ArticlePattern recognition2022
Deep learning of longitudinal mammogram examinations for breast cancer risk prediction.
Article in Pattern recognition, 2022. 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.
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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.
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Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 48 citations in OpenAlex.
- Current state of mammography-based artificial intelligence for future breast cancer risk prediction: a systematic review.Journal of the National Cancer Institute · 2026Pooled it
- Artificial Intelligence-Driven Mammography-Based Future Breast Cancer Risk Prediction: A Systematic Review.Journal of the American College of Radiology : JACR · 2024Pooled it
- MuSTAF: Clinically Relevant Multi-task Spatiotemporal Attention Fusion Framework for Breast Cancer Detection with Longitudinal Mammography.medRxiv : the preprint server for health sciences · 2026Article
- Integrating DNA methylation biomarkers for breast cancer risk prediction using artificial intelligence.Scientific reports · 2026Article
- Downsampling attention fusion network (DAFNet): a You Only Look Once network for lung nodule detection.Quantitative imaging in medicine and surgery · 2026Article
- 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
- Longitudinal MRI-based deep learning model for predicting pathological complete response in breast cancer: a multicenter, retrospective cohort study.NPJ precision oncology · 2026Article
- Advancing Precision Oncology Through Modeling of Longitudinal and Multimodal Data.IEEE reviews in biomedical engineering · 2026Review
- Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence · 2026Article
- Predicting short- to long-term breast cancer risk from longitudinal mammographic screening history.NPJ breast cancer · 2025Article
- Article
- Spatial-temporal radiogenomics in predicting neoadjuvant chemotherapy efficacy for breast cancer: a comprehensive review.Journal of translational medicine · 2025Review
- Deep learning in breast cancer risk prediction: a review of recent applications in full-field digital mammography.Frontiers in oncology · 2025Review
- Deep learning algorithm on H&E whole slide images to characterizeComputational and structural biotechnology journal · 2024Article
- On the bias in the AUC variance estimate.Pattern recognition letters · 2024Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
Information in digital mammogram images has been shown to be associated with the risk of developing breast cancer. Longitudinal breast cancer screening mammogram examinations may carry spatiotemporal information that can enhance breast cancer risk prediction. No deep learning models have been designed to capture such spatiotemporal information over multiple examinations to predict the risk. In this study, we propose a novel deep learning structure, LRP-NET, to capture the spatiotemporal changes of breast tissue over multiple negative/benign screening mammogram examinations to predict near-term breast cancer risk in a case-control setting. Specifically, LRP-NET is designed based on clinical knowledge to capture the imaging changes of bilateral breast tissue over four sequential mammogram examinations. We evaluate our proposed model with two ablation studies and compare it to three models/settings, including 1) a "loose" model without explicitly capturing the spatiotemporal changes over longitudinal examinations, 2) LRP-NET but using a varying number (i.e., 1 and 3) of sequential examinations, and 3) a previous model that uses only a single mammogram examination. On a case-control cohort of 200 patients, each with four examinations, our experiments on a total of 3200 images show that the LRP-NET model outperforms the compared models/settings.
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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.