Grant Number:	5R37CA256810-04 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:	2024

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. More recent models expand to place greater emphasis on genomic risk factors. However, the predominant move of adding genomic risk markers incorporates a measure that is invariant to time (based on SNPs) and do not necessarily solve the challenge of improving breast cancer risk classification. 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. Theobjective of this proposal is toprovide 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. The proposed methods will be applied to the Nurses' Health Study and further assessed externally in the Mayo Mammography Health Study. All of the proposed methods will be accompanied with user-friendly open-source software.




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.
PMID: 34435196
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