk models for breast cancer imaging surveillance outcomes, including surveillance detected
second breast cancer (benefit) and interval invasive breast cancer (failure), and inform the development of an
optimal risk-based imaging surveillance strategy for individual women with primary breast cancer. This
proposal builds on the resources of the Breast Cancer Surveillance Consortium (BCSC) from more than
60,000 women with a personal history of breast cancer and more than 330,000 surveillance mammography
examinations. The investigators will leverage modern data-adaptive modeling approaches, specifically
regularized regression models and machine learning methods which can potentially enhance prediction
accuracy, to develop risk models of surveillance outcomes (Aim 1). The investigators propose a
comprehensive internal validation with multiple metrics to evaluate the risk models developed via alternative
methods for a full understanding of their utilities and trade-offs between models in improving breast cancer
survivorship while maintaining clinical usability and interpretability (Aim 2.1). Specifically, the investigators will
evaluate the area under the receiver operating characteristic curve (AUC) and the calibration of each risk
model developed in Aim 1, and conduct comparison across models using net reclassification improvement and
variable importance measures. Additionally, an online tutorial created using R Markdown is proposed to
accelerate uptake of best practices for modern risk model development and validation in other cancers (Aim
2.2). The evaluation and dissemination of alternative methodological modeling approaches in this supplement
will directly inform development of risk-stratified surveillance algorithms in breast and other cancer types.
Publications
None. See parent grant details.