ArticleBiometrics2023
Supervised two-dimensional functional principal component analysis with time-to-event outcomes and mammogram imaging data.
Article in Biometrics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- 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
- Studies of parenchymal texture added to mammographic breast density and risk of breast cancer: a systematic review of the methods used in the literature.Breast cancer research : BCR · 2022Pooled it
- The DeepJoint Algorithm: An Innovative Approach for Studying the Longitudinal Evolution of Quantitative Mammographic Density and Its Association With Screen-Detected Breast Cancer Risk.Biometrical journal. Biometrische Zeitschrift · 2026Article
- Identification of Regions of Interest in Neuroimaging Data With Irregular Boundary Based on Semiparametric Transformation Models and Interval-Censored Outcomes.Statistics in medicine · 2025Article
- Score Test for Functional Markov Process With Image Predictor.Statistics in medicine · 2025Article
- A Latent-Class Model for Time-To-Event Outcomes and High-Dimensional Imaging Data.Statistics in medicine · 2025Article
- DYNAMIC PREDICTION WITH MULTIVARIATE LONGITUDINAL OUTCOMES AND LONGITUDINAL MAGNETIC RESONANCE IMAGING DATA.The annals of applied statistics · 2025Article
- Supervised Functional Principal Component Analysis Under the Mixture Cure Rate Model: An Application to Alzheimer'S Disease.Statistics in medicine · 2025Article
- Permutation Test for Image-on-Scalar Regression With an Application to Breast Cancer.Statistics in medicine · 2024Article
- Modeling correlated pairs of mammogram images.Statistics in medicine · 2024Article
- Causal mediation analysis using high-dimensional image mediator bounded in irregular domain with an application to breast cancer.Biometrics · 2023Article
- 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
- Bayesian Statistics for Medical Devices: Progress Since 2010.Therapeutic innovation & regulatory science · 2023Review
- Joanne Knight Breast Health Cohort at Siteman Cancer Center.Cancer causes & control : CCC · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Screening mammography aims to identify breast cancer early and secondarily measures breast density to classify women at higher or lower than average risk for future breast cancer in the general population. Despite the strong association of individual mammography features to breast cancer risk, the statistical literature on mammogram imaging data is limited. While functional principal component analysis (FPCA) has been studied in the literature for extracting image-based features, it is conducted independently of the time-to-event response variable. With the consideration of building a prognostic model for precision prevention, we present a set of flexible methods, supervised FPCA (sFPCA) and functional partial least squares (FPLS), to extract image-based features associated with the failure time while accommodating the added complication from right censoring. Throughout the article, we hope to demonstrate that one method is favored over the other under different clinical setups. The proposed methods are applied to the motivating data set from the Joanne Knight Breast Health cohort at Siteman Cancer Center. Our approaches not only obtain the best prediction performance compared to the benchmark model, but also reveal different risk patterns within the mammograms.
Indexed as
Identifiers
What OpenQuestion holds
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.