Improving Performance of Breast Cancer Risk Prediction by Incorporating Optical Density Image Feature Analysis
- 25 October 2017
- journal article
- Published by Elsevier BV in Academic Radiology
- Vol. 29, S199-S210
- https://doi.org/10.1016/j.acra.2017.08.007
Abstract
No abstract availableKeywords
Funding Information
- National Institutes of Health
- National Cancer Institute
This publication has 31 references indexed in Scilit:
- Assessment of a Four-View Mammographic Image Feature Based Fusion Model to Predict Near-Term Breast Cancer RiskAnnals of Biomedical Engineering, 2015
- Association between Computed Tissue Density Asymmetry in Bilateral Mammograms and Near-term Breast Cancer RiskThe Breast Journal, 2014
- Risk-Based Mammography Screening: An Effort to Maximize the Benefits and Minimize the HarmsAnnals of Internal Medicine, 2012
- Characterizing mammographic images by using generic texture featuresBreast Cancer Research, 2012
- A novel and automatic mammographic texture resemblance marker is an independent risk factor for breast cancerCancer Epidemiology, 2011
- Personalized estimates of breast cancer risk in clinical practice and public healthStatistics in Medicine, 2011
- Evaluation of the Tyrer-Cuzick (International Breast Cancer Intervention Study) Model for Breast Cancer Risk Prediction in Women With Atypical HyperplasiaJournal of Clinical Oncology, 2010
- More Mammography Muddle: Emotions, Politics, Science, Costs, and PolarizationRadiology, 2010
- Assessing Women at High Risk of Breast Cancer: A Review of Risk Assessment ModelsJNCI Journal of the National Cancer Institute, 2010
- Screening for Breast Cancer: An Update for the U.S. Preventive Services Task ForceAnnals of Internal Medicine, 2009