Artificial intelligence in mammographic phenotyping of breast cancer risk: a narrative review.
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 demon...
| Publicado en: | Breast Cancer Research Vol. 24; no. 1; pp. 1 - 13 |
|---|---|
| Autores principales: | , , , , |
| Formato: | diagnostic images review tables/charts Journal Article |
| Publicado: |
BioMed Central
2/20/2022
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=155341159&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 155341159 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14655411 8UYJ jtl: Breast Cancer Research issn: 14655411 maglogo: N pubinfo: dt: 2/20/2022 vid: 24 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 155341159 155341159 NLM35184757 155341159 10.1186/s13058-022-01509-z NLM35184757 155341159 ppf: 1 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Artificial intelligence in mammographic phenotyping of breast cancer risk: a narrative review. aug: au: Gastounioti, Aimilia Desai, Shyam Ahluwalia, Vinayak S. Conant, Emily F. Kontos, Despina affil: Department of Radiology, Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, 19104, Philadelphia, PA, USA sug: subj: Breast Neoplasms Risk Factors Risk Assessment Methods Cancer Screening Methods Mammography Methods Image Processing, Computer Assisted Artificial Intelligence Prediction Models Reproducibility of Results Deep Learning Breast Neoplasms Diagnosis ab: 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 cancer screening strategies. pubtype: Academic Journal doctype: diagnostic images review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|