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

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.





None. See parent grant details.