ArticleRadiology. Artificial intelligence2024
Deep Learning for Breast Cancer Risk Prediction: Application to a Large Representative UK Screening Cohort.
Article in Radiology. Artificial intelligence, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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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
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Current state of mammography-based artificial intelligence for future breast cancer risk prediction: a systematic review.Journal of the National Cancer Institute · 2026Pooled it
- Enhanced MobileNet with multi-scale feature fusion for automated breast cancer histopathology classification.Scientific reports · 2026Article
- Discriminating Breast Cancer Patients by Spectral Analysis of Arterial Pulse Waveforms.Cardiovascular engineering and technology · 2026Article
- Bridging Pixels and Practice: AI-Assisted Mammography as a Next-Generation Diagnostic Strategy for Breast Cancer Screening.Diagnostics (Basel, Switzerland) · 2026Review
- Advancements in artificial intelligence for cancer diagnosis and prognosis prediction: current applications and emerging opportunities.Frontiers in cell and developmental biology · 2026Review
- Pharmacological Prevention in Breast Cancer: Current Evidence, Challenges, and Future Directions.Cancers · 2025Review
- Improving Benign and Malignant Classifications in Mammography with ROI-Stratified Deep Learning.Bioengineering (Basel, Switzerland) · 2025Article
- Hybrid convolutional neural network and bi-LSTM model with EfficientNet-B0 for high-accuracy breast cancer detection and classification.Scientific reports · 2025Article
- MRI-based artificial intelligence models for post-neoadjuvant surgery personalization in breast cancer: a narrative review of evidence from Western Pacific.The Lancet regional health. Western Pacific · 2025Review
- Deep learning in breast cancer risk prediction: a review of recent applications in full-field digital mammography.Frontiers in oncology · 2025Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose To develop an artificial intelligence (AI) deep learning tool capable of predicting future breast cancer risk from a current negative screening mammographic examination and to evaluate the model on data from the UK National Health Service Breast Screening Program. Materials and Methods The OPTIMAM Mammography Imaging Database contains screening data, including mammograms and information on interval cancers, for more than 300 000 female patients who attended screening at three different sites in the United Kingdom from 2012 onward. Cancer-free screening examinations from women aged 50-70 years were performed and classified as risk-positive or risk-negative based on the occurrence of cancer within 3 years of the original examination. Examinations with confirmed cancer and images containing implants were excluded. From the resulting 5264 risk-positive and 191 488 risk-negative examinations, training (
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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.