ReviewBreast cancer research : BCR2022
Artificial intelligence in mammographic phenotyping of breast cancer risk: a narrative review.
Review in Breast cancer research : BCR, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06629896 (Comparison Between Contrast-Enhanced Mammography and Magnetic Resonance Imaging in the Surveillance of High-Risk Women for Breast Cancer), which is not on this map. Cited by 45 papers, 5 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.
Comparison Between Contrast-Enhanced Mammography and Magnetic Resonance Imaging in the Surveillance of High-Risk Women for Breast Cancer: The Randomized Controlled Trial 'Con-trust. Funded by European Commission NextGenerationEU - Ministero Della Salute PNRR: M6/C2_CALL 2023 Full Proposal
Who cites it
45 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Challenges in Implementing Artificial Intelligence in Breast Cancer Screening Programs: Systematic Review and Framework for Safe Adoption.Journal of medical Internet research · 2025Pooled it
- Examining inclusivity: the use of AI and diverse populations in health and social care: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Breast cancer risk prediction using machine learning: a systematic review.Frontiers in oncology · 2024Pooled it
- Mammography diagnosis of breast cancer screening through machine learning: a systematic review and meta-analysis.Clinical and experimental medicine · 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
- From pixels to pathology: how artificial intelligence mammographic risk scores capture tumor biology through imaging.European radiology · 2026Article
- AI-Driven Breast Cancer Diagnosis: A Systematic Review of Imaging Modalities, Deep Learning, and Explainability.Cancers · 2026Review
- Factors Influencing Universal Coverage of AI-Assisted Cervical Cancer Screening: Qualitative Study Based on the Macro Model of Health System.Journal of medical Internet research · 2026Article
- Advancements in Developing an Automated Breast Density Detection Technique for Breast Cancer Risk Prediction: Synthesizing a Signal-dependent Noise Stochastic Process.bioRxiv : the preprint server for biology · 2026Article
- Article
- Head-to-head comparisons of breast density assessment models using deep learning on digital and synthetic mammograms.Journal of medical imaging (Bellingham, Wash.) · 2026Article
- Breast cancer risk assessment for screening: a hybrid artificial intelligence approach.European radiology · 2026Article
- Breast Cancer Burden in BRICS Countries Attributable to Smoking, High Alcohol Use, High Body Mass Index, and Low Physical Activity, 1990-2021: Findings from the Global Burden of Disease 2021 and Mendelian Randomization.International journal of women's health · 2026Article
- Assessing mammographic density change within individuals across screening rounds using deep learning-based software.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Predicting short- to long-term breast cancer risk from longitudinal mammographic screening history.NPJ breast cancer · 2025Article
- Evaluating mammographic density's contribution to improve a breast cancer risk model with questionnaire-based and polygenic factors.NPJ breast cancer · 2025Article
- A practical work around for breast density distribution discrepancies between mammographic images from different vendors.European radiology · 2025Article
- Deep Learning-Based Recurrence Prediction in HER2-Low Breast Cancer: Comparison of MRI-Alone, Clinicopathologic-Alone, and Combined Models.Diagnostics (Basel, Switzerland) · 2025Article
- Deep learning prediction of mammographic breast density using screening data.Scientific reports · 2025Article
- Classifying the molecular subtype of breast cancer using vision transformer and convolutional neural network features.Breast cancer research and treatment · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
backgroundImproved breast cancer risk assessment models are needed to enable personalized screening strategies that achieve better harm-to-benefit ratio based on earlier detection and better breast cancer outcomes than existing screening guidelines. Computational mammographic phenotypes have demonstrated a promising role in breast cancer risk prediction. With the recent exponential growth of computational efficiency, the artificial intelligence (AI) revolution, driven by the introduction of deep learning, has expanded the utility of imaging in predictive models. Consequently, AI-based imaging-derived data has led to some of the most promising tools for precision breast cancer screening. MAIN BODY: This review aims to synthesize the current state-of-the-art applications of AI in mammographic phenotyping of breast cancer risk. We discuss the fundamentals of AI and explore the computing advancements that have made AI-based image analysis essential in refining breast cancer risk assessment. Specifically, we discuss the use of data derived from digital mammography as well as digital breast tomosynthesis. Different aspects of breast cancer risk assessment are targeted including (a) robust and reproducible evaluations of breast density, a well-established breast cancer risk factor, (b) assessment of a woman's inherent breast cancer risk, and (c) identification of women who are likely to be diagnosed with breast cancers after a negative or routine screen due to masking or the rapid and aggressive growth of a tumor. Lastly, we discuss AI challenges unique to the computational analysis of mammographic imaging as well as future directions for this promising research field.
conclusionsWe provide a useful reference for AI researchers investigating image-based breast cancer risk assessment while indicating key priorities and challenges that, if properly addressed, could accelerate the implementation of AI-assisted risk stratification to future refine and individualize breast cancer screening strategies.
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.