Using AI and Imaging Biomarkers for Insights into the Tumor Microenvironment of Breast Cancer
A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Tumor Microenvironment".
Deadline for manuscript submissions: closed (30 April 2021) | Viewed by 10742
Special Issue Editor
Interests: Breast cancer; Imaging; MRI; Radiomics/ Radiogenomics; AI
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
BC is the most common cancer in women and, despite advances in early disease detection and treatment, remains the second-leading cause of female cancer deaths, posing a major societal, medical, and health-economic burden. Molecular profiling has proven that BC is a disease with a remarkable heterogeneity and that various cancer cell populations co-exist in a given primary tumor that differ significantly in their genetic, phenotypic, and behavioral characteristics. The current lack of understanding of breast cancer heterogeneity contributes to treatment failures and patients’ deaths. BC heterogeneity is not solely driven by the combined effect of genomic instability within the tumor, but also by differential selective pressures from the tumor microenvironment (TME). Treatment management decisions are currently based on tumor information from invasive tissue sampling, but current invasive tools cannot provide a comprehensive assessment of BC heterogeneity of tumor in its entirety. One of the most promising areas of health innovation is the application of AI in biomedical imaging. Medical imaging has always been an integral part in breast cancer care, ranging from diagnosis and staging to therapy monitoring and post-therapeutic follow-up. With the possibility to enhance medical imaging with AI, there is a unique opportunity to develop imaging biomarkers that significantly broaden the understanding of BC heterogeneity and the TME challenge, with the ultimate goal to revolutionize risk stratification and treatment of BC.
This Special Issue of Cancers with a focus on “Using AI and Imaging Biomarkers for Insights into the Tumor Microenvironment of Breast Cancer” invites submission of the recent advances, current possibilities, and emerging techniques in imaging of the TME of breast cancers and enhancements with AI.
Prof. Dr. Katja Pinker-Domenig
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. Cancers 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 2900 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
- Tumor microenvironment
- Radiomics/-genomics
- Artificial intelligence
- Imaging biomarker
- Mammography
- Ultrasound
- Contrast-enhanced mammography
- Digital breast tomosynthesis
- MRI
- PET/MRI