Advances in Breast Imaging and Analytics
A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".
Deadline for manuscript submissions: 31 August 2024 | Viewed by 19513
Special Issue Editor
Interests: optimization of radiological diagnosis through technology and education; breast cancer prediction; early detection; and prognosis; breast density; medical image perception; radiological image evaluation; dose optimization; software technologies for cancer prediction; prognosis; breast composition analyses
Special Issue Information
Dear Colleagues,
The last decade has recorded significant improvements in breast cancer survival rates and reductions in deaths from the disease. These achievements have been possible due to improvements in risk prediction; early detection; and treatment. Therefore; optimising risk prediction and early detection are crucial to further reducing breast cancer deaths. Breast imaging tools such as mammography; ultrasound; digital breast tomosynthesis; computed tomography; magnetic resonance imaging; and molecular breast imaging are constantly evolving to optimise breast cancer detection and assessment. Despite these technological advances; about 30% of breast cancer cases are missed; suggesting that strategies to improve the interpretation of breast images are needed. Another issue is that breast cancer post-treatment events such as recurrence and secondary cancer are major causal factors for breast-cancer-related deaths. Therefore; the accurate prediction of treatment outcomes is crucial to develop informed options for tailoring follow-up strategies and reducing breast cancer deaths. Current outcome prediction tools; which are based on clinicopathologic data; show moderate predictive powers at best. Recent evidence indicates that medical images contain covert information that can be modelled to improve the risk prediction; detection; and prognosis of breast cancer. Interestingly; novel technologies such as artificial intelligence and machine learning provide opportunities to extract and model image-based information and genomic and clinicopathologic data as well as data from medical health records to transform the prediction; detection; and prognosis of breast cancer.
The purpose of this Special Issue is to investigate how breast imaging hardware technologies and image interpreters (radiologists) can be further optimised to facilitate early detection; and how intelligent software technologies can be used to extract information from breast images and combine with genomic and clinicopathologic data as well as medical health records to improve the prediction; detection; and prognosis of breast cancer.
Dr. Ernest Usang Ekpo
Guest Editor
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- breast cancer
- breast cancer early detection
- breast cancer assessment
- breast cancer risk prediction
- breast cancer prognosis
- breast density
- breast radiomics
- breast imaging technologies
- digital mammography
- digital breast tomosynthesis
- ultrasound
- breast computed tomography
- breast magnetic resonance imaging
- molecular breast imaging
- artificial intelligence
- machine learning
- technology and observer performance
Benefits of Publishing in a Special Issue
- Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
- Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
- Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
- External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
- e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.
Further information on MDPI's Special Issue polices can be found here.