ArticleBiometrics2023
Causal mediation analysis using high-dimensional image mediator bounded in irregular domain with an application to breast cancer.
Article in Biometrics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 13 citations in OpenAlex.
- Accelerating discovery of cancer causes for prevention in the era of rising early-onset cancers.Cell · 2026Review
- Bayesian Image Mediation Analysis.Journal of the American Statistical Association · 2026Article
- A multimodal imaging-based decision tree for preoperative axillary risk stratification in breast cancer: a retrospective clinical utility analysis.American journal of cancer research · 2026Article
- Mediation analysis with graph mediator.Biostatistics (Oxford, England) · 2025Article
- Causal functional mediation analysis with an application to functional magnetic resonance imaging data.Biostatistics (Oxford, England) · 2025Article
- Score Test for Functional Markov Process With Image Predictor.Statistics in medicine · 2025Article
- Permutation Test for Image-on-Scalar Regression With an Application to Breast Cancer.Statistics in medicine · 2024Article
- Mediation analysis in longitudinal study with high-dimensional methylation mediators.Briefings in bioinformatics · 2024Article
- Modeling correlated pairs of mammogram images.Statistics in medicine · 2024Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Mammography is the primary breast cancer screening strategy. Recent methods have been developed using the mammogram image to improve breast cancer risk prediction. However, it is unclear on the extent to which the effect of risk factors on breast cancer risk is mediated through tissue features summarized in mammogram images and the extent to which it is through other pathways. While mediation analysis has been conducted using mammographic density (a summary measure within the image), the mammogram image is not necessarily well described by a single summary measure and, in addition, such a measure provides no spatial information about the relationship between the exposure risk factor and the risk of breast cancer. Thus, to better understand the role of the mammogram images that provide spatial information about the state of the breast tissue that is causally predictive of the future occurrence of breast cancer, we propose a novel method of causal mediation analysis using mammogram image mediator while accommodating the irregular shape of the breast. We apply the proposed method to data from the Joanne Knight Breast Health Cohort and leverage new insights on the decomposition of the total association between risk factor and breast cancer risk that was mediated by the texture of the underlying breast tissue summarized in the mammogram image.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.