ArticleBiostatistics (Oxford, England)2023
Predicting the onset of breast cancer using mammogram imaging data with irregular boundary.
Article in Biostatistics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 24 citations in OpenAlex.
- 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.Cancer causes & control : CCC · 2023Pooled it
- Score Test for Functional Markov Process With Image Predictor.Statistics in medicine · 2025Article
- Validation of a Dynamic Risk Prediction Model Incorporating Prior Mammograms in a Diverse Population.JAMA network open · 2025Article
- Supervised Functional Principal Component Analysis Under the Mixture Cure Rate Model: An Application to Alzheimer'S Disease.Statistics in medicine · 2025Article
- Applying YOLOv6 as an ensemble federated learning framework to classify breast cancer pathology images.Scientific reports · 2025Article
- Efficient Nonparametric Estimation of 3D Point Cloud Signals through Distributed Learning.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2025Article
- Permutation Test for Image-on-Scalar Regression With an Application to Breast Cancer.Statistics in medicine · 2024Article
- Development and Validation of Dynamic 5-Year Breast Cancer Risk Model Using Repeated Mammograms.JCO clinical cancer informatics · 2024Article
- Modeling correlated pairs of mammogram images.Statistics in medicine · 2024Article
- Pectoral muscle removal in mammogram images: A novel approach for improved accuracy and efficiency.Cancer causes & control : CCC · 2024Article
- Causal mediation analysis using high-dimensional image mediator bounded in irregular domain with an application to breast cancer.Biometrics · 2023Article
- Epidemiology and Risk Factors for Breast Cancer: 21st Century Advances, Gaps to Address through Interdisciplinary Science.Cold Spring Harbor perspectives in medicine · 2023Review
- Association and Prediction Utilizing Craniocaudal and Mediolateral Oblique View Digital Mammography and Long-Term Breast Cancer Risk.Cancer prevention research (Philadelphia, Pa.) · 2023Article
- Identifying regions of interest in mammogram images.Statistical methods in medical research · 2023Article
- Strategies to Achieve Breast Health Equity in the St. Louis Region and Beyond over 15+ Years.Cancers · 2022Review
- Joanne Knight Breast Health Cohort at Siteman Cancer Center.Cancer causes & control : CCC · 2022Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
Abstract
With mammography being the primary breast cancer screening strategy, it is essential to make full use of the mammogram imaging data to better identify women who are at higher and lower than average risk. Our primary goal in this study is to extract mammogram-based features that augment the well-established breast cancer risk factors to improve prediction accuracy. In this article, we propose a supervised functional principal component analysis (sFPCA) over triangulations method for extracting features that are ordered by the magnitude of association with the failure time outcome. The proposed method accommodates the irregular boundary issue posed by the breast area within the mammogram imaging data with flexible bivariate splines over triangulations. We also provide an eigenvalue decomposition algorithm that is computationally efficient. Compared to the conventional unsupervised FPCA method, the proposed method results in a lower Brier Score and higher area under the ROC curve (AUC) in simulation studies. We apply our method to data from the Joanne Knight Breast Health Cohort at Siteman Cancer Center. Our approach not only obtains the best prediction performance comparing to unsupervised FPCA and benchmark models but also reveals important risk patterns within the mammogram images. This demonstrates the importance of utilizing additional supervised image-based features to clarify breast cancer risk.
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Registered trials
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.