ArticleAcademic radiology2018
Using Convolutional Neural Networks for Enhanced Capture of Breast Parenchymal Complexity Patterns Associated with Breast Cancer Risk.
Article in Academic radiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis 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
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Breast Cancer Detection and Diagnosis Using Mammographic Data: Systematic Review.Journal of medical Internet research · 2019Pooled it
- Keeping AI in medicine and radiology within the framework of scientific method: measuring to close the epistemic gap.Insights into imaging · 2025Article
- Deep Learning Approaches with Digital Mammography for Evaluating Breast Cancer Risk, a Narrative Review.Tomography (Ann Arbor, Mich.) · 2023Review
- Deep learning of longitudinal mammogram examinations for breast cancer risk prediction.Pattern recognition · 2022Article
- Artificial intelligence in mammographic phenotyping of breast cancer risk: a narrative review.Breast cancer research : BCR · 2022Review
- Clinical Artificial Intelligence Applications: Breast Imaging.Radiologic clinics of North America · 2021Review
- Automated position and size selection of round applicators for AccuBoost breast brachytherapy.Journal of contemporary brachytherapy · 2020Article
- Automated Breast Cancer Detection in Digital Mammograms of Various Densities via Deep Learning.Journal of personalized medicine · 2020Article
- CAD and AI for breast cancer-recent development and challenges.The British journal of radiology · 2020Review
- Gist processing in digital breast tomosynthesis.Journal of medical imaging (Bellingham, Wash.) · 2020Article
- Comparison of Breast MRI Tumor Classification Using Human-Engineered Radiomics, Transfer Learning From Deep Convolutional Neural Networks, and Fusion Methods.Proceedings of the IEEE. Institute of Electrical and Electronics Engineers · 2020Article
- Application of GIS Spatial Analysis and Scanning Statistics in the Gynecological Cancer Clustering Pattern and Risk Screening: A Case Study in Northern Jiangxi Province, China.Risk management and healthcare policy · 2020Article
- Artificial Intelligence for Mammography and Digital Breast Tomosynthesis: Current Concepts and Future Perspectives.Radiology · 2019Review
- Deep convolutional neural networks for mammography: advances, challenges and applications.BMC bioinformatics · 2019Article
- Application of convolutional neural networks to breast biopsies to delineate tissue correlates of mammographic breast density.NPJ breast cancer · 2019Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
RATIONALE AND
objectivesWe evaluate utilizing convolutional neural networks (CNNs) to optimally fuse parenchymal complexity measurements generated by texture analysis into discriminative meta-features relevant for breast cancer risk prediction. MATERIALS AND
methodsWith Institutional Review Board approval and Health Insurance Portability and Accountability Act compliance, we retrospectively analyzed "For Processing" contralateral digital mammograms (GE Healthcare 2000D/DS) from 106 women with unilateral invasive breast cancer and 318 age-matched controls. We coupled established texture features (histogram, co-occurrence, run-length, structural), extracted using a previously validated lattice-based strategy, with a multichannel CNN into a hybrid framework in which a multitude of texture feature maps are reduced to meta-features predicting the case or control status. We evaluated the framework in a randomized split-sample setting, using the area under the curve (AUC) of the receiver operating characteristic (ROC) to assess case-control discriminatory capacity. We also compared the framework to CNNs directly fed with mammographic images, as well as to conventional texture analysis, where texture feature maps are summarized via simple statistical measures that are then used as inputs to a logistic regression model.
resultsStrong case-control discriminatory capacity was demonstrated on the basis of the meta-features generated by the hybrid framework (AUC = 0.90), outperforming both CNNs applied directly to raw image data (AUC = 0.63, P <.05) and conventional texture analysis (AUC = 0.79, P <.05).
conclusionsOur results suggest that informative interactions between patterns exist in texture feature maps derived from mammographic images, which can be extracted and summarized via a multichannel CNN architecture toward leveraging the associations of textural measurements to breast cancer risk.
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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.