Grant Number:	4R37CA256810-05 Interpret this number
Primary Investigator:	Jiang, Shu
Organization:	Washington University
Project Title:	Dynamic Prediction Incorporating Time-Varying Covariates for the Onset of Breast Cancer
Fiscal Year:	2025

PROJECT SUMMARY Accurate assessment of risk is a top priority in oncology due to the population burden of cancer. Breast cancer is the leading cancer diagnosis among women worldwide and accordingly has the longest and broadest focus on risk prediction. Most traditional prediction models only utilize baseline factors known to be associated with breast cancer risk. The intrinsic heterogeneity between and within patients over time are reflected in part, by the time-varying covariate trajectories, which may provide important information for the prediction of breast cancer risk. The accumulation of cancer risk over life, well documented for breast cancer, is ideally suited to methods that incorporate time-varying covariates. The objective of this proposal is to provide novel statistical models that can incorporate patient heterogeneity in a personalized, dynamic manner leading to a more accurate risk prediction scheme. The proposed algorithms encompass innovative functional approaches to comprehensively characterize the changing pattern of the longitudinal trajectories by a set of outcome- independent/unsupervised and outcome-dependent/supervised features. The set of individual-specific features will contain information on the observed time-varying ‘pattern’ rather than one-time exposure in existing methods, leading to a higher predictive power. The dynamic prediction models will be built in a stepwise fashion, starting with a single time-varying covariate, and extended to the multivariate settings, to accommodate multiple time-varying covariates. In addition to contributions in prediction, the temporal change/trajectory of risk factors can add insights to pathways operating on risk of breast cancer. To develop viable preventive strategies, understanding the causal mechanisms whereby an exposure affects such dynamic trajectories (or mediators), to then in turn produce the breast cancer outcome is crucial, as these will provide insights into pathways that can better target breast cancer prevention and intervention trials. Given our ability to characterize the dynamic trajectories, we are positioned to fill in this gap and assess their intermediate role on the breast cancer pathway. In the two-year extension, we propose to develop a computationally efficient causal mediation framework to quantify the extent to which the effect of risk factors on breast cancer risk is mediated through BMI, hormone type and duration, mammographic density, and the whole mammogram image trajectory and the extent it is through other pathways. Successful completion of the proposed project will provide a transparent, robust, and reproducible statistical basis for inferences with the potential to shift the current paradigm leading to new pathways that can be targeted in breast cancer prevention and intervention trials to expand capacity for precision prevention.




Statistically Significant Association Does not Imply Improvement in Prediction of Clinical Outcomes.
Authors: Jiang S. , Rosner B.A. , Colditz G.A. .
Source: Cancer Prevention Research (philadelphia, Pa.), 2025-12-02 00:00:00.0; 18(12), p. 727-733.
PMID: 41024576
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Score Test for Functional Markov Process With Image Predictor.
Authors: Wang Y. , Colditz G.A. , Jiang S. .
Source: Statistics In Medicine, 2025 Aug; 44(18-19), p. e70231.
PMID: 40817779
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Automated Breast Density Assessment for Full-Field Digital Mammography and Digital Breast Tomosynthesis.
Authors: Jiang S. , Bennett D.L. , Chen S. , Toriola A.T. , Colditz G.A. .
Source: Cancer Prevention Research (philadelphia, Pa.), 2025-01-06 00:00:00.0; 18(1), p. 23-29.
PMID: 39450526
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Permutation Test for Image-on-Scalar Regression With an Application to Breast Cancer.
Authors: Jiang S. , Colditz G.A. .
Source: Statistics In Medicine, 2024-12-20 00:00:00.0; 43(29), p. 5596-5604.
EPub date: 2024-11-05 00:00:00.0.
PMID: 39501544
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Modeling correlated pairs of mammogram images.
Authors: Jiang S. , Colditz G.A. .
Source: Statistics In Medicine, 2024-02-13 00:00:00.0; , .
EPub date: 2024-02-13 00:00:00.0.
PMID: 38351511
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Pectoral muscle removal in mammogram images: A novel approach for improved accuracy and efficiency.
Authors: Chen S. , Bennett D.L. , Colditz G.A. , Jiang S. .
Source: Cancer Causes & Control : Ccc, 2023-09-07 00:00:00.0; , .
EPub date: 2023-09-07 00:00:00.0.
PMID: 37676616
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Association and Prediction Utilizing Craniocaudal and Mediolateral Oblique View Digital Mammography and Long-Term Breast Cancer Risk.
Authors: Chen S. , Tamimi R.M. , Colditz G.A. , Jiang S. .
Source: Cancer Prevention Research (philadelphia, Pa.), 2023-09-01 00:00:00.0; 16(9), p. 531-537.
PMID: 37428020
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Repeated measures of mammographic density and texture to evaluate prediction and risk of breast cancer: a systematic review of the methods used in the literature.
Authors: Anandarajah A. , Chen Y. , Stoll C. , Hardi A. , Jiang S. , Colditz G.A. .
Source: Cancer Causes & Control : Ccc, 2023-06-20 00:00:00.0; , .
EPub date: 2023-06-20 00:00:00.0.
PMID: 37340148
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Identifying regions of interest in mammogram images.
Authors: Jiang S. , Cao J. , Colditz G.A. .
Source: Statistical Methods In Medical Research, 2023 May; 32(5), p. 895-903.
EPub date: 2023-03-23 00:00:00.0.
PMID: 36951095
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Longitudinal Analysis of Change in Mammographic Density in Each Breast and Its Association With Breast Cancer Risk.
Authors: Jiang S. , Bennett D.L. , Rosner B.A. , Colditz G.A. .
Source: Jama Oncology, 2023-04-27 00:00:00.0; , .
EPub date: 2023-04-27 00:00:00.0.
PMID: 37103922
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Causal mediation analysis using high-dimensional image mediator bounded in irregular domain with an application to breast cancer.
Authors: Jiang S. , Colditz G.A. .
Source: Biometrics, 2023-02-28 00:00:00.0; , .
EPub date: 2023-02-28 00:00:00.0.
PMID: 36853975
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Studies of parenchymal texture added to mammographic breast density and risk of breast cancer: a systematic review of the methods used in the literature.
Authors: Anandarajah A. , Chen Y. , Colditz G.A. , Hardi A. , Stoll C. , Jiang S. .
Source: Breast Cancer Research : Bcr, 2022-12-30 00:00:00.0; 24(1), p. 101.
EPub date: 2022-12-30 00:00:00.0.
PMID: 36585732
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Dynamic prediction with time-dependent marker in survival analysis using supervised functional principal component analysis.
Authors: Shi H. , Jiang S. , Cao J. .
Source: Statistics In Medicine, 2022-05-16 00:00:00.0; , .
EPub date: 2022-05-16 00:00:00.0.
PMID: 35574725
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Supervised two-dimensional functional principal component analysis with time-to-event outcomes and mammogram imaging data.
Authors: Jiang S. , Cao J. , Rosner B. , Colditz G.A. .
Source: Biometrics, 2021-12-02 00:00:00.0; , .
EPub date: 2021-12-02 00:00:00.0.
PMID: 34854477
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Predicting the onset of breast cancer using mammogram imaging data with irregular boundary.
Authors: Jiang S. , Cao J. , Colditz G.A. , Rosner B. .
Source: Biostatistics (oxford, England), 2021-08-26 00:00:00.0; , .
EPub date: 2021-08-26 00:00:00.0.
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