About this Research Topic
Breast cancer continues to be the most commonly diagnosed cancer worldwide, accounting for nearly 15% of cases with diagnoses accountable to female patients alone, and it is expected that over 10% of women will go on to be diagnosed with cancer of the breast at one point in their lives. The five year survival rate of this cancer is relatively high compared to other cancers, in order to maintain this high survival rate there must be adequate screening and monitoring of these cancers to allow early diagnosis and appropriate monitoring throughout disease progression and to monitor efficacy of treatments implemented.
Current imaging practices in identifying and monitoring are varied, and each have their role and merits in imaging cancers of the breast. Mammography is the gold standard first line imaging technique in preliminary diagnosis, however usually requires further investigation. Ultrasonography is another technique commonly used, having a useful role in characterizing the tumor and informing early intervention choices based on findings. Magnetic Resonance Imaging is becoming more prevalent in diagnosis of many diseases and is also seeing a use in breast cancer imaging, providing detailed 3D, cross-sectional models which are highly useful in diagnosis and monitoring, however require expensive specialist technology and knowledge to acquire and interpret.
This research topic aims to identify new ways researchers are refining and optimizing these techniques in order to improve patient outcomes in those diagnosed with breast cancer, and to enable better monitoring and tracking throughout treatment.
We welcome Original Research, leading-edge Reviews and Clinical Trials related but not limited to the aspects below:
- Deep learning and machine learning algorithms in diagnosing breast cancer
- Imaging in staging of breast cancer and its impact on management
- Predictive and prognostic models based on imaging biomarkers in breast cancer
- Imaging biomarkers of tumor biology in breast cancer
- Imaging in breast cancer recurrence
Please note: manuscripts consisting solely of bioinformatics or computational analysis of public genomic or transcriptomic databases which are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) are out of scope for this section and will not be accepted as part of this Research Topic.
Current imaging practices in identifying and monitoring are varied, and each have their role and merits in imaging cancers of the breast. Mammography is the gold standard first line imaging technique in preliminary diagnosis, however usually requires further investigation. Ultrasonography is another technique commonly used, having a useful role in characterizing the tumor and informing early intervention choices based on findings. Magnetic Resonance Imaging is becoming more prevalent in diagnosis of many diseases and is also seeing a use in breast cancer imaging, providing detailed 3D, cross-sectional models which are highly useful in diagnosis and monitoring, however require expensive specialist technology and knowledge to acquire and interpret.
This research topic aims to identify new ways researchers are refining and optimizing these techniques in order to improve patient outcomes in those diagnosed with breast cancer, and to enable better monitoring and tracking throughout treatment.
We welcome Original Research, leading-edge Reviews and Clinical Trials related but not limited to the aspects below:
- Deep learning and machine learning algorithms in diagnosing breast cancer
- Imaging in staging of breast cancer and its impact on management
- Predictive and prognostic models based on imaging biomarkers in breast cancer
- Imaging biomarkers of tumor biology in breast cancer
- Imaging in breast cancer recurrence
Please note: manuscripts consisting solely of bioinformatics or computational analysis of public genomic or transcriptomic databases which are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) are out of scope for this section and will not be accepted as part of this Research Topic.
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
