ArticleAnnals of biomedical engineering2015
Assessment of a Four-View Mammographic Image Feature Based Fusion Model to Predict Near-Term Breast Cancer Risk.
Article in Annals of biomedical engineering, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.
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Who cites it
18 citing papers in PubMed, 2 syntheses or guidelines pooled 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
- Development and performance of female breast cancer incidence risk prediction models: a systematic review and meta-analysis.Annals of medicine · 2025Pooled it
- Utilizing Pseudo Color Image to Improve the Performance of Deep Transfer Learning-Based Computer-Aided Diagnosis Schemes in Breast Mass Classification.Journal of imaging informatics in medicine · 2025Article
- Deep learning of longitudinal mammogram examinations for breast cancer risk prediction.Pattern recognition · 2022Article
- 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
- A Comparison of Computer-Aided Diagnosis Schemes Optimized Using Radiomics and Deep Transfer Learning Methods.Bioengineering (Basel, Switzerland) · 2022Article
- An effective fine grading method of BI-RADS classification in mammography.International journal of computer assisted radiology and surgery · 2022Article
- Applying artificial intelligence technology to assist with breast cancer diagnosis and prognosis prediction.Frontiers in oncology · 2022Review
- Comparison of two-dimensional synthesized mammograms versus original digital mammograms: a quantitative assessment.Medical & biological engineering & computing · 2021Article
- Development and Assessment of a New Global Mammographic Image Feature Analysis Scheme to Predict Likelihood of Malignant Cases.IEEE transactions on medical imaging · 2020Article
- Convolutional Neural Network Based Breast Cancer Risk Stratification Using a Mammographic Dataset.Academic radiology · 2019Article
- Developing global image feature analysis models to predict cancer risk and prognosis.Visual computing for industry, biomedicine, and art · 2019Article
- 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
- Beyond breast density: a review on the advancing role of parenchymal texture analysis in breast cancer risk assessment.Breast cancer research : BCR · 2016Review
- Applying a computer-aided scheme to detect a new radiographic image marker for prediction of chemotherapy outcome.BMC medical imaging · 2016Article
- Association Between Changes in Mammographic Image Features and Risk for Near-Term Breast Cancer Development.IEEE transactions on medical imaging · 2016Article
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5 authors.
Funding
Abstract
The purpose of this study was to develop and assess a new quantitative four-view mammographic image feature based fusion model to predict the near-term breast cancer risk of the individual women after a negative screening mammography examination of interest. The dataset included fully-anonymized mammograms acquired on 870 women with two sequential full-field digital mammography examinations. For each woman, the first "prior" examination in the series was interpreted as negative (not recalled) during the original image reading. In the second "current" examination, 430 women were diagnosed with pathology verified cancers and 440 remained negative ("cancer-free"). For each of four bilateral craniocaudal and mediolateral oblique view images of left and right breasts, we computed and analyzed eight groups of global mammographic texture and tissue density image features. A risk prediction model based on three artificial neural networks was developed to fuse image features computed from two bilateral views of four images. The risk model performance was tested using a ten-fold cross-validation method and a number of performance evaluation indices including the area under the receiver operating characteristic curve (AUC) and odds ratio (OR). The highest AUC = 0.725 ± 0.026 was obtained when the model was trained by gray-level run length statistics texture features computed on dense breast regions, which was significantly higher than the AUC values achieved using the model trained by only two bilateral one-view images (p < 0.02). The adjustable OR values monotonically increased from 1.0 to 11.8 as model-generated risk score increased. The regression analysis of OR values also showed a significant increase trend in slope (p < 0.01). As a result, this preliminary study demonstrated that a new four-view mammographic image feature based risk model could provide useful and supplementary image information to help predict the near-term breast cancer risk.
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