ArticleRadiology2021
Deep Learning Predicts Interval and Screening-detected Cancer from Screening Mammograms: A Case-Case-Control Study in 6369 Women.
Article in Radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 3 of them syntheses that pooled it.
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Who cites it
22 citing papers in PubMed, 3 syntheses or guidelines pooled it, 37 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
- A systematic review of prediction models for risk of breast cancer.BMC cancer · 2025Pooled it
- Artificial Intelligence-Driven Mammography-Based Future Breast Cancer Risk Prediction: A Systematic Review.Journal of the American College of Radiology : JACR · 2024Pooled it
- Breast cancer risk assessment for screening: a hybrid artificial intelligence approach.European radiology · 2026Article
- Development and internal validation of a task-conditioned multimodal radiology-pathology model for breast cancer staging and biomarker profiling: a retrospective cohort study.Frontiers in cell and developmental biology · 2026Article
- Predicting short- to long-term breast cancer risk from longitudinal mammographic screening history.NPJ breast cancer · 2025Article
- Improving Benign and Malignant Classifications in Mammography with ROI-Stratified Deep Learning.Bioengineering (Basel, Switzerland) · 2025Article
- Article
- Mammography-based artificial intelligence for breast cancer detection, diagnosis, and BI-RADS categorization using multi-view and multi-level convolutional neural networks.Insights into imaging · 2025Article
- Frozen Large-Scale Pretrained Vision-Language Models are the Effective Foundational Backbone for Multimodal Breast Cancer Prediction.IEEE journal of biomedical and health informatics · 2025Article
- The Future of Breast Cancer Organized Screening Program Through Artificial Intelligence: A Scoping Review.Healthcare (Basel, Switzerland) · 2025Review
- Deep learning in breast cancer risk prediction: a review of recent applications in full-field digital mammography.Frontiers in oncology · 2025Review
- Deep learning model combined with computed tomography features to preoperatively predicting the risk stratification of gastrointestinal stromal tumors.World journal of gastrointestinal oncology · 2024Article
- Deep learning applications in breast cancer histopathological imaging: diagnosis, treatment, and prognosis.Breast cancer research : BCR · 2024Review
- Enhancing Accuracy in Breast Density Assessment Using Deep Learning: A Multicentric, Multi-Reader Study.Diagnostics (Basel, Switzerland) · 2024Article
- Rethinking Risk Modeling with Machine Learning.Annals of surgical oncology · 2023Article
- Breast Ultrasound Images Augmentation and Segmentation Using GAN with Identity Block and Modified U-Net 3.Sensors (Basel, Switzerland) · 2023Article
- Deep Learning Approaches with Digital Mammography for Evaluating Breast Cancer Risk, a Narrative Review.Tomography (Ann Arbor, Mich.) · 2023Review
- Differentiation between Phyllodes Tumors and Fibroadenomas through Breast Ultrasound: Deep-Learning Model Outperforms Ultrasound Physicians.Sensors (Basel, Switzerland) · 2023Article
- Quality control system for mammographic breast positioning using deep learning.Scientific reports · 2023Article
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Authors and funding
10 authors at 1 institution in 2 countries.
Funding
Abstract
Background The ability of deep learning (DL) models to classify women as at risk for either screening mammography-detected or interval cancer (not detected at mammography) has not yet been explored in the literature. Purpose To examine the ability of DL models to estimate the risk of interval and screening-detected breast cancers with and without clinical risk factors. Materials and Methods This study was performed on 25 096 digital screening mammograms obtained from January 2006 to December 2013. The mammograms were obtained in 6369 women without breast cancer, 1609 of whom developed screening-detected breast cancer and 351 of whom developed interval invasive breast cancer. A DL model was trained on the negative mammograms to classify women into those who did not develop cancer and those who developed screening-detected cancer or interval invasive cancer. Model effectiveness was evaluated as a matched concordance statistic (C statistic) in a held-out 26% (1669 of 6369) test set of the mammograms. Results The C statistics and odds ratios for comparing patients with screening-detected cancer versus matched controls were 0.66 (95% CI: 0.63, 0.69) and 1.25 (95% CI: 1.17, 1.33), respectively, for the DL model, 0.62 (95% CI: 0.59, 0.65) and 2.14 (95% CI: 1.32, 3.45) for the clinical risk factors with the Breast Imaging Reporting and Data System (BI-RADS) density model, and 0.66 (95% CI: 0.63, 0.69) and 1.21 (95% CI: 1.13, 1.30) for the combined DL and clinical risk factors model. For comparing patients with interval cancer versus controls, the C statistics and odds ratios were 0.64 (95% CI: 0.58, 0.71) and 1.26 (95% CI: 1.10, 1.45), respectively, for the DL model, 0.71 (95% CI: 0.65, 0.77) and 7.25 (95% CI: 2.94, 17.9) for the risk factors with BI-RADS density (b rated vs non-b rated) model, and 0.72 (95% CI: 0.66, 0.78) and 1.10 (95% CI: 0.94, 1.29) for the combined DL and clinical risk factors model. The
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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.