ArticleJournal of clinical oncology : official journal of the American Society of Clinical Oncology2023
Long-Term Performance of an Image-Based Short-Term Risk Model for Breast Cancer.
Article in Journal of clinical oncology : official journal of the American Society of Clinical Oncology, 2023. 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 28 papers, 5 of them syntheses that pooled it.
What it found
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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.
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
28 citing papers in PubMed, 5 syntheses or guidelines pooled it, 37 citations in OpenAlex.
- Artificial intelligence for risk-stratified breast cancer screening: a systematic review of evidence, clinical integration, and ethical implications in risk assessment tools.La Radiologia medica · 2026Pooled 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
- Clinical guidelines for the management of mammographic density: a systematic review of breast screening guidelines worldwide.JNCI cancer spectrum · 2024Pooled it
- Breast cancer risk prediction using machine learning: a systematic review.Frontiers in oncology · 2024Pooled 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
- From pixels to pathology: how artificial intelligence mammographic risk scores capture tumor biology through imaging.European radiology · 2026Article
- Blood DNA methylation and breast cancer risk: a prospective nested case-control study.EBioMedicine · 2026Article
- Advancing AI for multi-omics and clinical data integration in basic and translational cancer research.Nature reviews. Cancer · 2026Review
- Breast cancer risk assessment for screening: a hybrid artificial intelligence approach.European radiology · 2026Article
- Mammogram-based AI risk assessment in patients with dense breasts undergoing supplemental molecular breast imaging.Quantitative imaging in medicine and surgery · 2026Article
- Role of health digital twins in oncology drug development - a primer.Frontiers in oncology · 2026Review
- Precision Oncology: Current Landscape, Emerging Trends, Challenges, and Future Perspectives.Cells · 2025Review
- Predicting short- to long-term breast cancer risk from longitudinal mammographic screening history.NPJ breast cancer · 2025Article
- Artificial Intelligence-Driven Personalization in Breast Cancer Screening: From Population Models to Individualized Protocols.Cancers · 2025Review
- Incorporating Continuous Mammographic Density Into the BOADICEA Breast Cancer Risk Prediction Model.JCO precision oncology · 2025Article
- Mammographic calcifications association with risk of advanced breast cancer.Breast cancer research and treatment · 2025Article
- Artificial Intelligence and Breast Cancer Management: From Data to the Clinic.Cancer innovation · 2025Review
- Development and Validation of Dynamic 5-Year Breast Cancer Risk Model Using Repeated Mammograms.JCO clinical cancer informatics · 2024Article
- The scienthetic method: from Aristotle to AI and the future of medicine.British journal of cancer · 2024Review
- Risk stratification in breast screening workshop.BMC proceedings · 2024Article
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 at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeImage-derived artificial intelligence-based short-term risk models for breast cancer have shown high discriminatory performance compared with traditional lifestyle/familial-based risk models. The long-term performance of image-derived risk models has not been investigated.
methodsWe performed a case-cohort study of 8,604 randomly selected women within a mammography screening cohort initiated in 2010 in Sweden for women age 40-74 years. Mammograms, age, lifestyle, and familial risk factors were collected at study entry. In all, 2,028 incident breast cancers were identified through register matching in May 2022 (206 incident breast cancers were found in the subcohort). The image-based model extracted mammographic features (density, microcalcifications, masses, and left-right breast asymmetries of these features) and age from study entry mammograms. The Tyrer-Cuzick v8 risk model incorporates self-reported lifestyle and familial risk factors and mammographic density to estimate risk. Absolute risks were estimated, and age-adjusted AUC model performances (aAUCs) were compared across the 10-year period.
resultsThe aAUCs of the image-based risk model ranged from 0.74 (95% CI, 0.70 to 0.78) to 0.65 (95% CI, 0.63 to 0.66) for breast cancers developed 1-10 years after study entry; the corresponding Tyrer-Cuzick aAUCs were 0.62 (95% CI, 0.56 to 0.67) to 0.60 (95% CI, 0.58 to 0.61). For symptomatic cancers, the aAUCs for the image-based model were ≥0.75 during the first 3 years. Women with high and low mammographic density showed similar aAUCs. Throughout the 10-year follow-up, 20% of all women with breast cancers were deemed high-risk at study entry by the image-based risk model compared with 7.1% using the lifestyle familial-based model (
conclusionThe image-based risk model outperformed the Tyrer-Cuzick v8 model for both short-term and long-term risk assessment and could be used to identify women who may benefit from supplemental screening and risk reduction strategies.
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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.