ArticleAcademic radiology2013
Prediction of near-term breast cancer risk based on bilateral mammographic feature asymmetry.
Article in Academic radiology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 4 of them syntheses 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
26 citing papers in PubMed, 4 syntheses or guidelines pooled it, 62 citations in OpenAlex.
- Development and performance of female breast cancer incidence risk prediction models: a systematic review and meta-analysis.Annals of medicine · 2025Pooled it
- A systematic review of prediction models for risk of breast cancer.BMC cancer · 2025Pooled it
- Breast cancer risk prediction using machine learning: a systematic review.Frontiers in oncology · 2024Pooled it
- An Assessment of the Predictive Performance of Current Machine Learning-Based Breast Cancer Risk Prediction Models: Systematic Review.JMIR public health and surveillance · 2022Pooled it
- The top 100 most-cited articles on artificial intelligence in breast radiology: a bibliometric analysis.Insights into imaging · 2024Article
- Machine learning and new insights for breast cancer diagnosis.The Journal of international medical research · 2024Review
- A multi-stage fusion framework to classify breast lesions using deep learning and radiomics features computed from four-view mammograms.Medical physics · 2023Article
- The Systematic Review of Artificial Intelligence Applications in Breast Cancer Diagnosis.Diagnostics (Basel, Switzerland) · 2022Review
- AI in Breast Cancer Imaging: A Survey of Different Applications.Journal of imaging · 2022Review
- Prediction of Short-Term Breast Cancer Risk with Fusion of CC- and MLO-Based Risk Models in Four-View Mammograms.Journal of digital imaging · 2022Article
- Applying artificial intelligence technology to assist with breast cancer diagnosis and prognosis prediction.Frontiers in oncology · 2022Review
- Breast Cancer Risk Assessment: A Review on Mammography-Based Approaches.Journal of imaging · 2021Review
- Comparison of two-dimensional synthesized mammograms versus original digital mammograms: a quantitative assessment.Medical & biological engineering & computing · 2021Article
- Left-right breast asymmetry and risk of screen-detected and interval cancers in a large population-based screening population.The British journal of radiology · 2020Article
- Ethnic and age differences in right-left breast asymmetry in a large population-based screening population.The British journal of radiology · 2020Article
- Assessment of global and local region-based bilateral mammographic feature asymmetry to predict short-term breast cancer risk.Physics in medicine and biology · 2018Article
- Applying a new bilateral mammographic density segmentation method to improve accuracy of breast cancer risk prediction.International journal of computer assisted radiology and surgery · 2017Article
- Developing a new case based computer-aided detection scheme and an adaptive cueing method to improve performance in detecting mammographic lesions.Physics in medicine and biology · 2017Article
- Left-right analysis of mammary gland development in retinoid X receptor-α+/- mice.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2016Article
- Novel mammographic image features differentiate between interval and screen-detected breast cancer: a case-case study.Breast cancer research : BCR · 2016Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
RATIONALE AND
objectivesThe objective of this study is to investigate the feasibility of predicting near-term risk of breast cancer development in women after a negative mammography screening examination. It is based on a statistical learning model that combines computerized image features related to bilateral mammographic tissue asymmetry and other clinical factors. MATERIALS AND
methodsA database of negative digital mammograms acquired from 994 women was retrospectively collected. In the next sequential screening examination (12 to 36 months later), 283 women were diagnosed positive for cancer, 349 were recalled for additional diagnostic workups and later proved to be benign, and 362 remain negative (not recalled). From an initial pool of 183 features, we applied a Sequential Forward Floating Selection feature selection method to search for effective features. Using 10 selected features, we developed and trained a support vector machine classification model to compute a cancer risk or probability score for each case. The area under the receiver operating characteristic curve and odds ratios (ORs) were used as the two performance assessment indices.
resultsThe area under the receiver operating characteristic curve = 0.725 ± 0.018 was obtained for positive and negative/benign case classification. The ORs showed an increasing risk trend with increasing model-generated risk scores (from 1.00 to 12.34, between positive and negative/benign case groups). Regression analysis of ORs also indicated a significant increase trend in slope (P = .006).
conclusionsThis study demonstrates that the risk scores computed by a new support vector machine model involving bilateral mammographic feature asymmetry have potential to assist the prediction of near-term risk of women for developing breast cancer.
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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.