clinical-breast-cancer.com 的前十大競爭對手

tbcr.amegroups.org

Matched-pair long-term survival analysis of male and female patients with breast cancer: a population-based study

breast-cancer-research.biomedcentral.com

Background Improved breast cancer risk assessment models are needed to enable personalized screening strategies that achieve better harm-to-benefit ratio based on earlier detection and better breast cancer outcomes than existing screening guidelines. Computational mammographic phenotypes have demonstrated a promising role in breast cancer risk prediction. With the recent exponential growth of computational efficiency, the artificial intelligence (AI) revolution, driven by the introduction of deep learning, has expanded the utility of imaging in predictive models. Consequently, AI-based imaging-derived data has led to some of the most promising tools for precision breast cancer screening. Main body This review aims to synthesize the current state-of-the-art applications of AI in mammographic phenotyping of breast cancer risk. We discuss the fundamentals of AI and explore the computing advancements that have made AI-based image analysis essential in refining breast cancer risk assessment. Specifically, we discuss the use of data derived from digital mammography as well as digital breast tomosynthesis. Different aspects of breast cancer risk assessment are targeted including (a) robust and reproducible evaluations of breast density, a well-established breast cancer risk factor, (b) assessment of a woman’s inherent breast cancer risk, and (c) identification of women who are likely to be diagnosed with breast cancers after a negative or routine screen due to masking or the rapid and aggressive growth of a tumor. Lastly, we discuss AI challenges unique to the computational analysis of mammographic imaging as well as future directions for this promising research field. Conclusions We provide a useful reference for AI researchers investigating image-based breast cancer risk assessment while indicating key priorities and challenges that, if properly addressed, could accelerate the implementation of AI-assisted risk stratification to future refine and individualize breast can

phesgo.com

See definitions for frequently used HER2-positive breast cancer terms. See Full Safety, including most serious side effects, for more information.

komen.org

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ejbc.kr

Journal of Breast Cancer

wjgnet.com

Baishideng Publishing Group (BPG) publishes 47 peer-reviewed, open-access journals covering a broad range of topics in clinical medicine, as well as several topics in biochemistry and molecular biology, relevant to human health today.